Sentinel-2 offers the capabilities to observe Arctic sea ice features with high spatial and temporal resolution. Arctic sea ice drift, however, exacerbates observing the temporal evolution of floes by means of time series analysis. We therefore developed a novel rotation-invariant ice floe descriptor based on ice floe geometry and an image-processing workflow consisting of three main steps: (i) ice floe extraction, (ii) ice floe description and (iii) ice floe matching. We tested the methodology on Sentinel-2A images from 10 June and 3 July, 2017, and selected five floes present in both images. We further added ten “false samples” in the second image. All floes from the first image were correctly identified and matched with the floes from the second image. The methodology enables the identification of individual ice floes and determination of their relative rotation from multiple Sentinel-2 images.
Agricultural monitoring is of growing importance due to an increasing world population, slowing growth of agricultural output and concerns regarding food security. Remote sensing and dynamic crop modeling are powerful tools for yield prediction and frequently applied in literature. A large question arising in this context is the assimilation of remote sensing data into the model process. We present a novel technique employing Particle Swarm Optimization in an updating scheme flexibly incorporating different sources of uncertainty in both the model simulation and remote sensing observations. We tested the technique with the AquaCrop-OS model for winter wheat yield prediction by updating canopy cover obtained from remote sensing datasets. Preliminary results showed that the new method can outperform both a simple replacement update and a Kalman filter approach. It succeeded in removing the bias from field-level yield predictions and reducing the RMSE from 1.32 t/ha to 0.89 t/ha.
Uncertainties of aerosol parameters are the limiting factor for atmospheric correction over inland and coastal waters. For validating remote sensing products from these optically complex and spatially inhomogeneous waters the spatial resolution of automated sun photometer networks like AERONET is too coarse and additional measurements on the test site are required. We have developed a method which allows the derivation of aerosol parameters from measurements with any spectrometer with suitable spectral range and resolution. This method uses a pair of downwelling irradiance and sky radiance measurements for the extraction of the turbidity coefficient and aerosol Ångström exponent. The data can be acquired fast and reliable at almost any place during a wide range of weather conditions. A comparison to aerosol parameters measured with a Cimel sun photometer provided by AERONET shows a reasonable agreement for the Ångström exponent. The turbidity coefficient did not agree well with AERONET values due to fit ambiguities, indicating that future research should focus on methods to handle parameter correlations within the underlying model.
Numerous approaches characterising the radiation field of a water column have been developed and correction attempts for remote sensing data have been applied successfully. Various algorithms describe the complex interaction of biophysical parameters with down- and upwelling radiation in a water body and form the basis for water column correction. Parameters such as varying bottom reflectances and bathymetry aggravate an accurate parameterization of water column correction models. Applying these models, special interest lies in their sensitivity to both quality and accuracy of model input parameters. In this paper we discuss the sensitivity of the water column correction model MIP2 to bio-physical parameters, i.e. suspended matters (SM) and chlorophyll (CHL), in case 2 waters.
In August 2010, hyperspectral AISAeagle data have been acquired; in-situ measurements were conducted concurrently to the airborne campaign. The study was conducted at the rocky shores of the island Helgoland (North Sea, Germany). The study area is characterised by a heterogeneous water body resulting in varying and spatially uncorrelated concentrations of SM and CHL, which aggravate an accurate water column correction.
During analysis, special focus is set on areas with varying water characteristics such as vegetated bedrock, shallow sandy spots and deep water areas. Water column correction is performed using a sub-module of MIP, i.e. WATCOR. Reflectance deviation results show that variations of SM concentrations have a stronger influence than variations of CHL within the water column correction. Whereas, the shallow sandy spots reveal the highest sensitivity at constituent concentration variation followed by the deep water and the vegetated bedrock areas.
Analysis of coastal marine algae communities enables an estimation of the state of coastal marine environments and
provides evidence for environmental changes. Hyperspectral remote sensing provides a tool for mapping macroalgal
habitats if the algal communities are spectrally resolvable. We tested the performance of a new approach for determining the distribution of macroalgae communities in the rocky intertidal zone of Helgoland (Germany) using airborne hyperspectral (AISAeagle) data. This new approach calculates the slopes in wavelength regions between specific pigment absorption features and does not rely on absolute reflectance values. The first order derivatives of these wavelength regions form slope bands, which are then classified using a k-Means approach. The new derivatives approach proved to be a time effective possibility for identifying the dominating macroalgae species with sufficient accuracy (Cohan’s kappa = 0.70). The method was tested on another AISA data set and turned out to be as a robust (Cohan’s kappa = 0.77) and easy-to-use approach for delineating dominant algae communities or habitats, which can be adapted easily to different data sets.
Analysis of coastal marine algae communities enables us to adequately estimate the state of coastal marine environments and provides evidence for environmental changes. Hyperspectral remote sensing provides a tool for mapping macroalgal habitats if the algal communities are spectrally resolvable. We compared the performance of three classification approaches to determine the distribution of macroalgae communities in the rocky intertidal zone of Heligoland, Germany, using airborne hyperspectral (AISAeagle) data. The classification results of two supervised approaches (maximum likelihood classifier and spectral angle mapping) are compared with an approach combining k-Means classification of derivative measures. We identified regions of different slopes between main pigment absorption features of macroalgae and classified the resulting slope bands. The maximum likelihood classifier gained the best results (Cohan's kappa = 0.81), but the new approach turned out as a time-effective possibility to identify the dominating macroalgae species with sufficient accuracy (Cohan's kappa = 0.77), even in the heterogeneous and patchy coverage of the study area.
This paper presents results of a study investigating the potential for improvement of a physically-based model approach, when the static input data is enhanced by dynamic remote sensing information. The model PROMET (PROcess of Radiation Mass and Energy Transfer), which is normally used to simulate the water and energy fluxes at the landscape level, was applied on a field scale to simulate crop growth and yield. The remote sensing input data was derived from hyperspectral images of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). PROMET was set up for a field scale model run for two test fields grown with winter wheat (Triticum aestivum L.) mapping the crop development of the seasons 2004 and 2005. During the model runs, information on the absorptive capacity of the leaves for two canopy layers (sunlit and shaded layer) was updated using remotely sensed chlorophyll measurements. The chlorophyll contents of these two vegetation layers were assessed using angular CHRIS data. Control data were acquired through field measurements, which were conducted throughout the growing periods of both years and also accompanying the satellite overpasses. The stand-alone model was able to reproduce the average development of the crop and yield reasonably well, but the spatial heterogeneity was severely underestimated and yield was overestimated by approximately 20%. The combination of remote sensing data with the model led to an improvement of the spatial heterogeneity of the crop development and yield. The use of ground truth data to improve the modeling accuracy can be made possible.
The study presented here investigates the potential of improvement for a physically based model approach, when the
static input data is enhanced by dynamic remote sensing information. The land surface model PROMET (Processes of
Radiation, Mass and Energy Transfer) was generally applied, while the remote sensing input data was derived from
hyperspectral data of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA
(European Space Agency).
The PROMET model, whose vegetation routine basically applies the Farquhar et al. photosynthesis approach, was set up
to a field scale model run (10 x 10m) for a test acre tilled with wheat (Triticum aestivum L.) mapping the crop
development of the season 2005. During the model run, information on the absorptive capacity of the leaves for two
canopy layers (top, sunlit layer and bottom, shaded layer) was updated from remote sensing measurements, where
angular CHRIS images were available. Control data were acquired through an intensive field campaign, which
monitored the development of the stand throughout the vegetation period of the year 2005, also accompanying the
satellite overflights.
While the model without additional dynamic input data was able to reasonably reproduce the average development of the
crop and yield, the spatial heterogeneity was severely underestimated. The combination of remote sensing information
with the vegetation model led to a significant improvement of both the spatial heterogeneity of the crop development in
the model and yield, which again entailed an overall improvement of the model results in comparison to measured
reference data.
KEYWORDS: Sensors, Reflectivity, Imaging systems, Spectroscopy, Near infrared, Spatial resolution, Cameras, Vegetation, Airborne remote sensing, Signal to noise ratio
Until recently imaging spectroscopy was an extensive tool due to the limitations to airborne systems. Airborne imaging
spectrometer systems are cost-intensive for the user and the availability for multi-temporal applications is still low. But
especially for environmental analysis a high temporal resolution is required because most developments either in natural
or in managed ecosystems can only be monitored through time series of remote sensing observations. In 1997 these
drawbacks led to the idea of a low-cost imaging spectrometer to overcome the difficulties of the existing systems and
provide data for the institute's own environmental research purposes. This was the birth of the AVIS (Airborne Visible/
Infrared imaging Spectrometer) system.
KEYWORDS: Nitrogen, Vegetation, Reflectivity, Absorption, Spectroscopy, Remote sensing, Near infrared, Infrared spectroscopy, Environmental monitoring, Signal to noise ratio
Biochemical components of vegetation canopies, such as chlorophyll and nitrogen, are among the parameters controlling physiological processes and therefore essential for the characterization of these processes and their integration in hydrological or vegetation modeling.
AVIS (Airborne Visible/near Infrared imaging Spectrometer), built at the department for environmental sciences of the Ludwig-Maximilians-University Munich, is a cost-effective tool for environmental monitoring. Its spectral range lies between 550 and 1000nm and its multitemporal application enables observation of the development of chlorophyll and nitrogen content of plants throughout a vegetation period.
Twelve and nine airborne data sets were gathered between April and September 1999 and 2000 respectively from three maize fields in a test site south-west of Munich in the Bavarian Alpine foothills, Germany (48° 6’, 11° 17’ E). Weekly ground-based measurements of plant parameters (plant height, phenology, biomass, nitrogen content, chlorophyll content) during the vegetation periods provided data validation.
The chlorophyll and nitrogen content of the maize canopies were derived using the Chlorophyll Absorption Integral (CAI), which exhibited a high correlation with the chlorophyll content per area and the nitrogen content, both per area (g/m2) and in percentage of dry matter (nitrogen=%DM; chlorophyll=mg/g), during vegetative growth before emergence of the ear. The chlorophyll content per mass cannot be derived with the CAI, due to distinct variations of the chlorophyll per mass during plant growth caused by the low chilling tolerance of maize.
The mean field values and the spatial distribution of parameter values within one of the fields will be presented, demonstrating the capabilities of AVIS.
A new approach for calculating mesoscale soil moisture maps from coarse resolution (500 m - 1 km) SAR data utilizing spatially reduced ERS data is presented. The processing of the radar data is described which includes a correction for the impact of surface roughness, plant water content and soil texture upon of the backscatter intensity. First, the portion of the pixel which does not provide soil moisture information (forest, built-up areas, water) is corrected, then the corrected backscatter intensity is normalized to a reference landuse (cereals). The required landuse map was derived from LANDSAT data. However, these landuse classes which must be distinguished to account for differences in surface roughness and plant water content, may also be derived from AVHRR spectro-temporal unmixing. Using model results and measurements together with the landuse map, the impact of the plant water content was corrected. Finally, the soil texture was taken into account to calculate the mesoscale surface soil moisture. The resulting soil moisture was validated quantitatively using ground truth measurements. A qualitative validation of the spatial patterns was carried out through by comparison of the calculated soil moisture with precipitation patterns. A good agreement was found between ground based and satellite derived soil moisture (RMSQ less than 5 VOL%).
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