This PDF file contains the front matter associated with SPIE Proceedings Volume 9637, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

This paper is concerned with the estimation of wheat and rapeseed crops parameters (height, leaf area index and dry biomass), during their whole vegetation cycle, using satellite time series both acquired in optical and microwave domains. Crop monitoring at a fine scale represents an important stake from an environmental point of view as it provides essential information to combine increase of production and sustainable management of agricultural landscapes. The aim of this paper is to compare the potential of optical and SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation. Satellite (Formosat-2, Spot-4/5 and Radarsat-2) and ground data were acquired during the MCM’10 experiment conducted by the CESBIO laboratory in 2010. A vegetation index was derived from the optical images: the NDVI and backscattering coefficients and polarimetric parameters were computed from Radarsat-2 images. Results of this study show the high interest of using SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation during the whole vegetation cycle instead of using optical vegetation index. Polarimetric parameters do not improve wheat parameters estimation (e.g. backscattering coefficient σ° VV corresponds to the best parameter for wheat height estimation (r² = 0.60)) but show their high potential for rapeseed height and dry biomass monitoring (i.e. Shannon Entropy polarimetry (SEp ; r² = 0.70) and Radar Vegetation Index (RVI ; r² = 0.80) respectively).

Continuous monitoring on farming practices is urgently needed provided the challenges faced by rice fields. Information of seasonal parameters supplies crucial inputs for monitoring rice fields as well as improving other applications, such as biomass monitoring, yield estimation, integrated pest management, irrigation water management, and precision farming. We extracted the heading stages using multi-temporal MODerate resolution Imaging Spectroradiometer (MODIS) imageries in rice fields in northern districts of West Java, Indonesia. The spatial distribution of the heading stages in the whole year suggests complex cropping pattern of rice fields in West Java. The monthly average of EVI shows that green waves move northward as the results of stipulated cropping calendar. The Root Mean Square Error (RMSE) for the heading stages is 12.77 days. The heading stages periods of most rice fields are from the middle of February to the middle of March and from the middle of June to the middle of July for rendeng and gadu, consecutively. The findings provide timely and cost effective information for monitoring rice fields.

North Carolina (NC) is the second largest producer of hogs in the United States with Duplin county, NC having the densest population of hogs in the world. In NC, liquid swine manure is generally stored in open-air lagoons and sprayed onto sprayfields with sprinkler systems to be used as fertilizer for crops. Swine factory farms, termed concentrated animal feeding operations (CAFOs), are regulated by the Department of Environment and Natural Resources (DENR) based on nutrient management plans (NMPs) having balanced plant available nitrogen (PAN). The estimated PAN in liquid manure being sprayed must be less than the estimated PAN needed crops during irrigation. Estimates for PAN needed by crops are dependent on crop and soil types. Objectives of this research were to develop a new, time-efficient method to identify PAN needed by crops on Duplin county sprayfields for years 2010-2014. Using remote sensing data instead of NMP data to identify PAN needed by crops allowed calendar year identification of which crops were grown on sprayfields instead of a five-year range of values. Although permitted data have more detailed crop information than remotely sensed data, identification of PAN needed by crops using remotely sensed data is more time efficient, internally consistent, easily publically accessible, and has the ability to identify annual changes in PAN on sprayfields. Once PAN needed by crops is known, remote sensing can be used to quantify PAN at other spatial scales, such as sub-watershed levels, and can be used to inform targeted water quality monitoring of swine CAFOs.

From the early scanning spectrometers, the utility field spectroscopy has been constrained: by detector sensitivity, leading to high integration times; portability; and the measurements having very limited support (possibly an Earth surface area in the order of 0.25m² to 2m²). However, over the last twenty years or so detector sensitivities and electronics have in improved leading to practical Earth surface sampling time increasing their utility to support Earth observation science and as optical remote sensing teaching and training tools. Now the uncertainties associated with field spectral measurements are being more widely recognised and field sampling methods and instrument continue to be developed to enable these uncertainties to be quantified and minimised. There are a number of key challenges which still need to be more widely addressed if field spectroscopy is to provide evermore reliable and replicable measurements. An understanding of the systematic biases introduced by this sampling method has begun to be recognised. In addition, the mismatch in scale between near-ground spectroscopy measurement and observations from space-borne sensor has begun to be addressed with the development of unmanned aerial vehicles as platforms and lightweight and miniaturised stateof- the-art spectrometer systems. It is now possible to non-invasively sample terrestrial and hydrological ecosystems in a statistically robust manner and do so with supports similar in scale to those of air- and space-borne sensors. These developments will revolutionise the use of field spectroscopy to support empirical science and model development and the calibration and validation of space-based observations.

Pasture quality is a critical determinant which influences animal performance (live weight gain, milk and meat production) and animal health. Assessment of pasture quality is therefore required to assist farmers with grazing planning and management, benchmarking between seasons and years. Traditionally, pasture quality is determined by field sampling which is laborious, expensive and time consuming, and the information is not available in real-time. Hyperspectral remote sensing has potential to accurately quantify biochemical composition of pasture over wide areas in great spatial detail. In this study an airborne imaging spectrometer (AisaFENIX, Specim) was used with a spectral range of 380-2500 nm with 448 spectral bands. A case study of a 600 ha hill country farm in New Zealand is used to illustrate the use of the system. Radiometric and atmospheric corrections, along with automatized georectification of the imagery using Digital Elevation Model (DEM), were applied to the raw images to convert into geocoded reflectance images. Then a multivariate statistical method, partial least squares (PLS), was applied to estimate pasture quality such as crude protein (CP) and metabolisable energy (ME) from canopy reflectance. The results from this study revealed that estimates of CP and ME had a R² of 0.77 and 0.79, and RMSECV of 2.97 and 0.81 respectively. By utilizing these regression models, spatial maps were created over the imaged area. These pasture quality maps can be used for adopting precision agriculture practices which improves farm profitability and environmental sustainability.

Fluorescence is regarded as a valuable tool to investigate the eco-physiological status of vegetation. Chlorophyll a, which emits a typical fluorescence in the red/far-red region of the e.m. spectrum, plays a key role in the photosynthetic process and its fluorescence is considered an effective proxy of photosynthetic activity of plants. Laser Induced Fluorescence (LIF) has been studied for several decades both at leaf- and canopy-level by means of optical fibers-coupled instrumentation and fluorescence LIDAR systems. On the other hand, Solar-Induced Fluorescence (SIF) has been the object of several scientific studies quite recently, with the aim to investigate the feasibility of measuring the fluorescence of vegetation using passive spectroradiometers in view of global scale monitoring from satellite platforms. This paper presents the main technical features and preliminary tests of a fluorescence LIDAR, recently upgraded to acquire maps of interleaved LIF and SIF measurements at canopy level. In-house developed electronics and software permits the acquisition of interleaved LIF and SIF spectra by switching on/off the laser, the selection of the suitable grating, the setting of the integration time and the synchronization of the Intensified CCD (ICCD) gate opening time. For each pixel of the map, a fluorescence dataset can be acquired containing a LIF spectrum – from 570 nm to 830 nm with a spectral resolution of 0.5 nm - and radiance spectra from 685.53 nm to 690.30 nm with subnanometric spectral resolution containing the molecular oxygen O2-B telluric absorption band. The latter can be exploited for polynomial regression data fit and SIF retrieval.

Accurate estimation of leaf chlorophyll content (LCC) has great significance in study of the winter wheat, which is important for indicating nutrition status and photosynthetic. Selecting the closed related variable is the key to LCC monitoring. The variable importance for projection (VIP), applied to little samples and strong correlation data, is one of variable selection methods. In this study, VIP was used to select spectral variables, which includes reflectance spectra, first derivative spectra, vegetation indices and absorption or reflectance position features. The grey relational analysis (GRA) was used as a comparison. The results showed that (1) the VIP technology could be used to variable selection and had a strong correlation. (2) Reflectance spectra with the VIP method displayed the best accuracy, with R₂ and RMSE of 0.42 and 0.663mg/g, respectively. (3) Vegetation indices using GRA had higher estimation than VIP method, with R₂ and RMSE of 0.52 and 0.607 mg/g, respectively. (4) The VIP had more superiority and higher accuracy than the GRA in all kinds of hyperspectral features except vegetation indices. Therefore, the VIP technology could be used to the estimation of LCC and had a relatively good accuracy.

As citrus is progressively contributing to horticultural production, wealth and economy of a country, it is necessary to understand the factors impacting citrus production. Gummosis is one of the most serious diseases causing considerable loss of overall citrus production and yield quality. A qualitative and quantitative analysis of citrus leaf biochemical properties are necessary to monitor the crop health, disease /pest stress and production. Total leaf chlorophyll content (C_ab) represents one of the key biochemical factors which contributes in water, carbon, and energy exchange processes. Photosynthesis process in citrus will be disturbed as gummosis disease life cycle progresses. It is important to study C_ab to evaluate the photosynthesis rate and disease stress. In this study the potential of Radiative Transfer (RT) PROSPECT model to retrieve C_ab in citrus orchards was undertaken at different sites. The main goal is to evaluate the relationship between C_ab and gummosis disease stress for citrus at various phenological stages. Inversion of PROSPECT model on measured hyperspectral data is carried out to extract the leaf level parameters influencing the disease. This model was inverted with the ground truth hyperspectral reading. The testing was separately initiated for healthy and infected plant leaves. This can lead to understand the disease stress on citrus leaves. For accuracy, raw spectra are filtered and processed which is an input parameter for Inversion PROSPECT model. Here, retrieved C_ab content was correlated with gummosis disease stress in terms of oozing with R² = 0.6021 and RMSE= 0.481272.

In this paper we report on a flight experiment employing airborne laser bathymetry (ALB) and unmanned aerial vehicle (UAV) based laser scanning (ULS) for capturing very high resolution topography of shallow water areas and the surrounding littoral zone at the pre-alpine Pielach River in Austria. The aim of the research is to assess how information gained from non-bathymetric, ultra-high resolution ULS can support the ALB data. We focus first on the characterization of the water surface of a lowland river and provide validation results using the data of a topographic airborne laser scanning (ALS) sensor and a low flying ULS system. By repeat ULS survey of a the meandering river reach we are able to quantify short-term water level changes due to surface waves in high resolution. Based on a hydrodynamic-numerical (HN) model we assess the accuracy of the water surface derived from a water penetrating ALB sensor. In the second part of the paper we investigate the ability of ALB, ALS, and ULS to describe the complex topography and vegetation structure of the alluvial area. This is carried out by comparing the Digital Terrain Models (DTM) derived from different sensor configurations. Finally we demonstrate the potential of ULS for estimating single tree positions and stem diameters for detailed floodplain roughness characterization in HN simulations. The key findings are: (i) NIR scan data from ALS or ULS provide more precise water level height estimates (no bias, 1σ: 2 cm) compared to ALB (bias: 3 cm, 1σ: 4 cm), (ii) within the studied reach short-term water level dynamics irrelevant for ALB data acquisition considering a 60 cm footprint diameter, and (iii) stem diameters can be estimated based on ULS point clouds but not from ALS and ALB.

Monitoring soil organic matter (SOM) in the cultivated land quantitively and mastering its spatial change are helpful for the adjustment of fertility and sustainable development of agriculture. The hyperspectral technology could be used to detect the targets quickly and nondestructively. The study aimed to develop a universal method to monitor SOM by hyperspectral data. The main idea of the study could be described as follows. Several mathematical transformations were used to improve the expression ability of hyperspectral data. The correlations between SOM and the hyperspectral reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimizing model of monitoring SOM based on the method of multiple linear regressions. The in-situ sample was used to evaluate the accuracy of the model. Results showed that the inversion model with the one differentiation of logarithmic reciprocal transformation ( ^{(1 lg P)'}) of reflectivity could reach highest correlation coefficient (0.643) with lowest RMSE (2.622 g/kg), which was considered as the optimizing inversion model of SOM. It indicated that the one differentiation of logarithmic reciprocal transformation of hyperspectral had good response with SOM of cultivated land. Based on this transformation, the optimizing inversion model of SOM could reach good accuracy with high stability.

Mangrove vegetation is widely employed and studied as it is a unique ecosystem which is able to provide plenty of goods and applications to our country. In this paper, high resolution airborne image data obtained the flight mission on Kuala Sepetang Mangrove Forest Reserve, Perak, Malaysia will be used for mangrove species mapping. Supervised classification using the retrieved surface reflectance will be performed to classify the airborne data using Geomatica 2013 software package. The ground truth data will be used to validate the classification accuracy. High correlation of R²=0.873 was achieved in this study indicate that high resolution airborne data is reliable and suitable used for mangrove species mapping.

Extreme and abnormal weather events, as well as the more gradual meteorological changes associated with climate change, often coincide with not only increased abiotic risks (such as increases in temperature and decreases in precipitation), but also increased biotic risks due to environmental conditions that favor the rapid spread of crop pests and diseases. Durum wheat is by extension the most cultivated cereal in the south and east margins of the Mediterranean Basin. It is of strategic importance for Mediterranean agriculture to develop new varieties of durum wheat with greater production potential, better adaptation to increasingly adverse environmental conditions (drought) and better grain quality. Similarly, maize is the top staple crop for low-income populations in Sub-Saharan Africa and is currently suffering from the appearance of new diseases, which, together with increased abiotic stresses from climate change, are challenging the very sustainability of African societies. Current constraints in field phenotyping remain a major bottleneck for future breeding advances, but RGB-based High-Throughput Phenotyping Platforms (HTPPs) have shown promise for rapidly developing both disease-resistant and weather-resilient crops. RGB cameras have proven costeffective in studies assessing the effect of abiotic stresses, but have yet to be fully exploited to phenotype disease resistance. Recent analyses of durum wheat in Spain have shown RGB vegetation indexes to outperform multispectral indexes such as NDVI consistently in disease and yield prediction. Towards HTTP development for breeding maize disease resistance, some of the same RGB picture vegetation indexes outperformed NDVI (Normalized Difference Vegetation Index), with R² values up to 0.65, compared to 0.56 for NDVI. . Specifically, hue, a*, u*, and Green Area (GA), as produced by FIJI and BreedPix open source software, performed similar to or better than NDVI in predicting yield and disease severity conditions for wheat and maize. Results using UAVs (Unmanned Aerial Vehicles) have produced similar results demonstrating the robust strengths, and limitations, of the more cost-effective RGB picture indexes.

Minas Gerais state, Brazil, has experienced severe water scarcity in some areas, demanding large-scale water balance studies to subsidize water policies. The reflectance bands from the MOD13Q1 MODIS product were used together with gridded agrometeorological data in the state, during the year 2014, later extracting the main agriculture growing regions, North, Northwest and Minas Triangle, for analyzes. Precipitation (Prec) and reference evapotranspiration (ET₀) data from 36 weather stations were interpolated, while for actual evapotranspiration (ET), the SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was used. Two climatic water balance indicators were applied, the Water Balance Ratio (WB_r = Prec/ET) and the Water Balance Difference (WD_d = Prec - ET). The daily net radiation (R_n) was retrieved from surface albedo (α₀), air temperature (T_a) and shortwave atmospheric transmissivity (τ_sw), while the ground heat flux (G) was estimated as a fraction of R_n. For surface moisture, the evapotranspiration ratio (ET_r = ET/ET₀) and the evaporative fraction [E_f = λE/(R_n - G)] were used, with the latent heat flux (λE) obtained by transforming ET into energy units. Analyzing WD_r and WD_d, the most water scarcity critical MODIS 16-day periods, reaching to minimum values lower than 1.0 and -10 mm, respectively, were from the end of April to the middle of October. Higher water availability, detected by these indicators larger than 1.5 and 10 mm, respectively, were from the middle of October to the end of December. The maximums WD_r and WD_d of 7.0 and 158 mm happened from the middle of November to the start of December in the Northwest agricultural growing region. However, according to the ET_r and E_f values, after this period, the soil moisture storage showed a gap, increasing only in the second half of December, when they reached to averages of 0.63. The largest values of these last soil moisture indicators, above 0.70 in May, did not coincided with the period of higher Prec, but with the lowest atmospheric demands in all agricultural growing regions, due to a lapse time between the rainfalls and the variation of soil moisture storage. In addition, irrigation should plays a hole in the large-scale water balance. The indicators tested here can be implemented in the Southeast Brazil, for water policies under the actual conditions of water scarcity and competition among water sectors. It was demonstrated the potential to monitor the water conditions for the 16-day periods on a large scale by combining surface-based weather measurements with MODIS remote sensing products.

Evapotranspiration (ET) is a key parameter in climatological and hydrological models. Moreover, the knowledge of ET at a local scale in agricultural areas may improve the irrigation practices. An operative method using remote sensing techniques would provide spatial and temporal continuous ET data. However, there are limitations in remote sensing data, especially due to the spatial and temporal resolution of the images. For agricultural practices, high spatial and temporal resolution is desired, but nowadays no sensor offers both. The Sentinel-2 sensor has a 5-day revisit cycle and 10-30 m spatial resolution in the visible and near infrared (VNIR) bands. However, no thermal band (TIR) is available, which is the key input in the models for ET estimation based on the surface energy balance. A simple disaggregation procedure is applied in this work to MODIS-Spot images to derive TIR data at 10 m spatial resolution. The disaggregated temperatures are further used as inputs in the STSEB approach (Simplified Two Source Energy Balance) to estimate surface energy fluxes. Ground data in a vineyard and a grass field were used for validation. Average errors of 6%, 21% and 20% were obtained for net radiation, sensible heat flux and evapotranspiration, respectively.

Temporal availability of grapes actual evapotranspiration is an emerging issue since vineyards farms are more and more converted from rainfed to irrigated agricultural systems. The manuscript aims to verify the accuracy of the actual evapotranspiration retrieval coupling a single source energy balance approach and two different temporal upscaling schemes. The first scheme tests the temporal upscaling of the main input variables, namely the NDVI, albedo and LST; the second scheme tests the temporal upscaling of the energy balance output, the actual evapotranspiration. The temporal upscaling schemes were implemented on: i) airborne remote sensing data acquired monthly during a whole irrigation season over a Sicilian vineyard; ii) low resolution MODIS products released daily or weekly; iii) meteorological data acquired by standard gauge stations. Daily MODIS LST products (MOD11A1) were disaggregated using the DisTrad model, 8-days black and white sky albedo products (MCD43A) allowed modeling the total albedo, and 8-days NDVI products (MOD13Q1) were modeled using the Fisher approach. Results were validated both in time and space. The temporal validation was carried out using the actual evapotranspiration measured in situ using data collected by a flux tower through the eddy covariance technique. The spatial validation involved airborne images acquired at different times from June to September 2008. Results aim to test whether the upscaling of the energy balance input or output data performed better.

Four Landsat 8 images were used together with a net of seven agro-meteorological stations for modelling the large-scale radiation and energy balances in the mixed agro-ecosystems inside a semi-arid area composed by irrigated crops and natural vegetation of the Petrolina municipality, Northeast Brazil, along the year 2014. The SAFER algorithm was used to calculate the latent heat flux (λE), net radiation (R_n) was acquired by the Slob equation, ground heat flux (G) was considered as a fraction of R_n and the sensible flux (H) was retrieved by residue in the energy balance equation. For classifying the vegetation into irrigated crops and natural vegetation, the SUREAL algorithm was applied to determine the surface resistance (r_s) and threshold values for r_s were used to characterize the energy fluxes from these types of vegetated surfaces. Clearly one could see higher λE from irrigated crops than from natural vegetation with some situations of heat horizontal advection increasing its values until 23% times larger than R_n, with respective average λE ranges of 5.7 (64% of R_n) to 7.9 (79% of R_n) and 0.4 (4% of R_n) to 4.3 (37% of R_n) MJ m^-2 d^-1. The corresponding H mean values were from 1.8 (18% of R_n) to 3.2 (28% of R_n) and 5.4 (60% of R_n) to 9.2 (94% of R_n) MJ m^-2 d^-1. Average G pixel values ranged from 0.3 to 0.4 MJ m^-2 d^-1, representing 3 and 4% of R_n for natural vegetation and irrigated crops, respectively.

The diversity of habitats in water bodies like rivers is characterised by the status of morphological and hydrological conditions. The good ecological status of water bodies is claimed in the European Water Framework Directive. For the assessment of this status the hydro-morphology is one of the most important supporting components for the classification of the ecological status of water bodies. Therefore the periodical monitoring is a mandatory measure in the scope of the European Water Framework Directive. Regarding the so called overview-method of the LAWA (German Working Group on water issues of the Federal States and the Federal Government represented by the Federal Environment Ministry) the use of remote sensing data and remote sensing methodologies becomes more important. Therefore remote sensing data on different scales (satellite, aerial photographs) as well as other topographic information (ATKIS) and a high resolution DTM are merged into an integrative process of analysis using remote sensing and GIS methodology. The analyses are focused on two parameters. First, a detailed land use classification based on LANDSAT satellite data is performed for whole catchment of a small river. The results show significant increase of urban areas close to the river. The second analyses deals with the determination of river curvature and introduces the use of a quasi-continuously representation of the river. An additional challenge is the chosen study area of a low mountain range river. While large rivers are clear visible in remote sensing data, the usability and transformation of the well-established algorithms and work flows to small rivers need a further substantial research.

The increasing availability of high resolution high repetitively VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be launched in 2015, offers unprecedented opportunity to improve agricultural monitoring. In this study, regional evapotranspiration and crop water consumption were estimated over an irrigated area located in the Kairouan plain (central Tunisia) using the FAO-56 dual crop coefficient water balance model combined with NDVI image time series providing estimates of the actual basal crop coefficient (Kcb) and vegetation fraction cover. Three time series of high-resolution SPOT5 images have been acquired for the 2008-2009, 2011-2012 and 2012-2013 hydrological years. We also benefited from a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment. The SPOT5 images were radiometrically corrected, first, using the SMAC6s Algorithm, and then improved using invariant objects located on the scene.

The method was first calibrated using ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. For other crops for which no calibration data was available, parameters were taken from bibliography. Then, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey for three irrigated perimeters. In a subsequent step, evapotranspiration estimates were obtained using a large aperture scintillometer and were used for an additional validation of the model outputs.

From the beginning of the 1990s, the use of Global Navigation Satellite System (GNSS) reflected signals have been identified as a as source of opportunity for remote sensing applications. In the last two decades, the potential of the technique have been demonstrated for ocean and continental surfaces studies, and several applications have been proposed in the context of high availability of GNSS signals. The GNSS-R technique is generally based on the use of a passive receiver simultaneously acquiring the direct and reflected signals from various GNSS satellites to estimate geophysical parameters from the scattering surface. In the last years, several ground-based [2], [3], airborne [4] and space-borne [5]–[8] experiments have been proposed. The most considered application foreseen for GNSS-R is ocean altimetry for a precise determination of sea-surface heights as well as roughness and wind direction. For continental surfaces, because of direct relationship between surface permittivity and reflected signal, different approaches [6], [9], [10] have been proposed to estimate surface parameters (soil moisture, vegetation biomass, snow). Different observables have been proposed to analyze GNSS signals: the Delay-Doppler Map, the direct and reflected complex waveforms bistatic signal, the ratio between the direct and reflected waveform’s peak time series (Interferometric Complex Field). In this context, the airborne instrument GLORI is proposed to demonstrate contribution of GNSS-R to estimate soil moisture over agricultural soils and biomass of forests or annual cultures. A secondary goal is the feasibility of centimeter-precision altimetry above continental water bodies. The second section describes the characteristics of GLORI instrument. The third section presents airborne campaigns realized over the south West of France and fourth sections discusses the first results. Conclusions are gathered in section 5.

Accurate retrieval of canopy biophysical and leaf biochemical constituents from space observations is critical to diagnosing the functioning and condition of vegetation canopies across spatio-temporal scales. Retrieved vegetation characteristics may serve as important inputs to precision farming applications and as constraints in spatially and temporally distributed model simulations of water and carbon exchange processes. However significant challenges remain in the translation of composite remote sensing signals into useful biochemical, physiological or structural quantities and treatment of confounding factors in spectrum-trait relations. Bands in the red-edge spectrum have particular potential for improving the robustness of retrieved vegetation properties. The development of observationally based vegetation retrieval capacities, effectively constrained by the enhanced information content afforded by bands in the red-edge, is a needed investment towards optimizing the benefit of current and future satellite sensor systems. In this study, a REGularized canopy reFLECtance model (REGFLEC) for joint leaf chlorophyll (Chll) and leaf area index (LAI) retrieval is extended to sensor systems with a band in the red-edge region for the first time. Application to time-series of 5 m resolution multi-spectral RapidEye data is demonstrated over an irrigated agricultural region in central Saudi Arabia, showcasing the value of satellite-derived crop information at this fine scale for precision management. Validation against in-situ measurements in fields of alfalfa, Rhodes grass, carrot and maize indicate improved accuracy of retrieved vegetation properties when exploiting red-edge information in the model inversion process.

In this paper, we describe some results from a study on hyperspectral analysis of coniferous canopy scattering for the purpose of estimating forest biophysical and structural parameters. Georeferenced airborne hyperspectral measurements were taken from a flying helicopter over a coniferous forest dominated by Pinus hartweguii and Abies religiosa within the Federal District Conservation Land in Mexico City. Hyperspectral data was recorded in the optical range from 350 to 2500 nm at 1nm spectral resolution using the FieldSpec 4 (ASD Inc.). Spectral measurements were also carried out in the ground for vegetation and understory components, including leaf, bark, soil and grass. Measurements were then analyzed through a previously developed multiple scattering approximation (MSA) model, which represents above-canopy spectral reflectance through a non-linear combination of pure spectral components (endmembers), as well as through a set of photon recollision probabilities and interceptance fractions. In this paper we provide an expression for the canopy absorptance as the basis for estimating the components of canopy radiation budget using the MSA model. Furthermore, since MSA does not prescribe a priori the endmembers to incorporate in the model, a multiple endmember selection method (MESMSA) was developed and tested. Photon recollision probabilities and interceptance fractions were estimated by fitting the model to airborne spectral reflectance and selected endmembers where then used to estimate the canopy radiation budget at each measured location.

We plan to estimate gross primary production (GPP) using the SGLI sensor on-board the GCOM-C1 satellite after it is launched in 2017 by the Japan Aerospace Exploration Agency, as we have developed a GPP estimation algorithm that uses SGLI sensor data. The characteristics of this GPP estimation method correspond to photosynthesis. The rate of plant photosynthesis depends on the plant's photosynthesis capacity and the degree to which photosynthesis is suppressed. The photosynthesis capacity depends on the chlorophyll content of leaves, which is a plant physiological parameter, and the degree of suppression of photosynthesis depends on weather conditions. The framework of the estimation method to determine the light-response curve parameters was developed using ux and satellite data in a previous study^[1]. We estimated one of the light-response curve parameters based on the linear relationship between GPP capacity at 2000 (μmolm^-2_s^-1) of photosynthetically active radiation and a chlorophyll index (CI_green ^[2;3] ). The relationship was determined for seven plant functional types. Decreases in the photosynthetic rate are controlled by stomatal opening and closing. Leaf stomatal conductance is maximal during the morning and decreases in the afternoon. We focused on daily changes in leaf stomatal conductance. We used open shrub flux data and MODIS reflectance data to develop an algorithm for a canopy. We first evaluated the daily changes in GPP capacity estimated from CI_green and photosynthesis active radiation using light response curves, and GPP observed during a flux experiment. Next, we estimated the canopy conductance using flux data and a big-leaf model using the Penman-Monteith equation^[4]. We estimated GPP by multiplying GPP capacity by the normalized canopy conductance at 10:30, the time of satellite observations. The results showed that the estimated daily change in GPP was almost the same as the observed GPP. From this result, we defined a normalized canopy conductance index based on the satellite value taken at 10:30 as the canopy conductance factor. The method of scaling-up the canopy conductance index and the availability of data from the global observing satellite project are discussed herein.

A radiative transfer model (RTM) to calculate the snow brightness temperatures (Tb) is a critical element in terrestrial snow parameter retrieval from microwave remote sensing observations. The RTM simulates the Tb based on a layered snow by solving a set of microwave radiative transfer equations. Even with the same snow physical inputs to drive the RTM, currently existing models such as Microwave Emission Model of Layered Snowpacks (MEMLS), Dense Media Radiative Transfer (DMRT-QMS), and Helsinki University of Technology (HUT) models produce different Tb responses. To backwardly invert snow physical properties from the Tb, differences from RTMs are first to be quantitatively explained. To this end, this initial investigation evaluates the sources of perturbations in these RTMs, and reveals the equations where the variations are made among the three models. Modelling experiments are conducted by providing the same but gradual changes in snow physical inputs such as snow grain size, and snow density to the 3 RTMs. Simulations are conducted with the frequencies consistent with the Advanced Microwave Scanning Radiometer- E (AMSR-E) at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz. For realistic simulations, the 3 RTMs are simultaneously driven by the same snow physics model with the meteorological forcing datasets and are validated against the snow insitu samplings from the CLPX (Cold Land Processes Field Experiment) 2002-2003, and NoSREx (Nordic Snow Radar Experiment) 2009-2010.

The distribution and change of snow coverage are sensitive factors of climate change. In northeast part of China, farmlands are still covered with snow in spring. Since sowing activity can only be done when the snow melted, fields snow coverage monitoring provides reference for the determination of sowing date. Because of the restriction of the sensors and application requirements, current researches on remote sensing of snow focus more on the study of musicale and large scale, rather than the study of small scale, and especially research on snow melting period is rarely reported.HJ-1A/B satellites are parts of little satellite constellation, focusing on environment and disaster monitoring and meteorological forecast. Compared to other data sources, HJ-1A/B satellites both have comparatively higher temporal and spatial resolution and are more conducive to monitor the variations of melting snow coverage at small watershed. This paper was based on HJ-1A/1B data, taking Hongxing farm of Bei’an, Heilongjiang Province, China as the study area. In this paper, we exploited the methods for extraction of snow cover information on farmland in two cases, both HJ-1A/1B CCD with HJ-1B IRS data and just HJ-1A/1B CCD data. The reason we chose the two cases is that, the two optical satellites HJ-1A/B are capable of providing a whole territory coverage period in visible light spectrum in two days, infrared spectrum in four days. So sometimes we can only obtain CCD image. In this case, the method of normalized snow index cannot be used to extract snow coverage information. Using HJ-1A/1B CCD with HJ-1B IRS data, combined with the theory of snow remote sensing monitoring, this paper analyzed spectral response characteristics of HJ-1A/1B satellites data, then the widely used Normalized Difference Snow Index(NDSI) and S3 Index were quoted to the HJ-1A/1B satellites data. The NDSI uses reflectance values of Red and SWIR spectral bands of HJ-1B, and S3 index uses reflectance values of NIR, Red and SWIR spectral bands. With multi-temporal HJ satellite data, the optimal threshold of normalized snow index was determined to divide the farmland into snow covering area, melting snow area and non-snow area. The results are quite similar to each other and of high accuracy, and the melting snow coverage can be well extracted by two types of normalized snow index. When we can only obtain CCD image, we use supervised classification method to extract melting snow coverage. With this method, the accuracy of fields snow coverage extraction is slightly lower than that using normalized snow index methods mentioned above. And in mountain area, the snow coverage area is slightly larger than that is extracted by normalized snow index methods, because the shadows make the color of snow in the valley darker, the supervised classification method divides it into non-snow coverage area, while the normalized snow index method well weakened the effect of shadow. This study shows that extraction accuracy in both cases is assessed, and both of them can meet the needs of practical applications. HJ-1A/1B satellites are conducive to monitor the variations of melting snow coverage over farmland, and they can provide reference for the determination of sowing date.

The goal of this paper is to analyze the sensitivity of X-band SAR (TerraSAR-X) signals as a function of different physical bare soil parameters (soil moisture, soil roughness), and to demonstrate that it is possible to estimate of both soil moisture and texture from the same experimental campaign, using a single radar signal configuration (one incidence angle, one polarization). Firstly, we analyzed statistically the relationships between X-band SAR (TerraSAR-X) backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 36°, over a semi-arid site in Tunisia (North Africa). Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter. Then, we proposed to retrieve of both soil moisture and texture using these multi-temporal X-band SAR images. Our approach is based on the change detection method and combines the seven radar images with different continuous thetaprobe measurements. To estimate soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our approaches are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced.

Finally, by considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved.

Due to the lack of observation data which match the pixels size of satellite remote sensing data, the inversion accuracy of satellite inversion products in Tibet plateau is lack of an effective verification. Hence, the in situ observations are required to support their calibration and validation. For this purpose, a multi-level and multi-scale soil moisture and temperature regular automatic monitoring network (MS-SMTRMN) was established on Qiangtang grassland of northern Tibet area to support multiple satellite remote sensing application, climate modeling or assimilation, and land surface process studies. In this paper, MS-SMTRMN aim at multi-satellite remote sensing application was detailed and the observation data with quality control were used to the verification for multiple satellite retrieval products (FY3, AMSR2 and SMOS). This study will contribute to the understanding of the quality of products and lays the foundation for the satellite data assimilation results in the TP area.

Recently, Tibetan plateau (TP) has become a hot area of climate change research. And Land Surface Temperature (LST) is one of key factors in the research. In order to get a long time-series, high spatial resolution and high accuracy LST dataset, we carried out analysis of influence essential factor of LST retrieval from AVHRR oriented Tibetan plateau area. First, choose MODTRAN5.2 to simulate the impact of land surface, atmospheric, geometric factors on bright temperatures of channel 4 and channel 5 for special features of TP using stand atmospheric models. Result showed that emissivity, boundary temperature, water vapor amount and view zenith angle were the principal elements of bright temperature. Second an improved algorithm from Wanz-Dozier split window model was established considering these factors. At last, differences between LST retrieval result considering different factors were given.

This study focused on the wheat grain protein content (GPC) estimation based on wheat canopy chlorophyll parameters which acquired by hand-held instrument, Multiplex 3. Nine fluorescence spectral indices from Multiplex sensor were used in this study. The wheat GPC estimation experiment was conducted in 2012 at the National Experiment Station for Precision Agriculture in Changping district, Beijing. A square with area of 1.1 ha was selected and divided to 110 small plots by 10×10m in this study. In each plot, four 1-m2 area distributed in the square were selected for canopy fluorescence spectral measurements, physiological and biochemical analyses. Measurements were performed five times at wheat raising, jointing, heading stage, milking and ripening stage, respectively. The wheat plant samples for each plot were then collected after the measurement and sent to Lab for leaf N concentration (LNC) and canopy nitrogen density (CND) analyzed. GPC sampling for each plot was collected manually during the harvested season. Then, statistical analysis were performed to detect the correlation between fluorescence spectral indices and wheat CND for each growth stage, as well as GPC. The results indicate that two Nitrogen Balance Indices, NBI_G and NBI_R were more sensitive to wheat GPC than other fluorescence spectral indices at milking stage and ripening stage. Five linear regression models with GPC and fluorescence indices at different winter wheat growth stages were then established. The R² of GPC estimated model increased form 0.312 at raising stage to 0.686 at ripening stage. The study reveals that canopy-level fluorescence spectral parameters were better indicators for the wheat group activity and could be demonstrated to be good indicators for winter wheat GPC estimation.

Solar-induced chlorophyll fluorescence is closely related to photosynthesis and can serve as an indicator of plant status. Several methods have been proposed to retrieve fluorescence signal (Fs) either at specific spectral bands or within the whole fluorescence emission region. In this study, we investigated the precision of the fluorescence signal obtained through these methods under various sensor spectral characteristics. Simulated datasets generated by the SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model with known ‘true’ Fs as well as an experimental dataset are exploited to investigate four commonly used Fs retrieval methods, namely the original Fraunhofer Line Discriminator method (FLD), the 3 bands FLD (3FLD), the improved FLD (iFLD), and the Spectral Fitting Methods (SFMs). Fluorescence Spectrum Reconstruction (FSR) method is also investigated using simulated datasets. The sensor characteristics of spectral resolution (SR) and signal-to-noise ratio (SNR) are taken into account. According to the results, finer SR and SNR both lead to better accuracy. Lowest precision is obtained for the FLD method with strong overestimation. Some improvements are made by the 3FLD method, but it still tends to overestimate. Generally, the iFLD method and the SFMs provide better accuracy. As to FSR, the shape and magnitude of reconstructed Fs are generally consistent with the ‘true’ Fs distributions when fine SR is exploited. With coarser SR, however, though R² of the retrieved Fs may be high, large bias is likely to be obtained as well.

In crop leaves the ratio of carbon to nitrogen (C/N), defined as ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is a good indicator that can synthetically evaluate the balance of carbon and nitrogen, nutrient status in crop plants. Hence it is very important how to monitor changes of leaf C/N effectively and in real time for nutrient diagnosis and growing management of crops in fields. In consideration of the close relationships between chlorophyll, nitrogen (N) and C/N, some typical indices aimed at N estimation were tested to estimate leaf C/N in winter wheat as well as several indices aimed chlorophyll evaluation. The multi-temporal hyperspectral data from the flag-leaf, anthesis, filling, and milk-ripe stages were obtained to calculate these selected spectral indices for evaluating C/N in winter wheat. The results showed that some tested indices such as MCARI/OSAVI2, MTCI and Rep-Le had the better performance of estimating C/N. In addition, GRA (gray relational analysis) and Branch-and-Bound method were also used along with spectral indices sensitive to C/N for improving the accuracy of monitoring C/N in winter wheat, and obtained the better results with R2 of 0.74, RMSE of 0.991. It indicates that monitoring of leaf C/N in winter wheat with hyperspectral reflectance measurements appears very potential.

Saltmarsh is one of the important communities of wetlands, however, due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of spectral libraries of saltmarsh species is essential to monitor this EEC. Hyperspectral remote sensing, can explore the area of wetland monitoring and mapping. The benefits of Hyperion data to wetland monitoring have been studied at Hunter Wetland Park, NSW, Australia. After exclusion of bad bands from the original data, an atmospheric correction model was applied to minimize atmospheric effect and to retrieve apparent surface reflectance for different land cover. Large data dimensionality was reduced by Forward Minimum Noise Fraction (MNF) algorithm. It was found that first 32 MNF band contains more than 80% information of the image. Pixel Purity Index (PPI) algorithm worked properly to extract pure pixel for water, builtup area and three vegetation Casuarina sp., Phragmitis sp. and green grass. The result showed it was challenging to extract extreme pure pixel for Sporobolus and Sarcocornia from the data due to coarse resolution (30 m) and small patch size (<3 m) of those vegetation on the ground . Spectral Angle Mapper, classified the image into five classes: Casuarina, Saltmarsh (Phragmitis), Green grass, Water and Builtup area with 43.55 % accuracy. This classification also failed to classify Sporobolus as a distinct group due to the same reason. A high spatial resolution airborne hyperspectral data and a new study site with a bigger patch of Sporobolus and Sarcocornia is proposed to overcome the issue.

Monitoring of water environment and ecosystem, detecting water contaminants and understanding water quality parameters are most important tasks in water management and protection of whole aquatic environment. Detection of biological contaminants play a very important role in preserving human health and water management. To obtain accurate and precise results of determination of the level of biological contamination and to distinguish its type it is necessary to determine precisely spectral reflectance coefficients of several water biological pollutants with inter alia spectroradiometer. This paper presents a methodology and preliminary results of acquisition of spectral reflectance coefficients with different reference panels (e.g. with 5%, 20%, 50%, 80% and 96% of reflectivity) of several biological pollutants. The authors’ main task was to measure spectral reflectance coefficients of different biological water pollutants with several reference panels and to select optimal reference standard, which would allow for distinguish different types of several biological contaminants. Moreover it was necessary to indicate the spectral range in which it is possible to discriminate investigated samples of biological contaminants. By conducting many series of measurements of several samples of different types of biological pollutants, authors had concluded how the reflectivity of reference panel influences the accuracy of acquisition of spectral reflectance coefficients. This research was crucial in order to be able to distinguish several types of biological pollutants and to determine the useful spectral range for detection of different kinds of biological contaminants with multispectral and hyperspectral imagery.

FORMOSAT-2, launched in May 2004, is a Taiwanese satellite developed by the National Space Organization (NSPO) of Taiwan. The Remote Sensing Instrument (RSI) is a high spatial- resolution optical sensor onboard FORMOSAT-2 with a 2 m spatial resolution in the panchromatic (PAN) band and a 8 m spatial resolution in four multispectral (MS) bands from the visible to near-infrared region. The RSI images acquired during the daytime can be used for land cover/use studies, natural and forestry resources, disaster prevention and rescue works. The main objectives of this work were to investigate the application of FORMOSAT-2 data in order to: (1) identify beach patterns; (2) correctly extract a sand spit boundary. Different pixel-based and object-based classification algorithms were applied to four FORMOSAT-2 scenes and the results were compared with the results already obtained in previous works. Analyzing the results obtained, is possible to conclude that the FORMOSAT-2 data are adequate to the correct identification of beach patterns and to an accurately extraction of the sand spit boundary (Douro river estuary, Porto, Portugal). The results obtained were compared with the results already achieved with IKONOS-2 images. In conclusion, this research has demonstrated that the FORMOSAT-2 data and image processing techniques employed are an effective methodology to identify beach patterns and to correctly extract sand spit boundaries. In the future more FORMOSAT-2 images will be processed and will be consider the use of pan sharped images and data mining algorithms.

Wetlands are very valuable areas because they provide a wide range of ecosystems services therefore modeling of wetland areas is very relevant, however, the most widely used hydrological models were developed in the 90s and usually are not adjusted to simulate wetland conditions. In case of wetlands including interception storage into the model’s calculation is even more challenging, because literature data hardly exists. This study includes the computation of interception storage capacity based on Landsat 7 image and ground truthing measurements conducted in the Biebrza Valley, Poland. The method was based on collecting and weighing dry, wet and fully saturated samples of sedges. During the experiments measurements of fresh/dry biomass and leaf area index (LAI) were performed. The research was repeated three times during the same season (May, June and July 2013) to observe temporal variability of parameters. Ground truthing measurements were used for the validating estimation of parameters derived from images acquired in a similar period as the measurements campaigns. The use of remote sensing has as major advantage of being able to obtain an area covering spatially and temporally distributed estimate of the interception storage capacity.

Results from this study proved that interception capacity of wetlands vegetation is changing considerably during the vegetation season (temporal variability) and reaches its maximum value when plants are fully developed. Different areas depending on existing plants species are characterized with different values of interception capacity (spatial variability). This research frames within the INTREV and HiWET projects, funded respectively by National Science Centre (NCN) in Poland and BELSPO STEREO III.

Structure of the floodplain, especially its topography and vegetation, influences the overland flow and dynamics of floods which are key factors shaping ecosystems in surface water-fed wetlands. Therefore elaboration of the digital terrain model (DTM) of a high spatial accuracy is crucial in hydrodynamic flow modelling in river valleys. In this study the research was conducted in the unique Central European complex of fens and marshes - the Lower Biebrza river valley. The area is represented mainly by peat ecosystems which according to EU Water Framework Directive (WFD) are called “water-dependent ecosystems”. Development of accurate DTM in these areas which are overgrown by dense wetland vegetation consisting of alder forest, willow shrubs, reed, sedges and grass is very difficult, therefore to represent terrain in high accuracy the airborne laser scanning data (ALS) with scanning density of 4 points/m² was used and the correction of the “vegetation effect” on DTM was executed. This correction was performed utilizing remotely sensed images, topographical survey using the Real Time Kinematic positioning and vegetation height measurements. In order to classify different types of vegetation within research area the object based image analysis (OBIA) was used. OBIA allowed partitioning remotely sensed imagery into meaningful image-objects, and assessing their characteristics through spatial and spectral scale. The final maps of vegetation patches that include attributes of vegetation height and vegetation spectral properties, utilized both the laser scanning data and the vegetation indices developed on the basis of airborne and satellite imagery. This data was used in process of segmentation, attribution and classification. Several different vegetation indices were tested to distinguish different types of vegetation in wetland area. The OBIA classification allowed correction of the “vegetation effect” on DTM. The final digital terrain model was compared and examined within distinguished land cover classes (formed mainly by natural vegetation of the river valley) with archival height models developed through interpolation of ground points measured with GPS RTK and also with elevation models from the ASTER-GDEM and SRTM programs. The research presented in this paper allowed improving quality of hydrodynamic modelling in the surface water-fed wetlands protected within Biebrza National Park. Additionally, the comparison with other digital terrain models allowed to demonstrate the importance of accurate topography products in such modelling. The ALS data also significantly improved the accuracy and actuality of the river Biebrza course, its tributaries and location of numerous oxbows typical in this part of the river valley in comparison to previously available data. This type of data also helped to refine the river valley cross-sections, designate river banks and to develop the slope map of the research area.

This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or smoke pixels, whose presence in hyperspectral images can often undermine the performance of traditional classification algorithms. In order to improve neural network performance, future activities will include also the exploiting of hyperspectral images in the shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, which include significant emitted radiance from fire.

Prosopis juliflora is a fast growing tree species originating from South and Central America with a high invasion potential in semi-arid areas around the globe. It was introduced to East Africa for the stabilization of dune systems and for providing fuel wood after prolonged droughts and deforestation in the 1970s and 1980s. In many dry lands in East Africa the species has expanded rapidly and has become challenging to control. The species generally starts its colonization on deep soils with high water availability while in later stages or on poorer soils, its thorny thickets expand into drier grasslands and rangelands. Abandoned or low input farmland is also highly susceptible for invasion as P. juliflora has competitive advantages to native species and is extremely drought tolerant.

In this work we describe a rapid approach to detect and map P. juliflora invasion at country level for the whole of Somaliland. Field observations were used to delineate training sites for a supervised classification of Landsat 8 imagery collected during the driest period of the year (i.e., from late February to early April). The choice of such a period allowed to maximise the spectral differences between P. juliflora and other species present in the area, as P. juliflora tends to maintain a higher vigour and canopy water content than native vegetation, when exposed to water stress.

The results of our classification map the current status of invasion of Prosopis in Somaliland showing where the plant is invading natural vegetation or agricultural areas. These results have been verified for two spatial subsets of the whole study area with very high resolution (VHR) imagery, proving that Landsat 8 imagery is highly adequate to map P. juliflora. The produced map represents a baseline for understanding spatial distribution of P. juliflora across Somaliland but also for change detection and monitoring of long term dynamics in support to P. juliflora management and control activities.

The present work is devoted to the measurement of the dielectric properties of mosses and lichens in the frequency range from 500 MHz to 18 GHz. Subjects of this research were three species of march vegetation – moss (Dicranum polysetum Michx), groundcedar (Diphasiastrum complanatum (L.) Holub) and lichen (Cladonia stellaris). Samples of vegetation were collected in Tomsk region, Western Siberia, Russia. Complex dielectric permittivity was measured in coaxial section by Agilent Technologies vector network analyzer E8363B. Green samples was measured for some moisture contents from 100% to 3–5 % during a natural drying. The measurements were performed at room temperature, which remained within 21 ÷ 23 ° C.

The frequency dependence of the dielectric constant for the three species of marsh vegetation differ markedly. Different parts of the complex permittivity dependency on moisture were fitted by line for all frequency points. Two break point were observed corresponding to the transition of water in the vegetation in various phase states. The complex permittivity spectra of water in the vegetation allow determining the most likely corresponding dielectric model of water in the vegetation by the method of hypothesis testing. It is the Debye’s model. Parameters of Debye’s model were obtained by numerical methods for all of three states of water. This enables to calculate the dielectric constant of water at any frequency range from 500 MHz to 18 GHz and to find the parameters of the dielectric model of the vegetation.

Satellite radar altimetry, initially designed for studying ocean surface topography, demonstrated a strong potential for the continuous monitoring of ice sheets and land surfaces over the last 25 years. If radar altimetry is mostly used for its capacity to determine surface height, the backscattering coefficients provide information on the surface properties. Spatio-temporal variations of radar altimetry backscattering over land and ice sheets were related to the nature of the surface and its changes against time. This study presents the results of an along-track analysis of radar altimetry echoes over land, Antarctica and Greenland at Ku and S bands from June 2002 to July 2003 using the ERS-2 and ENVISAT datasets on their nominal orbit during the tandem phase of the two missions. Temporal average and deviations are presented at global scale for ascending and descending tracks for the two missions.

In semi-arid areas, an operational grain yield forecasting system, which could help decision-makers to plan annual imports, is needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Remote sensing has demonstrated its strong potential for the monitoring of the vegetation's dynamics and temporal variations. Through the use of a rich database, acquired over a period of two years for more than 60 test fields, and from 20 optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two approaches to estimate the dynamics and yields of cereals in the context of semi-arid, low productivity regions in North Africa.

The first approach is based on the application of the semi-empirical growth model SAFY “Simple Algorithm For Yield estimation”, developed to simulate the dynamics of the leaf area index and the grain yield, at the field scale. The model is able to reproduce the time evolution of the LAI of all fields. However, the yields are under-estimated. Therefore, we developed a new approach to improve the SAFY model. The grain yield is function of LAI area in the growth period between 25 March and 5 April. This approach is robust, the measured and estimated grain yield are well correlated. Finally, this model is used in combination with remotely sensed LAI measurements to estimate yield for the entire studied site.

This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.

The southern region is responsible for 70% of rice production in Brazil. In this study, rice areas of Rio Grande do Sul were selected, using the land use classification, scale 1: 100,000, provided by Brazilian Institute of Geography and Statistics (IBGE). MODIS Images were used and meteorological data, available by National Institute of Meteorology (INMET). The period of analysis was crop season 2011/2012, October to March. To obtain evapotranspiration was applied agrometeorological-spectral model SAFER (Simple Algorithm For Retrieving Evapotranspiration). From the analysis of the results, on planting and cultivation period , the average evapotranspiration (ET) daily was 1.93 ± 0.96 mm.day-1. In the vegetative development period of rice, the daily ET has achieved 4.94 mm.day-1, with average value 2,31± 0.97 mm.day-1. In the period of harvest, evapotranspiration daily average was 1.84 ± 0.80 mm.day-1. From results obtained, the estimation of evapotranspiration from satellite images may assist in monitoring the culture during the cycle, assisting in estimates of water productivity and crop yield.

The monitoring of rice fields can improve productivity by helping farmers throughout the rice cultivation cycle, on various issues: when to harvest, when to treat the crops against disease, when to increase the water level, how to share observations and decisions made in a collaborative way, etc. In this paper, we propose an architecture to monitor a rice field by a wireless sensor network. Our architecture is based on static sensor nodes forming a disconnected network, and mobile nodes communicating with the sensor nodes in a delay-tolerant manner. The data collected by the static sensor nodes are transmitted to mobile nodes, which in turn transmit them to a gateway, connected to a database, for further analysis. We focus on the related architecture, as well as on the energy-efficient protocols intended to perform the data collection.

Morocco is a primarily arid to semi-arid country. These climatic conditions make irrigation an imperative and inevitable technique. Especially, agriculture has a paramount importance for the national economy. Retrieving of crops and their location as well as their spatial extent is useful information for agricultural planning and better management of irrigation water resource. Remote sensing technology was often used in management and agricultural research. Indeed, it's allows crops extraction and mapping based on phenological characteristics, as well as yield estimation. The study area of this work is the Tadla irrigated perimeter which is characterized by heterogeneous areas and extremely small size fields. Our principal objectives are: (1) the delimitation of the major crops for a good water management, (2) the insulation of sugar beet parcels for modeling its yields. To achieve the traced goals, we have used Landsat-8 OLI (Operational Land Imager) data pan-sharpened to 15 m. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifications were applied to the Normalized Difference Vegetation Index (NDVI) time-series of 10 periods. Classifications were calculated for a site of more than 124000 ha. This site was divided into two parts: the first part for selecting, training datasets and the second one for validating the classification results. The SVM and SAM methods classified the principal crops with overall accuracies of 85.27% and 57.17% respectively, and kappa coefficient of 80% and 43% respectively. The study showed the potential of using time-series OLI NDVI data for mapping different crops in irrigated, heterogeneous and undersized parcels in arid and semi-arid environment.

Agro-Ecological Infrastructures (AEIs) include many semi-natural habitats (hedgerows, grass strips, grasslands, thickets…) and play a key role in biodiversity preservation, water quality and erosion control. Indirect biodiversity indicators based on AEISs are used in many national and European public policies to analyze ecological processes. The identification of these landscape features is difficult and expensive and limits their use. Remote sensing has a great potential to solve this problem. In this study, we propose an operational tool for the identification and characterization of AEISs. The method is based on segmentation, contextual classification and fusion of temporal classifications. Experiments were carried out on various temporal and spatial resolution satellite data (20-m, 10-m, 5-m, 2.5-m, 50-cm), on three French regions southwest landscape (hilly, plain, wooded, cultivated), north (open-field) and Brittany (farmland closed by hedges).

The results give a good idea of the potential of remote sensing image processing methods to map fine agro-ecological objects. At 20-m spatial resolution, only larger hedgerows and riparian forests are apparent. Classification results show that 10-m resolution is well suited for agricultural and AEIs applications, most hedges, forest edges, thickets can be detected. Results highlight the multi-temporal data importance. The future Sentinel satellites with a very high temporal resolution and a 10-m spatial resolution should be an answer to AEIs detection. 2.50-m resolution is more precise with more details. But treatments are more complicated. At 50-cm resolution, accuracy level of details is even higher; this amplifies the difficulties previously reported. The results obtained allow calculation of statistics and metrics describing landscape structures.

Soybean producers suffer from caterpillar damage in many areas of the world. Estimated average economic losses are annually 500 million USD in Brazil, Argentina, Paraguay and Uruguay. Designing efficient pest control management using selective and targeted pesticide applications is extremely important both from economic and environmental perspectives. With that in mind, we conducted a research program during the 2013-2014 and 2014-2015 planting seasons in a 4,000 ha soybean farm, seeking to achieve early pest detection. Nowadays pest presence is evaluated using manual, labor-intensive counting methods based on sampling strategies which are time consuming and imprecise. The experiment was conducted as follows. Using manual counting methods as ground-truth, a spectrometer capturing reflectance from 400 to 1100 nm was used to measure the reflectance of soy plants.

A first conclusion, resulting from measuring the spectral response at leaves level, showed that stress was a property of plants since different leaves with different levels of damage yielded the same spectral response. Then, to assess the applicability of unsupervised classification of plants as healthy, biotic-stressed or abiotic-stressed, feature extraction and selection from leaves spectral signatures, combined with a Supported Vector Machine classifier was designed. Optimization of SVM parameters using grid search with cross-validation, along with classification evaluation by ten-folds cross-validation showed a correct classification rate of 95%, consistently on both seasons. Controlled experiments using cages with different numbers of caterpillars--including caterpillar-free plants--were also conducted to evaluate consistency in trends of the spectral response as well as the extracted features.

Carbon dioxide (CO₂) is an inodorous and transparent gas, and naturally originates in our atmosphere. Due to its optical characteristics, CO₂ is the most important greenhouse gas and play a key role in climate change due to an effective thermal infrared (IR) radiation absorber. Satellite observations of atmospheric carbon dioxide (CO₂) can significantly improve our knowledge about the sources and sinks of CO₂. The remote sensing satellite, namely Greenhouse Gases Observing Satellite (GOSAT) was employed to investigate the spatial and variations of CO₂ column-averaged dry airmole fractions, denoted XCO₂ over Peninsular Malaysia from January 2013 to December 2013. The analysis of CO₂ in the study area shows the significant differences between northeast monsoon (NEM) and the southwest monsoon (SWM). During NEM season, cold air outbreaks from Siberia spreads to equatorial region in the form of north-easterly cold surge winds and associated with a low-level anticyclone over Southeast Asia. Inversely, air masses from the southwest contribute to long–range air pollution due to transportation of atmospheric CO₂ by wind is associated with biomass burning in Sumatra, Indonesia. The GOSAT data and the Satellite measurements are able to measure the increase of the atmosphere CO₂ values over different regions.

Climate change affects forest both directly and indirectly through disturbances, that are a natural and integral part of forest ecosystems, and climate change can alter these natural interactions. Forest vegetation characteristics, including land cover and phenology, affect processes such as water cycle, absorption and re-emission of solar radiation, momentum transfer, carbon cycle, and latent and sensible heat fluxes. The climate system responds in complex ways to changes in forcing that may be natural or human-induced. Drastic climate change over the last decades has greatly increased the importance of forest land cover changes monitoring through time-series satellite data. Satellite based derived biophysical parameters for assessment of climate impacts on forest vegetation have to meet particularly high quality requirements. Forest vegetation and climate interact through a series of complex feedbacks, which are not very well understood. Satellite remote sensing is suited tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and GPP (Gross Primary Production), which are key biophysical variables for studying land surface processes and surface-atmosphere interactions for forested areas. The aim of this paper was to investigate their pattern dynamics due to the impact of climate variations on a periurban forest Branesti-Cernica, placed to the North-Eastern part of Bucharest city, Romania. The forest vegetation analysis was based on derived biogeophysical parameters from time-series satellite remote sensing MODIS Terra/Aqua and NOAA AVHRR data and in-situ monitoring ground data (as air temperature, aerosols distribution, relative humidity, etc.) over 2002–2014 period.

The canopy water content of wetland vegetation is an important measuring index of the health status of wetland ecosystem. This article takes the Honghe national wetland nature reserve as study area. We focus on innovative approaches for retrieving canopy water content from optical remote sensing data-multispectral and hyperspectral data. Spectral features, such as narrow band spectral indices, hyperspectral vegetation indices in early literatures, absorption features and vegetation indices extracted from TM image were used to estimate the canopy water content. For narrow band spectral indices, Normalized difference vegetation index comprised of 970 nm and at 900 nm had a highest correlation with canopy water content. For general hyperspectral vegetation indices in early literatures, WI had a highest correlation with canopy water content. For absorption features, the absorption deepness at 1200nm had a highest correlation with canopy water content. In addition, NDII (band5) extracted from TM images could be used for estimating canopy water content. Finally, a distribution map of canopy water content in HNNR was generated.