BibSonomy publications for /user/jgomezdans/satelliteFri Jul 04 17:01:54 CEST 2008Hydrology and Earth System Sciences1663-1696Unsupervised classification of saturated areas using a time series of remotely sensed images42007remotesensing classification drought moisture reflectance vegetation satellite The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape. Thu Jul 03 18:17:33 CEST 20082002 Soil Moisture Experiment (SMEX02)Remote Sensing of EnvironmentSep4475--482Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans922004satellite remotesensing moisture drought reflectance Soil ndwi evaporation vegetation soilmoisture stress Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.Thu May 15 14:50:50 CEST 2008Remote Sensing Data Assimilation Special IssueRemote Sensing of Environmentapr41395--1407Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield1122008ASAR models Data crops retrieval LAI Crop MERIS microwave satellite SAR maps growth vegetation Wheat This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated. The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season. Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360�kg/ha to 420�kg/ha.Thu May 15 14:40:09 CEST 2008Agricultural Systemsjan1-376--90Yield uncertainty at the field scale evaluated with multi-year satellite data922007modeling remotesensing vegetation satellite yield models uncertainty crops optical The level of yield risk faced by a farmer is an important factor in the design of appropriate management and insurance strategies. The difference between field scale and regional scale yield risk, which can be significant, also represents an important measure of the factors that cause the yield gap - the difference between average and maximum yields. While field scale yield risk is difficult to assess with traditional data sources, yield maps derived from remote sensing offer promise for obtaining the necessary data in any region. We analyzed remotely sensed yield datasets for two regions in Northwest Mexico, the Yaqui and San Luis Rio Colorado Valleys, in conjunction with time series of aggregated regional yields for 1976-2002. Regional scale yield risk was roughly 8% of average yields in both regions. Field scale yield risk was determined to be 58% higher than regional scale risk in both regions. The difference between field and regional scale risk accounted for 50% of the spatial variance in yields in the Yaqui Valley, and 70% in the San Luis Rio Colorado Valley, indicating that climatic uncertainty represents an important source of the spatial yield variability. This implies that accurate seasonal climate forecasts could substantially reduce yield losses in farmers' fields. The results were shown to be fairly sensitive to assumptions about the magnitude and nature of errors in yield estimation, suggesting that improved understanding of estimation errors are needed to realize the full potential of remote sensing for yield risk analysis.Thu May 15 14:26:48 CEST 2008Recent Development in River Basin Research and ManagementPhysics and Chemistry of the Earth, Parts A/B/C33-361365--1376The use of earth observation techniques to improve catchment-scale pollution predictions282003optical models erostion pollution nitrogen crops remotesensing reflectance satellite assimilation vegetation Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions.Thu May 15 14:26:11 CEST 2008IEEE Transactions on Geoscience and Remote Sensing1629- 1637Methods and examples for remote sensing data assimilation in land surface process modeling412003assimilation crops models canopy satellite reflectance BRDF model uncertainty optical vegetation remotesensing sensing Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data [Landsat Thematic Mapper (TM)].Thu May 15 14:25:12 CEST 2008Photogrammetric Engineering and Remote SensingJune6647-664Remote Sensing for Crop Management692003vegetation remotesensing satellite yield optical crops Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and positionlocating technologies, remote sensing from ground, air, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future.Thu May 15 14:19:06 CEST 2008IEEE Transactions on Geoscience and Remote Sensing5-17The potential of multifrequency polarimetric SAR in assessingagricultural and arboreous biomass351997vegetation crops SAR remotesensing satellite biomass microwave Polarimetric radar data collected by AIRSAR and SIR-C over agricultural fields, forests, and olive groves of the Italian Montespertoli site are analyzed. The objective is to investigate the radar capability in discriminating among various vegetation species and its sensitivity to agricultural and arboreous biomass. Results indicate that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to discriminate between agricultural fields and other targets, while a combined use of L- and C- (5.3 GHz) bands allows the authors to discriminate within agricultural areas. To monitor biomass, P-band gives the best results for forests and olive groves, L-band appears to be good for crops with low plant density (m-2), while for crops with high plant density, both L- and C-bands are useful. The availability of crosspolarized data is important for both classification and biomass retrievalThu May 15 14:18:41 CEST 2008IEEE Transactions on Geoscience and Remote Sensing,960-968Experimental and model investigation on radar classificationcapability371999crops SAR microwave satellite remotesensing classification The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposedThu May 15 13:09:14 CEST 2008Amsterdam, The Netherlands, The NetherlandsEnviron. Model. Softw.81070--1081Multispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate232008uncertainty crops satellite nitrogen remotesensing imported The advantages of using multispectral remotely sensed data instead of CORINE Land Cover for the modelling of nitrate concentrations in the leachate of the Rur catchment are presented and discussed in this paper. In this context it has been shown that the identification of main crops and annual crop rotation in the Rur catchment by SPOT, LANDSAT and ASTER imagery provides the key for a spatial and thematic enhancement of the model results. The spatial resolution of the nitrogen surplus data set which denotes the linkage between RAUMIS and GROWA is enhanced from district level to field/pixel level. In parallel, the empirical water balance model GROWA is enhanced to differentiate between agricultural crops in the real evapotranspiration calculation. It is calibrated by runoff data measured at gauging stations. Results indicate, e.g., an average nitrate concentration in the leachate of 42mg NO"3/L in the relatively wet year of 2002 and almost 62mg NO"3/L in the dry year of 2003. There is a 20mg NO"3/L weather-induced difference which can be modelled in a more detailed way using self-processed remotely sensed data. The model results were compared to nitrate concentrations observed in the top parts of multi-level wells. In this way the related coefficient of determination has been improved from a value (R) of -0.50 using CORINE to 0.59 by using self-processed remotely sensed data, thus demonstrating the potential of the enhanced model system.Thu May 15 12:42:42 CEST 20082002 Soil Moisture Experiment (SMEX02)Remote Sensing of Environment#sep#4548--559Crop condition and yield simulations using Landsat and MODIS922004reflectance application Crop satellite MODIS assimilation moisture models crops remotesensing uncertainty mapping modis vegetation Soil optical Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6�1.6-km2 grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.Thu May 15 12:28:50 CEST 2008Applications of Quantitative Remote Sensing to HydrologyPhysics and Chemistry of the Earth, Parts A/B/C1-33--13Remote sensing data assimilation using coupled radiative transfer models282003BRDF yield modeling assimilation canopy optical satellite vegetation reflectance remotesensing crops brdf uncertainty This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth.Thu May 15 11:47:50 CEST 2008International Journal of Remote SensingApril61021-1036Combining agricultural crop models and satellite observations: from field to regional scales191998satellite crops wheat remotesensing assimilation uncertainty This review article gives an overview of how satellite observations are used to feed or tune crop models and improve their capability to predict crop yields in a region. Relations between crop characteristics which correspond to models state variables and satellite observations are briefly analysed, together with the various types of crop models commonly used. Various strategies for introducing short wavelength radiometric information into specific crop models are described, from direct update of model state variables to optimization of model parameter values, and some of them are exemplified. Methods to unmix crop-specific information from mixed pixels in coarse resolution-high frequency imagery are analysed. The conditions of use of the various methods and types of information are discussed.Wed May 07 15:41:36 CEST 2008Earth and Planetary Science Letters#sep#3-4516--523The accuracy of digital elevation models of the Antarctic continent2372005elevation altimetry Antarctica digital ICESat glaciology GLAS dem ERS model satellite The accuracy of two widely used digital elevation models of Antarctica was assessed using data from the Geoscience Laser Altimeter System onboard ICESat. The digital elevation models were derived from satellite radar altimeter and terrestrial data sets. The first, termed JLB97, was produced predominantly from ERS-1 data while the second, termed, RAMPv2 included other sources of data in areas of high relief and poor coverage by ERS-1. The accuracy of the models was examined as a function of surface slope and original data source. Large errors, in excess of 100 m, were ubiquitous in both models in areas where terrestrially-derived elevation data had been used but were more extensive in RAMPv2. Elsewhere, the systematic error (bias) was found to be a monotonic function of slope for JLB97, with a more complex, less predictable bias for RAMPv2. The magnitude of the global, slope-dependent, bias ranged from less than a metre to slightly over 10 m but with much larger regional deviations. The random error ranged from about 1 m to over 100 m depending on the DEM and slope. The random error was consistently over a factor two larger for RAMPv2 compared to JLB97.Wed May 07 15:36:09 CEST 2008IEEE Transactions on Geoscience and Remote Sensing1324-1338MODIS land data storage, gridding, and compositing methodology: Level 2 grid361998satellite modis remotesensing grid Wed May 07 15:36:09 CEST 2008Remote Sensing of Environment135-148First operational BRDF, Albedo and Nadir reflectance products from MODIS832002remotesensing satellite modis reflectance products brdf albedo Wed May 07 15:36:09 CEST 2008International Journal of Remote SensingThe Southern Africa Fire Network (SAFNet) regional burned-area product-validation protocolIn press2005wildfire validation brdf fires remotesensing satellite Wed May 07 15:36:09 CEST 2008American Geophysical Union Spring 2000 Meeting, Session B04: Remote Sensing of the Biosphere, Washington DC, May 30 - June 3Burned area mapping from multitemporal surface bi-directional reflectance2000remotesensing fire reflectance modis satellite wildfire burnedarea brdf