This PDF file contains the front matter associated with SPIE Proceedings Volume 9239, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The SMAPVEX12 (Soil Moisture Active/Passive Validation Experiment) was carried out over the summer of 2012 in Manitoba, Canada. The goal of the project was to improve the accuracy of satellite based remote sensing of soil moisture. Data were gathered during a 42-day field campaign with surface measurements on 55 different agricultural fields in south-western Manitoba. The extended duration of the campaign, contrast in soil textures, and variety of crop types over the study region provided an excellent range of soil moisture and vegetation conditions. The study fields ranged from bare to fully vegetated, with volumetric soil moisture levels spanning almost 50%. Remotely sensed data were collected on 17 days by aircraft at 1.4 Ghz with a microwave radiometer at two different resolutions. Observed brightness temperatures from the radiometer showed a typical inverse relationship to the near simultaneous soil moisture measurements from the field. This study will focus on improving existing models for passive microwave retrieval of soil moisture using a more extensive data set of field-measured soil temperature, soil moisture and vegetation biomass from a wider range of crops than has been available in previous studies. The extensive ground data collected will allow for both a validation of the high-resolution passive soil moisture estimate, as well as an analysis on the effect of scaling to a lower resolution passive measurement.

This work presents a real time method for rainfall estimation based on attenuation data acquired via Ka-band satellite link and discusses some results of its application. Data to be processed are recorded with a commercial kit for satellite web supplied by a European provider and operating above the urban area of Florence (Italy). Since the system automatically performs a continuous adjustment of the transmitted power in function of the intensity of the received signal, this information is being exploited to estimate the entity of the precipitation within the area. The adopted model for the attenuation of a microwave link due to hydrometeors is the one suggested by Olsen and Hodge and recommended by the ITU. The results are interpreted together with registered rain-rate measurements provided by three rain gauges dislocated within the area.

Satellite altimeters on-board Envisat and SARAL (Altika) are routinely used to create virtual monitoring stations from the satellite path crossing with any river of significant width. These virtual stations have the advantage of having a low operational cost and providing near real-time absolute measurement of water level. However, many shortcomings still remain open questions and the precision of measurements can vary widely depending on a number of factors such as the river width and environmental conditions surrounding the water course. In this article we have concentrated our efforts on the relation between land cover classes, the shape of waveforms produced by the backscatter response and the separability among different land cover classes and water. Seven land cover classes often encountered nearby large river banks were analyzed: agriculture, native forest, planted forest, savanna, pasture, urban and open water. Waveforms of these classes were sampled to build a waveform library. They were compared among themselves using cross-correlation, cumulative difference and Kolmogorov-Smirnov distance. Average waveforms for each class were calculated and compared. The results show that only the open water" and forest" classes could be characterized as having a typical behavior, probably caused by the limitations of the measurements used. Furthermore, these two classes have very similar responses and could easily be confused. The other classes generally showed chaotic behavior which can mostly be attributed to variations in their cover characteristics. We expect that a better understanding of the influence of land cover on waveform shapes will increase accuracy of water level measurements.

The detection of buried objects with remote sensing techniques mainly relies on thermal infrared, ground penetrating radar, and metal detectors. However, nowadays people also start to use low frequency passive microwave radiometry for the same purpose. The detection performance of passive microwave radiometry is influenced by the depth and size of the object, environmental factors, and soil properties. Soil moisture is a key variable here, due to its strong influence on the observed dielectric constant. Through digging activities will the hydrological conditions of the soil change significantly that can be detected by remotely sensing systems. A study was designed to examine the influence of the hydrological changes caused by the direct placement of an object in the ground. Simulations in a soil moisture model and field observations revealed the development of a wetter part above and a drier part underneath an object. The observations were converted to brightness temperatures with a coherent model in combination with a dielectric mixing model. Development of a drier area underneath an object generally increases the brightness temperature after a precipitation event. As a results are brightness temperature anomalies of low dielectric constant objects raised during the first 36 hours after a rain event. Ground observations of soil moisture and porosity revealed an increase in porosity and loss in soil moisture for the part that was excavated. Knowledge of past weather conditions could therefore improve buried object detection by passive microwave sensors.

The main hydrologic feedback from the land-surface to the atmosphere is the evapotranspiration, ET, which embraces the response of both the soil and vegetated surface to the atmospheric forcing (e.g., precipitation and temperature), as well as influences locally atmospheric humidity, cloud formation and precipitation, the main driver for drought. Actual ET is regulated by several factors, including biological quantities (e.g., rooting depth, leaf area, fraction of absorbed photosynthetically active radiation) and soil water status. The ET temporal dynamic is strongly affected by rainfall deficits, and in turn it represents a robust proxy of the effects of water shortage on plants. These characteristics make ET a promising quantity for monitoring environmental drought, defined as a shortage of water availability that reduces the ecosystem productivity. In the last few decades, the capability to accurately model ET over large areas in a spatial-distributed fashion has increased notably. Most of the improvements in this field are related to the increasing availability of remote sensing data, and the achievements in modelling of ET-related quantities. Several land-surface models exploit the richness of newly available datasets, including the Community Land Model (CLM) and the Meteosat Second Generation (MSG) ET outputs. Here, the potentiality of ET maps obtained by combining land-surface models and remote sensing data through these two schemes is explored, with a special focus on the reliability of ET (and derived standardized variables) as drought indicator. Tests were performed over Europe at moderate spatial resolution (3-5 km), with the final goal to improve the estimation of soil water status as a contribution to the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu).

Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, , the accuracy of the model may be questioned due to spatial and temporal variability of cloud coverage. During the irrigation season, cloud coverage exhibits a quite regular daily behavior, which, when rendered in probabilistic terms, allows for an a-priory evaluation of the most likely suitable pair of images to estimate thermal inertia, given the results of the satellite passes. In turn, the water content of soil is estimated through thermal inertia by coupling diurnal optical and nighttime thermal images, e.g. as acquired by MODIS sensor on board polar orbiting satellites AQUA and TERRA, which have spatial resolution high enough to cope with typical agricultural applications. The method relies on the availability of the shortwave albedo and, at least, two daily thermographs preferably acquired in specific epochs of the day: the first at sunset when latent and heat fluxes are negligible; the second just before sunrise, when surface soil temperature reaches its minimum. Unfortunately, high resolution thermal images are often not available in those specific epochs, so that the accuracy of estimate accuracy decays even severely. In this perspective the paper, following previous contributions by some of the authors of the present paper ^[1-4], proposes exploiting SEVIRI data, characterized by higher acquisition rate but coarser spatial resolution as available from geostationary platform, to supplement MODIS data in a twofold way: i) by allowing to verify, by means of cloud detection algorithms, the hypothesis of clear sky throughout the time; ii) by synthesizing a high spatial/high temporal resolution sequence of images, through fusion of MODIS and SEVIRI data via Bayesian smoothing. A first validation of the latter method is achieved by comparing the results with in situ micro-meteorological measurements.

Water temperature is an important parameter of water quality and influences other physical and chemical parameters. It also directly influences the survival and growth of animal and plant species in river ecosystems. In situ measurements do not allow for a total spatial coverage of water bodies and rivers that is necessary for monitoring and research at the Federal Institute of Hydrology (BfG), Germany. Hence, the ability of different remote sensing products to identify and investigate water inflows and water temperatures in Federal waterways is evaluated within the research project 'Remote sensing of water surface temperature'. The research area for a case study is the Upper and Middle Rhine River from the barrage in Iffezheim to Koblenz. Satellite products (e. g. Landsat and ASTER imagery) can only be used for rivers at least twice as wide as the spatial resolution of the satellite images. They can help to identify different water bodies only at tributaries with larger inflow volume (Main and Mosel) or larger temperature differences between the inflow (e. g. from power plants working with high capacity) and the river water. To identify and investigate also smaller water inflows and temperature differences, thermal data with better ground and thermal resolution is required. An aerial survey of the research area was conducted in late October 2013. Data of the surface was acquired with two camera systems, a digital camera with R, G, B, and Near-IR channels, and a thermal imaging camera measuring the brightness temperature in the 8-12 m wavelength region (TIR). The resolution of the TIR camera allowed for a ground resolution of 4 m, covering the whole width of the main stream and larger branches. The RGB and NIR data allowed to eliminate land surface temperatures from the analysis and to identify clouds and shadows present during the data acquisition. By degrading the spatial resolution and adding sensor noise, artificial Landsat ETM+ and TIRS datasets were created to evaluate whether the methods applied to the aerial survey data are also applicable for satellite datasets. In situ measurements were obtained from water quality measurement stations and specifically deployed temperature loggers. Two alternative methods to correct for atmospheric influences were evaluated: calibration based on in situ water temperature measurements and atmospheric correction based on atmospheric parameters modelled with MODTRAN R5. Both methods rely on input data, the former on in situ measurements of the water temperature, the latter on data from climate stations. The results are validated by the dataset of independent in situ measurements. The remaining difference of the corrected aerial survey to the in situ measurements could be reduced to 0.0±0.2 C for the calibration and 0.1±0.3 C for the atmospheric correction. The variance of the atmospheric correction proved to be larger than of the in situ calibration method, but still smaller than the variance of atmospherically corrected, real LANDSAT ETM+ data. Inflows with differing water temperatures could be identified successfully with the change point analysis method even for smaller dischargers and the mixing processes of water bodies with different temperatures could be traced into great detail. With decreasing spatial resolution and increasing sensor noise, the ability to detect inflows remained the same, but at the cost of a higher number of 'false positive' change points.

Woody vegetation encroachment into grasslands or bush thickening, a global phenomenon, is transforming the Southern African grassland systems into savanna-like landscapes. Estimation of woody vegetation is important to rangeland scientists and land managers for assessing its impact on grass production and calculating its grazing and browsing capacity. Assessment of grazing and browsing components is often challenging because agro-ecological landscapes of this region are largely characterized by small scale and heterogeneous land-use-land-cover patterns. In this study, we investigated the utility of high spatial resolution remotely sensing data for modelling grazing and browsing capacity at landscape level. Woody tree density or Tree Equivalents (TE) and Total Leaf Mass (LMASS) data were derived using the Biomass Estimation for Canopy Volume (BECVOL) program. The Random Forest (RF) regression algorithm was assessed to establish relationships between these variables and vegetation indices (Simple Ratio and Normalized Difference Vegetation Index), calculated using the red and near infrared bands of SPOT5. The RF analysis predicted LMASS with R² = 0.63 and a Root Mean Square Error (RMSE) of 1256 kg/ha compared to a mean of 2291kg/ha. TE was predicted with R² = 0.55 and a RMSE = 1614 TE/ha compared to a mean of 3746 TE/ha. Next, spatial distribution maps of LMASS/ha and TE/ha were derived using separate RF regression models. The resultant maps were then used as input data into conventional grazing and browsing capacity models to calculate grazing and browsing capacity maps for the study area. This study provides a sound platform for integrating currently available and future remote sensing satellite data into rangeland carrying capacity modelling and monitoring.

The European Space Agency (ESA) has embarked on the development of the Sentinel constellation. Sentinel-2 is intended to improve vegetation assessment at local to global scale. Rangeland quality assessment is crucial for planning and management of grazing areas. Well managed and improved grazing areas lead to higher livestock production, which is a pillar of the rural economy and livelihoods, especially in many parts of the African continent. Leaf nitrogen (N) is an indicator of rangeland quality, and is crucial for understanding ecosystem function and services. Today, estimation of leaf N is possible using field and imaging spectroscopy. However, a few studies based on commercially available multispectral imageries such as WorldView-2 and RapidEye have shown the potential of a red-edge band for accurately predicting and mapping leaf N at the broad landscape scale. Sentinel-2 has two red edge bands. The objective of this study was to investigate the utility of the spectral configuration of Sentinel-2 for estimating leaf N concentration in rangelands and savannas of Southern Africa. Grass canopy reflectance was measured using the FieldSpec 3, Analytical Spectral Device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectances were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random Forest (RF) technique was used to predict leaf N using all thirteen bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with the root mean square error (RMSE) of 0.04 (6% of the mean). Interestingly, spectral bands centred at 705 nm (red edge) and two shortwave infrared centred at 2190 and 1610 nm were found to be the most important bands in predicting leaf N. These findings concur with previous studies based on spectroscopy, airborne hyperspectral or multispectral imagery, e.g. RapidEye, on the importance of shortwave infrared and red-edge reflectance in the estimation of leaf N. In that sense, the ESA’s Sentinel-2 sampling in both spectral regions has a unique spectral configuration, and a high potential to estimate leaf N which is crucial for informing decision makers on rangeland condition monitoring.

Australian Plague Locust, Chortoicetes terminifera (Walker), can rapidly increase in population size in the remote interior of eastern Australia under favorable habitat conditions and cause severe agricultural damage. To minimize losses, earlydetection of locust outbreaks is essential to the implementation of preventive control. Quantitative measurement of locust habitat suitability is critical for improving the efficiency of ground and aerial surveys, and providing vital information for locust population forecasting. Here, routine locust survey by the Australian Plague Locust Commission during 2003 and 2011 is investigated in relation to the habitat greenness derived from the fortnightly 250 m composites of Normalized Difference Vegetation Index (NDVI), and the rainfall amount from the weekly 5 km grids of modelled precipitation, using the spatial analysis and statistics of ESRI ArcGIS. The sighting dates of high-density locust nymphs (band and sub-band) were assigned into 5 groups corresponding to the nymphal development stages, and the fortnightly NDVI values and weekly rainfall totals for the locust locations were extracted for the previous 13 weeks. The averaged NDVI values for locust habitats showed a slight increase of 0.04-0.13 from initially 0.23-0.29 within 4-7 weeks before 2nd-5th instar bands and sub-bands were sighted. The median values of NDVI increase were on an equivalence scale of 0.05-0.15 from the background of 0.21-0.26; the increments were equal to 12-37% in the historical range from 13-22% and equal to 38-59% from the 11-18% of seasonal maxima, which indicated by normalized NDVI anomalies that the majority of high-density nymphs had all experienced a period of better than average conditions in both historical and seasonal perspectives. However, 5th-instar bands and sub-bands were consistently found in slightly dried habitats, while 1st-instar bands were mostly seen in much green areas but on the trend of dry-off. The time-series of habitat greenness for 1st-instar bands illustrated a very different pattern from the others, which could have resulted from the limited dataset mainly from the winter rain zone. Significant single rainfall of 25-30 mm was required to trigger the locust breeding sequence, and in excess of 40-50 mm total rainfall for locusts to survive the entire nymphal period. These findings will improve the understanding of locust plague mechanisms related to habitat condition, potentially provide practical means to monitor locust habitat conditions remotely and improve the underlying basis for locust survey and population management in Australia.

The availability of newly generated data from Advanced Very High Resolution Radiometer (AVHRR) covering the last three decades has broaden our understanding of vegetation dynamics (greening) from global to regional scale through quantitative analysis of seasonal trends in vegetation time series and climatic variability especially in the Guinea savannah region of Nigeria where greening trend is inconsistent. Due to the impact of changes in global climate and sustainability of means of human livelihood, increasing interest on vegetation productivity has become important. The aim of this study is to examine association between NDVI and rainfall using remotely sensed data, since vegetation dynamics (greening) has a high degree of association with weather parameters. This study therefore analyses trends in regional vegetation dynamics in Kogi state, Nigeria using bi-monthly AVHRR GIMMS 3g (Global Inventory Modelling and Mapping Studies) data and TAMSAT (Tropical Applications of Meteorology Satellite) monthly data both from 1983 to 2011 to identify changes in vegetation greenness over time. Analysis of changes in the seasonal variation of vegetation greenness and climatic drivers was conducted for selected locations to further understand the causes of observed interannual changes in vegetation dynamics. For this study, Mann-Kendall (MK) monotonic method was used to analyse long-term inter-annual trends of NDVI and climatic variable. The Theil-Sen median slope was used to calculate the rate of change in slopes between all pair wise combination and then assessing the median over time. Trends were also analysed using a linear model method, after seasonality had been removed from the original NDVI and rainfall data. The result of the linear model are statistically significant (p <0.01) in all the study location which can be interpreted as increase in vegetation trend over time (greening). Also the result of the NDVI trend analysis using Mann-Kendall test shows an increasing (i.e. positive) trend in the time series. The significance of the result was tested using Kendall's tau rank correlation coefficient and the results were significant. Finally the NDVI data and TAMSAT data were analysed together in order to describe the relationship between both values. Although, increase in rainfall over the last decades enhances vegetation greenness, other factors such as land use change and population density need to be investigated in order to better explain changing trends of vegetation greening for the study area in the future.

Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying the Neural Network method with IOs techniques on WorldView-2 imagery.

Small unmanned aerial vehicle (UAV) and a prototype hyperspectral imaging camera (HSI) was used to measure the hemispherical directional reflectance factor (HDRF) of a test field with known light scattering properties. The HSI acquires a burst of 24 images within two seconds and all of these images are acquired with different spectral content. By using the autopilot of the UAV, the flight can be preplanned so that the target area is optimally covered with overlapping images from multiple view angles. Structure from motion (SFM) algorithm is used to accurately determine the view angles for each image. The HDRF is calculated for each ground pixel by determining view directions from all of the images for that particular pixel. The pixel intensity values are then processed to reflectance by using a reference panel, which has been measured in laboratory with Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). The UAV flight was performed over a test field with different gravel targets. The targets have known HDRF and this allows us to validate the UAV results. Another test was performed over a crop field to display the potential of this method for crop monitoring.

Hydrological indexes calculation requires the existence of spatial data such as the drainage network, the hydrological basin and the contours. For the Greek territory this data can be extracted from the topographic maps of the Hellenic Military Geographical Service (HMGS) or from remote sensing data. In this study the suitability and the accuracy of spatial information derived from remote sensing data are controlled with reference to the respective information from the topographic maps of 1/50.000. DSM from ALOS, ASTER, SRTM and airphoto stereopairs has been used for the automatic extraction of the drainage network, the hydrological basin and the calculation of hydrological indexes such as drainage length and Horton's Laws. The Selinountas river basin in western Peloponnese was selected for the evaluation and the results are presented in this paper.

Climate change challenges our understanding of risk by modifying hazards and their interactions. Sudden increases in population and rapid urbanization are changing exposure to risk around the globe, making impacts harder to predict. Despite the availability of operational mapping products, there is no single tool to integrate diverse data and products across hazards, update exposure data quickly and make scenario-based predictions to support both short and long-term risk-related decisions. RASOR (Rapid Analysis and Spatialization Of Risk) will develop a platform to perform multi-hazard risk analysis for the full cycle of disaster management, including targeted support to critical infrastructure monitoring and climate change impact assessment. A scenario-driven query system simulates future scenarios based on existing or assumed conditions and compares them with historical scenarios. RASOR will thus offer a single work environment that generates new risk information across hazards, across data types (satellite EO, in-situ), across user communities (global, local, climate, civil protection, insurance, etc.) and across the world. Five case study areas are considered within the project, located in Haiti, Indonesia, Netherlands, Italy and Greece. Initially available over those demonstration areas, RASOR will ultimately offer global services to support in-depth risk assessment and full-cycle risk management.

The determination of crop evapotranspiration (ETc) values is very useful information for planning irrigation, water supply estimation, regulation of water rights and river basins hydrologic studies. Values of ETc in the North region of Minas Gerais state, Brazil, were estimated in this research from the multispectral images of the Landsat 5 TM by means of the model Surface Energy Balance Algorithm for Land- SEBAL, based on the simplified energy balance equation of a surface covered by vegetation, using a few daily surface climatological parameters (wind speed, rainfall, air temperature and relative humidity, solar radiation). The aim of this study was to estimate the regional spatial distribution of the energy balance components and evapotranspiration in the study area, covering the irrigated perimeter of Gorutuba, involving the cities of Nova Porteirinha, Janaúba, Porteirinha, Verdelândia and Pai Pedro. Thematic maps of regional evapotranspiration and energy balance components were generated from spectral analyzes of the images obtained, associated with the used weather data. The ability of SEBAL to provide the spatial variability of energy balance components, including evapotranspiration, demonstrated its sensitivity to different occupation of the soil surface vegetation, and to high data temporal and spatial resolutions data, indicating that the SEBAL model can be used in scales and operational routine for north region of Minas Gerais.

The energy balance (EB) components were quantified in a commercial farm with corn crop, irrigated by central pivots, in the Northwestern side of São Paulo state, Southeast Brazil. The SAFER (Simple Algorithm For Evapotranspiration Retrieving) was applied to retrieve the latent heat flux (λE), considering six pivots, covering irrigated areas from 74 to 108 ha. With λE quantified and considering soil heat flux (G) as a fraction of net radiation (Rn), the sensible heat flux (H) was acquired as a residual in the energy balance equation. Seven Landsat satellite images, covering all corn crop stages from 23 April 2010 to 29 August 2010, allowed relating the energy balance components according to the accumulated degree-days (DD_ac) from the planting to harvest dates. The average Rn values ranging from 5.2 to 7.2 MJ m^-2 day^-1, represented 30 to 45% of global solar radiation (RG). Considering the variation of the energy balance components along the corn crop growing seasons, the average ranges for λE, H and G were respectively 0.0 to 6.4 MJ m^-2 day^-1, -1.5 to 6.7 MJ m^-2 day^-1 and 0.1 to 0.6 MJ m^-2 day^-1. The fraction of the available energy (R_n - G) used as λE was from 0.0 to 1.3 indicated a good irrigation management, insuring that the water deficit could not be the reason of any yield reduction. Although Rn did not reflected well the crop stages, its partition strongly depended on these stages. λE higher than Rn and the negative H/Rn, happening sometimes along the corn growing seasons, occurred after the vegetative growth and before the harvest times, indicated heat advection from the surrounding areas to the irrigation pivots, which represented an additional energy source for the evaporative process. The models applied here with only the visible and infrared bands of the Landsat sensor are very useful for the energy balance analyses, considering the size of the corn crop irrigation pivots in Southeast Brazil, when subsidizing a rational irrigation water application in corn crop.

Irrigation system modelling is often used to aid decision-makers in the agricultural sector. It gives insight on the consequences of potential management and infrastructure changes. However, simulating an irrigation district requires a considerable amount of input data to properly represent the system, which is not easily acquired or available. During the simulation process, several assumptions have to be made and the calibration is usually performed only with flow measurements. The advancement of estimating evapotranspiration (ET) using remote sensing is a welcome asset for irrigation system modelling. Remotely-sensed ET can be used to improve the model accuracy in simulating the water balance and the crop production. This study makes use of the Ador-Simulation irrigation system model, which simulates water flows in irrigation districts in both the canal infrastructure and on-field. ET is estimated using an energy balance model, namely SEBAL, which has been proven to function well for agricultural areas. The seasonal ET by the Ador model and the ET from SEBAL are compared. These results determine sub-command areas, which perform well under current assumptions or, conversely, areas that need re-evaluation of assumptions and a re-run of the model. Using a combined approach of the Ador irrigation system model and remote sensing outputs from SEBAL, gives great insights during the modelling process and can accelerate the process. Additionally cost-savings and time-savings are apparent due to the decrease in input data required for simulating large-scale irrigation areas.

The land surface albedo is among the most important parameters controlling the atmospheric radiation fluxes and the surface–atmosphere interactions. In the present study, surface albedo parameters and aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, onboard NASA’s Terra and Aqua satellites, were analyzed and processed for the estimation of the shortwave surface albedo over Europe, Northern Africa and the Middle East, at 1 km × 1 km spatial resolution and on an 8–day average basis, for the period 2001–2012. The surface albedo was computed as a linear combination of black-sky and white-sky albedos. This methodology allows the computation of surface albedo for different values of AOT and solar zenith angle (SZA). MODIS Level 3 AOT data were used in the computations, while the surface albedo was calculated as an average of albedo values, using different SZAs on a pixel basis. The final albedo product was analyzed in terms of spatial and seasonal characteristics, and inter– annual trends, during the period examined. A strong dependency of the albedo on land cover type was found, as it was expected. The results also revealed substantial spatiotemporal variability of the surface albedo in the area examined, highlighting the great potential of satellite remote sensing in supporting climate change related studies, at both local and regional scales.

Vineyard variability within the fields is well known by grape growers, producing different plant responses and fruit characteristics. Many technologies have been developed in last recent decades in order to assess this spatial variability, including remote sensing and soil sensors. In this paper we study the possibility of creating a stable classification system that better provides useful information for the grower, especially in terms of grape batch quality sorting. The work was carried out during 4 years in a rain-fed Tempranillo vineyard located in Rioja (Spain). NDVI was extracted from airborne imagery, and soil conductivity (EC) data was acquired by an EM38 sensor. Fifty-four vines were sampled at véraison for vegetative parameters and before harvest for yield and grape analysis. An Isocluster unsupervised classification in two classes was performed in 5 different ways, combining NDVI maps individually, collectively and combined with EC. The target vines were assigned in different zones depending on the clustering combination. Analysis of variance was performed in order to verify the ability of the combinations to provide the most accurate information. All combinations showed a similar behaviour concerning vegetative parameters. Yield parameters classify better by the EC-based clustering, whilst maturity grape parameters seemed to give more accuracy by combining all NDVIs and EC. Quality grape parameters (anthocyanins and phenolics), presented similar results for all combinations except for the NDVI map of the individual year, where the results were poorer. This results reveal that stable parameters (EC or/and NDVI all-together) clustering outcomes in better information for a vineyard zonal management strategy.

An efficient use of water for irrigation is a challenging task. From an agronomical point of view, it requires establishing the optimal amount of water to be supplied, at the correct time, based on phenological phase and water stress spatial distribution. Indeed, the knowledge of the actual water stress is essential for agronomic decisions, vineyards need to be managed to maintain a moderate water stress, thus allowing to optimize berries quality and quantity. Methods for quickly quantifying where, when and in what extent, vines begin to experience water stress are beneficial. Traditional point based methodologies, such those based on Scholander pressure chamber, even if well established are time expensive and do not give a comprehensive picture of the vineyard water deficit. Earth Observation (E.O.) based methodologies promise to achieve a synoptic overview of the water stress. Some E.O. data, indeed, sense the territory in the thermal part of the spectrum and, as it is well recognized, leaf radiometric temperature is related to the plant water status. However, current satellite sensors have not detailed enough spatial resolution to detect pure canopy pixels; thus, the pixel radiometric temperature characterizes the whole soil-vegetation system, and in variable proportions. On the other hand, due to limits in the actual crop dusters, there is no need to characterize the water stress distribution at plant scale, and a coarser spatial characterization would be sufficient. The research aims to assess to what extent: 1) E.O. based canopy radiometric temperature can be used, straightforwardly, to detected plant water status; 2) E.O. based canopy transpiration, would be more suitable (or not) to describe the spatial variability in plant water stress. To these aims: 1) radiometric canopy temperature measured in situ, and derived from a two-source energy balance model applied on airborne data, were compared with in situ leaf water potential from freshly cut leaves; 2) two source energy balance components were validated trough flux tower measures, then, the actual canopy latent heat flux is compared to in situ leaf water potential.

In this study, space remote sensing data and crop specific information from the ESA-led AgriSAR 2009 campaign are used for studying the profiles of C-band SAR backscatter signals and multispectral-based leaf area index (LAI) over the growth period of canola, pea and wheat. In addition, the correlations between radar backscatter parameters and the crop yields were analyzed, based on extracted statistics of temporal profiles. The results show that the HV backscatter and LAI are correlated differently before and after LAI peak. In addition, the coefficient of determination between peakrelated statistics from polarimetric indicator profiles and yield for pea fields can reach up to 0.68, and for canola and wheat up to 0.47 and 0.5, respectively. HV backscatter and coherence between HH and VV are most.

Mato Grosso state, Central West Brazil, has been highlighted by the grain production, mainly soybean and corn, as first (November-March) and second (April-August) harvest crops, respectively. For water productivity (WP) analyses, MODIS products together with a net of weather stations were used. Evapotranspiration (ET) and biomass production (BIO) were acquired during the year 2012 and WP was considered as the ratio of BIO to ET. The SAFER (Simple Algorithm For Evapotranspiration Retrieving) for ET and the Monteith's radiation model for BIO were applied together, considering a mask which separated the crops from other surface types. In relation to the first harvest crop ET, BIO and WP values above of those for other surface types, happened only from November to January with incremental values reaching to 1.2 mm day^-1; 67 kg ha^-1 day^-1; and 0.7 kg m^-3, respectively; and between March and May for the second harvest crops, with incremental values attaining 0.5 mm day^-1; 27 kg ha^-1 day^-1; and 0.3 kg m^-3, respectively. In both cases, during the growing seasons, the highest WP parameters in cropped areas corresponded, in general, to the blooming to grain filling transition. Considering corn crop, which nowadays is increasing in terms of cultivated areas in the Brazilian Central West region, and crop water productivity (CWP) the ratio of yield to the amount of water consumed, the main growing regions North, Southeast and Northeast were analyzed. Southeast presented the highest annual pixel averages for ET, BIO and CWP (1.7 mm day^-1, 78 kg ha^-1 day^-1 and 2.2 kg m^-3, respectively); while for Northeast they were the lowest ones (1.2 mm day^-1, 52 kg ha^-1 dia^-1 and 1.9 kg m^-3). Throughout a soil moisture indicator, the ratio of precipitation (P) to ET, it was indeed noted that rainfall was enough for a good grain yield, with P/ET lower than 1.00 only outside the crop growing seasons. The combination of MODIS images and weather stations proved to be useful for monitoring vegetation and water parameters, which can contribute to the sustainability of the agro-ecosystems exploration in Mato Grosso state, avoiding water scarcity in the near future.

A method called a “spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model,” which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

The crop yield estimation is essential for the food security and the economic development of any nation. Particularly, the United States is the world largest grain exporter, and the total amount of corn exported from the U.S. accounted for 49.2% of the world corn trade in 2010 and 2011. Thus, accurate estimation of crop yield in U.S. is very significant for not only the U.S. crop producers but also decision makers of food importing countries. Estimating the crop yield using remote sensing data plays an important role in the Agricultural Sector, and it is actively discussed and studied in many countries. This is because remote sensing can observe the large areas repetitively. Consequently, the use of various techniques based on remote sensing data is steadily increasing to accurately estimate for crop yield. Therefore, the objective of this study is to estimate the accurate yield of corn and soybeans using climate dataset of PRISM climate group and Terra/MODIS products in the United States. We construct the crop yield estimation model for the decade (2001-2010) and perform predictions and validation for 2011 and 2012.

High spatial resolution satellite imagery provides an alternative for time consuming and labor intensive in situ measurements of biophysical variables, such as chlorophyll and water content. However, despite the high spatial resolution of current satellite sensors, mixtures of canopies and backgrounds will be present, hampering the estimation of biophysical variables. Traditional correction methodologies use spectral differences between canopies and backgrounds, but fail with spectrally similar canopies and backgrounds. In this study, the lack of a generic solution to reduce background effects is tackled. Through synthetic imagery, the mixture problem was demonstrated with regards to the estimation of biophysical variables. A correction method was proposed, rescaling vegetation indices based on the canopy cover fraction. Furthermore, the proposed method was compared to traditional background correction methodologies (i.e. soil-adjusted vegetation indices and signal unmixing) for different background scenarios. The results of a soil background scenario showed the inability of soil-adjusted vegetation indices to reduce background admixture effects, while signal unmixing and the proposed method removed background influences for chlorophyll (ΔR² = ~0.3; ΔRMSE = ~1.6 μg/cm²) and water (ΔR² = ~0.3; ΔRMSE = ~0.5 mg/cm²) related vegetation indices. For the weed background scenario, signal unmixing was unable to remove the background influences for chlorophyll content (ΔR² = -0.1; ΔRMSE = -0.6 μg/cm ² ), while the proposed correction method reduced background effects (ΔR²= 0.1; ΔRMSE = 0.4 μg/cm²). Overall, the proposed vegetation index correction method reduced the background influence irrespective of background type, making useful comparison between management blocks possible.

The Interest in Unmanned Aerial Vehicles (UAVs) has grown around the world and several efforts are underway to integrate UAV operations routinely and safely into remote sensing applications, specially applied in precision agriculture. Reviewing the use of UAV in agriculture it shows limitations and opportunities. So the challenges of UAV platforms for remote sensing and precision agriculture were identified during a real case studied at a citrus area to monitor the HLB (Huanglongbing) infestation. Recommended actions for moving forward were identified and showed that is possible to use UAVs for detection of crop diseases with high precision.

Recent development in compact, lightweight hyperspectral imagers have enabled UAV-based remote sensing with reasonable costs. We used small hyperspectral imager based on Fabry-Perot interferometer for monitoring small freshwater area in southern Finland. In this study we shortly describe the utilized technology and the field studies performed. We explain processing pipeline for gathered spectral data and introduce target detection-based algorithm for estimating levels of algae, aquatic chlorophyll and turbidity in freshwater. Certain challenges we faced are pointed out.

The North-Western part of the Black Sea is highly affected by eutrophication due to nutrient and sediment load inflow from the Danube River, which is the second largest delta in Europe. To get a general spatial picture of the water quality of the Romanian coast, it is not only time consuming, but also hard to measure with traditional in situ sampling. To solve these issues, methods have been developed to use close range spectral measurements for accurate and cheap assessments in real-time for the concentrations of Chlorophyll-a, Total Suspended Matter and water transparency. This paper presents the applicability of a state-of-the-art hand-held hyper-spectral sensor and a simple water transparency indicator for monitoring water quality. The fieldwork was conducted during the summer of 2013 on the Romanian coast of the Black Sea. The same techniques are used to calculate these parameters from satellite images (MODIS). The validation results and potential applications of the instruments will be discussed.

Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral identified and the health status of these species are assessed by the selected band. Further, the performance of this band selection approaches are evaluated in multi-sensor image fusion for better mapping of mangrove ecosystems, wherein spatial resolution is enhanced while retaining the optimal number of hyperspectral bands.

The use of satellite remote sensing data is a valid alternative to the classical survey bathymetric methods for bathymetric estimation in shallow waters. Multispectral satellite data has been used to produce bathymetric maps by considering the pixel reflectance as a depth indicator. Teodoro et al., (2010) already proposes a model for the estimation of depth based on Principal Component Analysis (PCA) of an IKONOS-2 image, for the Douro River estuary (Porto, Portugal). In this work, alternative univariate and bivariate models are proposed for the same IKONOS-2 image based on PCA and Independent Component Analysis (ICA). The PCA is the standard method for separating mixed signals. Such analysis provides signals that are linearly uncorrelated. Although the separated signals are uncorrelated they could still be depended, i.e., nonlinear correlation remains. The ICA was developed to investigate such data. Fast ICA algorithm was used in Matlab®. The results obtained were compared with the bathymetric estimation trough PCA. Best univariate ICA based model allowed to estimate depth with a mean error of 0.00m [with 1.15 of standard deviation], outperforming the best PCA based univariate model results of 0.39[1.34], even with the first PCA component explains 80% of data variance. With bivariate models is possible to reduce the standard deviation of the error to 1.01m.

The spatiotemporal evolution of the snow cover may help to obtain conclusions on the variability of the atmospheric agents in high mountain areas. That evolution is difficult to analyze due to the heterogeneity of the snow distribution on the ground. The use of terrestrial images which are treated to obtain snow detection, is an inexpensive and promising technique more capable of solving the drawbacks that other techniques presented. This work analyzes the spatiotemporal variability of the snow by using terrestrial photography and the effects of scale on its modelling in the river Trevélez valley, in southern Spain. Temporal series of images of the area were employed from September, 2011 up to May, 2013. By georeferencing the images, the snow pixels were identified, and the temporal variations in the snow cover with respect to its spatial distribution were determined. The maps obtained were used as a direct source of data assimilation in that model. Finally, the improvement in the global simulation of the snow model when this data source was incorporated was assessed by making a comparative study between the temporal series of the snow flow measured at the gauging point band the flow obtained in the simulation. As a result, a temporal series of snow maps of the area was made. In turn, the assimilation of the data improved the simulation by up to 9.74% for the equivalent of water. At a watershed scale, the simulation of the flow at the control point reproduced the trend observed. These results permit one to conclude that the methodology used is precise enough to find out the exact position of snow cover and to improve the efficiency of the model used.

This region has the particularity of, at the same time, coexisting with sporadic floods and scarcity of water. This situation requires complex studies involving water resources based on runoff of rain waters and on the courses of the rivers. For the extraction of drainage was carried out by using of the system for hydrological modeling treatment TerraHidro, developed by INPE's Image Processing Division. This system uses the PFS method for extraction of drainage, which has provided good results, enabling the reduction of the time spent on manual editing of drainage errors. TerraHidro has a tool called Height Above the Nearest Drainage (HAND) which gives information on potential flood areas. Elevation data were used in the Aster GDEM with spatial resolution of 30 meters for drainage extraction. A qualitative comparison was performed between drainage extracted by TerraHidro and drainage manually extracted by a specialist.

The combination of thermal satellite remote sensing and geochemical tracing has been demonstrated as a robust and cost effective technique to identify potential groundwater discharge sites in coastal areas on a regional scale. Here, the approach is evaluated in its applicability to lakes, demonstrated through a case study in the west of Ireland. Surface water temperature patterns generated from Landsat 7 ETM+ Thermal Infrared (TIR) images are used to detect groundwater inputs captured as anomalous cold plumes visibly emanating from shallow lake margins during summer months. Qualitative assessments of groundwater inputs are completed using natural tracers (radon (²²²Rn, t½ = 3.8 days) and conductivity) to verify the presence of groundwater and to identify localized seepage sites or groundwater “hotspots”. Despite the difficulties in acquiring cost- and cloud free satellite imagery and the inevitable mismatch between satellite image acquisition and in-situ lake survey dates, the results are extremely promising. Temperature values generated from the thermal images reveal a strong negative correlation with measured radon activity which implies that decreases in surface water temperatures are associated with increases in radon activity and hence groundwater inputs to the lake. The study demonstrates the suitability of the approach as a comprehensive and cost-effective preliminary assessment tool for identification and localization of groundwater discharge entry points for use potentially in any region where discernible temperature differences exist. Understanding where groundwater discharge occurs is the first step towards more in-depth geochemical surveys that seek to clarify the role played by groundwater in lacustrine biogeochemical budgets.

Different satellite missions have instruments to measure the water level variation of oceans and some of these instruments are being used in continental water applications with satisfying results. Altimeters on-board the Envisat and SARAL(Altika) satellites are consistently used to measure the water level in continental water bodies. Recent studies on satellite altimetry combined with satellite imagery have shown the great potential of this technique to estimate the water volume of rivers, lakes, wetlands and reservoirs and its temporal variation in response to climate and other environmental variables. A consistent monitoring of water level variations in reservoirs is crucial to the development policies and implementation of actions regarding the distribution and use of the stored water resource. The Trés Marias reservoir is located within the São Francisco river basin, known as the national integration" river, which provides water flow to the semi-arid region of Brazil. This study presents a method to combine satellite altimetry and imagery of the lake's surface to estimate volume changes and create a model from which volume changes could be computed from either the altimetry or the lake's surface area. Our intention with this study is to evaluate the method and its precision, and the possibility to apply it in other areas, such as wetlands and other lakes where in situ measurements are not available. Moreover, data of monitoring stations usually have an arbitrary altitude reference and are not available for the general public; the data from the satellite altimetry has the advantage of being of global reference (geoid) and compatible with the establishment of a worldwide lake and reservoir database. We combined Envisat and SARAL/Altika altimetry data from 2007-2014 period with Landsat imagery from the same time frame. The data was corrected using a novel processing technique resulting in a relative precision of 0.24 m (RMSE).

This paper presents a methodology to determine the permanent protected areas (PPA) of the the riverbanks. The amount of protected area depends on the river width and the size of each property which have a river running through or by it, as stated in the Brazilian forest code law. The rules are: 30 meters for rivers up to 10 meters wide, 50m for rivers 10 to 50m wide, 100m for rivers 50 to 200m wide, 200m for rivers 200 to 600m wide, and 500m, to rivers wider than 600m. The steps to determine the PPA buffer along the river are (1) construction of the triangular grid (TIN) that constitutes the basis for the calculation of the central axis of the river; (2) definition of the points representing the central axis of the river, called skeletonization; (3) definition of the river width; (4) calculation of the buffer for each river segment. PPA is defined by overlaying the river protected area polygon with the property polygon. A PPA area of a property can be reduced according to its size and according to public improvements like roads, permanent protected areas, for example. At the end the area to be preserved in a property is delimited.

For better risk management, detailed and quantitative measurement of channel and stream-bed structure is required to assimilate and forecast how the water and sediment flow in mountain channels. Our previous research demonstrated good performance of green-wavelength TLS for measurement of submerged stream-bed in a steep mountain channel. The results also showed that each of water depth and flow velocity alone does not affect the accuracy of TLS measurement. Instead, it was indicated that the specification of data acquisition may have an impact on the accuracy of derived Digital Terrain Models (DTMs). Therefore, this paper examines how the acquisition protocol of TLS affects the accuracy of data collected in the mountain channel. First, it is tested whether different scanner height, that is, incident angle affects the data acquisition in terms of point density and accuracy. Then, the difference in minimum point spacing is examined to find how much impact it has on derived DTM. It is also analyzed whether a combination of multiple TLS data acquired from different direction improves data accuracy, compared to the data acquired by single measurement. All the acquired underwater data by TLS are water refraction corrected and validated using field surveyed data. The results of these tests showed that the accuracy of derived DTM was improved when the scanner height was raised or data was acquired from multiple directions, however, acquiring denser point cloud with minimum point spacing of 1 mm did not improve the accuracy of the data.

The spectral assessment of soil properties is handicapped by the fact that spectral predictive mechanisms often vary from one population to another. In a landscape approach, heterogeneous conditions with a wide variety of combinations of spectrally active factors have to be considered. Heterogeneity, however, is one main reason for poor predictions from spectroscopic data, as an optimal calibration needs limited but sufficient set heterogeneity. For our study, the investigated plots were located in an area that covered about 600 km²; geologic conditions and sampled soil types were highly variable. In total, 172 soil samples were taken from the top horizon of agricultural fields, afterwards analysed in the laboratory for total organic carbon (OC) and black carbon (BC) and additionally measured with a full range ASD FieldSpec-instrument. The heterogeneity of the sample set was reflected by both the analysed soil parameters and the measured soil spectra. As a consequence, one “global” calibration model (with PLSR) provided only moderate results for the studied soil variables. In the following we focused on two issues, which were i) to replace the global calibration by local calibration procedures, and ii) to study the effect of spectral variable selection for calibration success. For the CARS selection procedure (“competitive adaptive reweighted sampling”), the results demonstrated that more accurate estimates can be obtained using selected variables instead of the full spectrum.

The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs. It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as “marginal”. Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited. Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate and cost-effective information on land use in general and agronomic parameters in the particular. The availability of high spatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level. In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and the delineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDAR data. We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grove cultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and its automatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment can be confirmed. Data extracted by the proposed methodology can be used as input data for decision-making support systems for olive grove management.

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC’s model comprises a small set of simple IF–THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

With increasing population pressure throughout the world and the need for increased agricultural production there is a definite need for improved management of the world's agricultural resources. Comprehensive, reliable and timely information on agricultural resources is necessary for the implementation of effective management decisions. In that sense, the demand for high-quality and high-frequency geo-information for monitoring of agriculture and its associated ecosystems has been growing in the recent decades. Satellite image data enable direct observation of large areas at frequent intervals and therefore allow unprecedented mapping and monitoring of crops evolution. Furthermore, real time analysis can assist in making timely management decisions that affect the outcome of the crops. The DEIMOS-1 satellite, owned and operated by ELECNOR DEIMOS IMAGING (Spain), provides 22m, 3-band imagery with a very wide (620-km) swath, and has been specifically designed to produce high-frequency revisit on very large areas. This capability has been proved through the contracts awarded to Airbus Defence and Space every year since 2011, where DEIMOS-1 has provided the USDA with the bulk of the imagery used to monitor the crop season in the Lower 48, in cooperation with its twin satellite DMCii’s UK-DMC2. Furthermore, high density agricultural areas have been targeted with increased frequency and analyzed in near real time to monitor tightly the evolution. In this paper we present the results obtained from a campaign carried out in 2013 with DEIMOS-1 and UK-DMC2 satellites. These campaigns provided a high-frequency revisit of target areas, with one image every two days on average: almost a ten-fold frequency improvement with respect to Landsat-8. The results clearly show the effectiveness of a high-frequency monitoring approach with high resolution images with respect to classic strategies where results are more exposed to weather conditions.

The amount of carbon sequestration by vegetation can be estimated using vegetation productivity. At present, there is a knowledge gap in oil palm net primary productivity (NPP) at a regional scale. Therefore, in this study NPP of oil palm trees in Peninsular Malaysia was estimated using remote sensing based light use efficiency (LUE) model with inputs from local meteorological data, upscaled leaf area index/fractional photosynthetically active radiation (LAI/fPAR) derived using UK-DMC 2 satellite data and a constant maximum LUE value from the literature. NPP values estimated from the model was then compared and validated with NPP estimated using allometric equations developed by Corley and Tinker (2003), Henson (2003) and Syahrinudin (2005) with diameter at breast height, age and the height of the oil palm trees collected from three estates in Peninsular Malaysia. Results of this study show that oil palm NPP derived using a light use efficiency model increases with respect to the age of oil palm trees, and it stabilises after ten years old. The mean value of oil palm NPP at 118 plots as derived using the LUE model is 968.72 g C m^-2 year^-1 and this is 188% - 273% higher than the NPP derived from the allometric equations. The estimated oil palm NPP of young oil palm trees is lower compared to mature oil palm trees (<10 years old), as young oil palm trees contribute to lower oil palm LAI and therefore fPAR, which is an important variable in the LUE model. In contrast, it is noted that oil palm NPP decreases with respect to the age of oil palm trees as estimated using the allomeric equations. It was found in this study that LUE models could not capture NPP variation of oil palm trees if LAI/fPAR is used. On the other hand, tree height and DBH are found to be important variables that can capture changes in oil palm NPP as a function of age.

Gas regulation is one of the important ecological service functions of ecosystem. Plants transform solar energy into biotic energy through photosynthesis, fixing CO2 and releasing O2, which plays an irreplaceable role in maintaining the CO2/O2 balance and mitigating greenhouse gases emissions. The ecosystem service value of gas regulation can be evaluated from the amount of CO2 and releasing O2. Taken the net primary productivity (NPP) of ecosystem as transition parameter, the value of gas regulation service in Beijing city in recent 30 years was evaluated and mapped with time series LandSat images, which was used to analyze the spatial patterns and driving forces. Results showed that he order of ecosystem service value of gas regulation in Beijing area was 1978 < 1992 < 2000 < 2010, which was consistent with the order of NPP. The contribution order for gas regulation service of six ecosystems from1978 to 2010 was basically stable. The forest and farmland played important roles of gas regulation, of which the proportion reached 80% and varied with the area from 1978 to 2010. It indicated that increasing the area of forest and farmland was helpful for enhance the ecosystem service value of gas regulation.

Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm^-2 for NAMK, and 0.503 and 10.661 μg cm^-2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

Biomass fires can significantly degrade regional air quality through the emission of primary aerosols and the photochemical production of ozone and secondary aerosols. The injection height of smoke from biomass burning into the atmosphere (‘plume rise height’) is one of the critical factors in determining the impact of fire emissions on air quality. Plume rise models are used to simulate plume rise height and prescribe the vertical distribution of fire emissions for input to smoke dispersion and air quality models. While several plume rise models exist, their uncertainties, biases, and application limits when applied to biomass fires are not well characterized. The poor state of model evaluation is due in large part to a lack of appropriate observational datasets. We have initiated a research project to address this critical observation gap. In August of 2013 we performed a multi-agency field experiment designed to obtain the data necessary to improve the air quality models used by agricultural smoke managers in the northwestern United States. In the experiment, the ground-based mobile lidar, developed at the US Forest Service Missoula Fire Science Laboratory, was used to monitor plume rise heights for nine agricultural fires in the northwestern United States. The lidar measurements were compared with plume rise heights calculated with the Briggs equations, which are used in several smoke management tools. Here we present the preliminary evaluation results and provide recommendations regarding the application of the models to agricultural burning based on lidar measurements made in the vicinity of Walla Walla, Washington, on August 24, 2013.

Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modeling vegetation-climate interactions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of forest biophysical parameters like as Normalized Difference Vegetation Index (NDVIs) and Leaf Aria Index (LAI), which requires time-series data with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). This paper will quantify this impact over a forest ecosystem Cernica- Branesti placed in the North-Eastern part of Bucharest town, Romania, with NDVI and LAI parameters extracted from MODIS Terra and NOAA AVHRR satellite images in synergy with meteorological data over 2000-2013 periods. For investigated test area, considerable NDVI and LAI decline have been observed during heat wave and drought events of 2003, 2007 and 2012 years. Under water stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.

An experimental approach to the land drainage system detection and its physical and spatial parameters evaluation by the form of pilot project is presented in this paper. The novelty of the approach is partly based on using of unique unmanned aerial vehicle - airship with some specific properties. The most important parameters are carrying capacity (15 kg) and long flight time (3 hours). A special instrumentation was installed for physical characteristic testing in the locality too. The most important is 30 meter high mast with 3 meter length bracket at the top with sensors recording absolute and comparative temperature, humidity and wind speed and direction in several heights of the mast. There were also installed several measuring units recording local condition in the area. Recorded data were compared with IR images taken from airship platform. The locality is situated around village Domanín in the Czech Republic and has size about 1.8 x 1.5 km. There was build a land drainage system during the 70-ties of the last century which is made from burnt ceramic blocks placed about 70 cm below surface. The project documentation of the land drainage system exists but real state surveying haven´t been never realized. The aim of the project was land surveying of land drainage system based on infrared, visual and its combination high resolution orthophotos (10 cm for VIS and 30 cm for IR) and spatial and physical parameters evaluation of the presented procedure. The orthophoto in VIS and IR spectrum and its combination seems to be suitable for the task.

The Alto Tocantins watershed, located in the Brazilian Savanna (Cerrado biome), is under an intense land use and occupation process, causing increased pressure on natural resources. Pasture areas in the region are highly relevant to the rational use of natural resources in order to achieve economic and environmental sustainability. In this context, remote sensing techniques have been essential for obtaining information relevant to the assessment of vegetation conditions on a large scale. This study aimed to apply this tool in conjunction with field measurements to evaluate evapotranspiration (ET) against pasture degradation indicators. The SAFER algorithm was applied to estimate ET using MODIS images and weather station data from year 2012. Results showed that ET was lower in degraded pastures. It is noteworthy that during low rainfall period, ET values were 22.2% lower in relation to non-degraded pastures. This difference in ET indicates changes in the partition of the energy balance and may impact the microclimate. These results may contribute to public policies that aim to reduce the loss of the productive potential of pastures.

The objective of this study was the spatial identification of the NDVI index and cotton yield distributions through different crop phenological stages using geostatistical methods in Goiás state, Brazil. The experiment was carried out in a commercial field with 47.4 ha, in 80x80m georeferenced grid with 74 plots. Yield monitor data and multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates to monitor the plant vigor. Satellite images of AWiFS sensor were acquired on 08/02/2011 and 01/04/2011, during the first flowering and fruiting cotton stages, respectively, corresponding to 70 and 120DAE (days after emergence). Measures of canopy reflectance, plant height and leaf nitrogen content were determined and cotton yield was obtained by mechanical harvest in August, 2011. Data were analyzed using descriptive statistics, correlation and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between NDVI and cotton yield at 120DAE. At first flowering, the NDVI and cotton yield showed strong spatial dependence, while for 120DAE there was no dependence, probably due to the enlargement of vegetated coverage. There were similarities in the bottom left of the study area with high values of NDVI, as well as the highest values of cotton yield due to excellent plant vigor in the cotton flowering stage. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct stages of plant growth and also to predict the cotton yield.

The use of satellite methods for inland waters is often difficult because of their spatial resolution comparable to or greater thatn the size of water resevoirs. Remote sensing with high spatial resolution is often associated with ea large repeat period of data, or with a significant dependence of the quality of data on weather conditions. In this regard, the sue of Jason-2 sattelite equipped with dual-frequency (13.6 GHz and 5 GHz) radar altimeters and passive three-frequency (18,21 and 37 GHz) microwave radiometers is of interest, because the footprint diameter of their altimeters in Ku-band is about 10km and the repeat period of observations is ten days, that make it suitable for observations of large and medium-sized inland waters. In this work we use the data of Jason-2 satellite to determine the water level variations ice-cover régime of 6 lakes in Russia, water areas of which are intersected by the tracks of this satellite. Variations in water level is calculated on the base of retracking method taking into account the fact that the waveforms of altimetry pulses of satellites Jason-2 are distorted due to the influence of land. Satellite data are compared with available in situ observations and the correlation coefficient with in situ is calculated. The ice regime of lakes is determined using the a new method based on the analysis of the difference between the brightness temperatures of land and water in summer and winter periods. For validation of this method the visual images of the lakes from Aqua and Landsat satellites and in situ data are used.

The solar radiation incidence on the horizontal plane is not the true solar radiation (also called surface solar radiation) on the earth surface, it not take the influence of the rugged terrain into account. Topographic correction process is established which necessarily take integrated consideration of the geographic factors and the local topographic factors (i.e. slope, aspect, terrain inter-shielding effect). Based on the high resolution Digital Elevation Model (DEM) data and the horizontal solar radiation as the input data of topographic correction process, using the mountain solar radiation correction model to simulate the topographic correction process and to present the spatial distribution of surface solar radiation in China Ganzi region on June 30, 2010. Because of the influence of the rugged terrain, the spatial distribution of surface solar radiation is accompanied by the strong spatial heterogeneity, and the spatial representativeness of the observed data of meteorological station is limited. By use of the variogram model to calculate the spatial representativeness and to associate the strength of spatial representativeness with the distance. The results indicated that: 1) rugged terrain mainly makes the solar radiation the redistribution effect significantly on sunny/shady slope of local region, and the increase of slope has a subduction effect on radiation. The terrain factor is essential on determining the solar radiation over the complex terrain. 2) The spatial representativeness of Ganzi meteorological station is approximately 350 meters, the strength of spatial representativeness has the negatively correlation with the distance. There is a necessary to consider the spatial representativeness when verifying the retrieved data.

Wheat (Triticum aestivum) is the second most produced cereal in the world, and has major importance in the global agricultural economy. Brazil is a large producer of wheat, especially the Rio Grande do Sul state, located in the south of the country. The purpose of this study was to analyze the estimation of biophysical parameters – evapotranspiration (ET), biomass (BIO) and water productivity (WP) – from satellite images of the municipalities with large areas planted with wheat in Rio Grande do Sul (RS). The evapotranspiration rate was obtained using the SAFER Model (Simple Algorithm for Retrieving Evapotranspiration) on MODIS (Moderate Resolution Imaging Spectroradiometer) images taken in the agricultural year 2012. In order to obtain biomass and water productivity rates we applied the Monteith model and the ratio between BIO and ET. In the beginning of the cycle (the planting period) we observed low values for ET, BIO and WP. During the development period, we observed an increase in the values of the parameters and decline at the end of the cycle, for the period of the wheat harvest. The SAFER model proved effective for estimating the biophysical parameters evapotranspiration, biomass production and water productivity in areas planted with wheat in Brazilian Southern. The methodology can be used for monitoring the crops' water conditions and biomass using satellite images, assisting in estimates of productivity and crop yield. The results may assist the understanding of biophysical properties of important agro-ecosystems, like wheat crop, and are important to improve the rational use of water resources.

Remote sensing plays a significant role in local, regional and global monitoring of land covers. Ecological concerns worldwide determine the importance of remote sensing applications for the assessment of soil conditions, vegetation health and identification of stress-induced changes. The extensive industrial growth and intensive agricultural land-use arise the serious ecological problem of environmental pollution associated with the increasing anthropogenic pressure on the environment. Soil contamination is a reason for degradation processes and temporary or permanent decrease of the productive capacity of land. Heavy metals are among the most dangerous pollutants because of their toxicity, persistent nature, easy up-take by plants and long biological half-life. This paper takes as its focus the study of crop species spectral response to Cd pollution. Ground-based experiments were performed, using alfalfa, spring barley and pea grown in Cd contaminated soils and in different hydroponic systems under varying concentrations of the heavy metal. Cd toxicity manifested itself by inhibition of plant growth and synthesis of photosynthetic pigments. Multispectral reflectance, absorbance and transmittance, as well as red and far red fluorescence were measured and examined for their suitability to detect differences in plant condition. Statistical analysis was performed and empirical relationships were established between Cd concentration, plant growth variables and spectral response Various spectral properties proved to be indicators of plant performance and quantitative estimators of the degree of the Cd-induced stress.

Chlorophyll-a (CHL-a) and Sea Surface Temperature (SST), amongst others, are proxies or indicators for water quality and can be easily retrieved synoptically and almost in near-real time through satellite remote-sensing. However, as they evolve in space and time in response to winds and currents, a full resolution of the temporal and spatial scales of these latters is required and their influence in shaping the distribution of water quality parameters needs to be assessed. While providing synoptic views and revealing mesoscale features, satellites suffer, indeed, from inadequate representation of sub-grid physical processes and lack of temporal resolution. Conventional point-wise measurements provide data to study high-frequency motions such as tides or high-frequency wind-driven circulation, lacking on the other hand of the spatial resolution required to resolve their spatial variability. We show here that a combined use of near-surface currents, available through High-Frequency (HF) radars, and satellite data (e.g., TERRA and AQUA/MODIS, VIIRS/NPP), are complementary tools as they help solving both fine-scale structures, especially at the coastal boundaries where satellite imageries lack of the required resolution, as well as satellite-derived mesoscale structures are fundamental aid to understand and interpret finer-scale structures in terms of larger-scale ocean dynamics.

This paper shows the results of a scientific research in which a GNSS continuous monitoring system for earth-dam deformations has been developed, then, deformations have been related with reservoir water surface and level. The experiment was conducted near Bivona (Sicily, Italy), on the Castello dam (Magazzolo Lake). On the top of the dam three control points were placed and three GNSS permanent stations were installed. The three stations continuously transmitted data to the control centre of the University of Palermo. The former has been determined using freely available satellite data (specifically Landsat 7 SLC-Off) collected during the whole study period (DOYs 101 to 348 2011). Issues related with the un-scanned rows filling and to better distinguish water from land pixels on the shoreline. The aim of this work is various: first of all, we want to evaluate whether the GPS post processing techniques can provide static results comparable to other monitoring techniques, such as spirit levelling. The study could take a significant importance given that the Italian legislation until today does not provide for the use of this technology to manage or monitor dams displacements or other civil engineering constructions. The use of GPS data in structural monitoring could in fact reduce some management costs. Usually the conventional GPS monitoring methods, where a base station GPS receiver must be located near the dam, did not ensure that the accuracy of results have been independent from the displacement of the crown (top end of dam). In this paper, a new approach in the area of study of the GNSS permanent network has been engaged to solve these problems. Field-testing results show that the new GNSS approach has excellent performances, and the monitoring of different section of the dam could reveal important information on its deformation, that its not operationally possible to retrieve elsewhere. The post-processing accuracy positioning is around 1–5 mm for the deformations monitoring of the Castello dam. Displacements of different sections of the dam reveal different behaviour (in time and periodicity) that looks to be related with water surface (and level) retrieved from remote sensing.

LAI is defined as one sided green leaf area per unit ground area in broadleaf canopies and is an important input parameter to monitor crop growth conditions and to improve the performance of crop yield models. Because direct measurements of LAI are usually time-consuming and require continuous updates, remote sensing is an alternative to estimate this attribute over large areas as watershed scale. The primary objective of this work was to derive a reliable LAI estimation model from VHR satellite data to be compared with moderate resolution satellite products in order to improve LAI estimation performance for next validation activities. Due to lack of contemporaneous satellite and on-site sensor data acquisitions and intrinsic complexity of physical models, in our study case the semi-empirical approach with the CLAIR model was applied. It is based on an inverse exponential relationship between LAI and the WDVI (Weighted Difference Vegetation Index) related to different land covers. LAI values were generated from multispectral GeoEye-1 sensor data covering a time space of 5 years (2009-2013) to study crop phenological stages on the study area of the Carapelle watershed located in the North of Puglia region (Southern Italy). Data were preliminarily pre-processed (geometric and radiometric correction), classified (ISODATA method) and texture based analyzed in order to extract the vegetated areas (mainly cereal crops). Finally, the resulted maps were compared with moderate resolution satellite data by reaching a possible correspondence.

Water productivity (WP) of various classes of soil usage from watersheds was estimated using the SAFER - Simple Algorithm For Evapotranspiration Retrieving - algorithm and the Monteith equation to estimate the parameters of biomass production (BIO). Monteith’s equation is used to quantify the absorbed photosynthetically active radiation (APAR) and Actual Evapotranspiration (ET) was estimated with the SAFER algorithm. The objective of the research is to analyze the spatial-temporal water productivity in watersheds with different uses and soil occupation during the period from 1996 to 2010, in conditions of drought and using the Monteith model to estimate the production of BIO and using the SAFER model for ET. Results indicated an increase of 153.2% in ET value during the period 1997-2010, showing that the irrigated areas were responsible for this increase in ET values. In September 2000, image of day of year (DOY) 210 showed high values of BIO, with averages of 80.67 kg ha^-1d^-1. In the year 2010 (DOY:177), the mean value of BIO was 62.90 kg ha^-1d^-1, with an irrigated area with a maximum value of 227.5 kg ha^-1d^-1. The highest incremental values of BIO is verified from the start of irrigated areas equal to the value of ET, because there is a relationship between BIO and ET. The maximum water productivity (WP) value occurred in June/2001, with 3,08 kg m^-3, the second highest value was in 2010 (DOY:177), with a value of 2,97 kg m^-3. Irrigated agriculture show the highest WP value, with maximum value of 6.7 kg m^-3. The lowest WP was obtained for DOY 267, because of the dry season with condition of low soil moisture.

Remote sensing (RS) has been recognized as the most feasible means to provide spatially distributed regional evapotranspiration (ET). However, classical RS flux algorithms (SEBS, S-SEBI, SEBAL, etc.) can hardly be used with coarser resolution RS data from sensors like MODIS or AVHRR for no consideration of surface heterogeneity in mixed pixels even they are suitable for assessing the surface fluxes with high resolution RS data.A new model named FAFH is developed in this study to enhance the accuracy of flux estimation in mixed pixels based on high resolution landcover classification data. The area fraction and relative sensible heat fraction of each heterogeneous land use type calculated within coarse resolution pixels are calculated firstly, and then used for the weighted average of modified sensible heat. The study is carried out in the core agricultural land of Zhangye, the middle reaches of Heihe river based on the flux and landcover classification product of HJ-1B in our earlier work. The result indicates that FAFH increases the accuracy of sensible heat by 5% absolutely, 10.64% relatively in the whole research area.