BibSonomy publications for /user/jgomezdans/sensingThu 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 modeling412003crops optical uncertainty canopy sensing model reflectance remotesensing vegetation models satellite BRDF assimilation 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)].Wed May 07 20:18:23 CEST 2008Journal of HydrologyJuly1-2151--173Estimating potential evapotranspiration using Shuttleworth-Wallace model and NOAA-AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin3272006models, and, sensing remote, Summary One of key inputs to hydrological modeling is the potential evapotranspiration, either from the interception (PET0) or from the soil water of root zone (PET). The Shuttleworth-Wallace (S-W) model is developed for their estimation. In this parameterization, neither experimental measurement nor calibration is introduced. Based on IGBP land cover classification, the typical thresholds of vegetation parameters are drawn from the literature. The spatial and temporal variation of vegetation LAI is derived from the composite NOAA-AVHRR NDVI using the SiB2 method. The CRU database supplies with the required meteorological data. They are all publicly available. The developed S-W model is applicable at the global scale, particularly to the data-poor or ungauged large basins. Using the century monthly time series of CRU TS 2.0 and the monthly composite NOAA-AVHRR NDVI from 1981 to 2000, annual PET is estimated 1354 mm over the Mekong River basin, spatially distributed strikingly non-uniformly from 300 to 2040 mm, and seasonally changed significantly with LAI. By replacing the monthly with the 10-day composite NDVI and the albedo of 0.10 with 0.15 for substrate soil surface, annual PET relatively decreases less than 4\% and 1.7\%, respectively over the whole basin. The correlation with pan evaporation (Epan) is quite scattered but grouped with the vegetation types and consistent with a rough ratio as reported in the literature. In contrast, the PET and the reference evapotranspiration (RET) are vegetation-type-dependently correlated very well. The PET0 is estimated 1.63 times of PET in average over the whole basin. The application of BTOPMC model shows that the derived LAI, PET0 and PET behave very well in the distributed hydrological modeling.Wed May 07 20:18:23 CEST 2008Agrometeorology 2003Physics and Chemistry of the Earth, Parts A/B/C1-369--79Basin-wide actual evapotranspiration estimation using NOAA/AVHRR satellite data302005remote, sensing The present study elaborates on the estimation of actual evapotranspiration at watershed scale using mathematical models and remote sensing techniques. A water balance model was updated and used to simulate runoff from a watershed, based on input data of precipitation and potential evapotranspiration. The water balance simulation considers the loss and routing functions to estimate actual evapotranspiration. Two methods were used to estimate the areal precipitation, the precipitation gradient method and the Thiessen polygons method adjusted for the mean elevation of the watershed. Areal potential evapotranspiration was calculated using three empirical and semi-empirical methods based on temperature, precipitation, and solar radiation. The potential evapotranspiration methods used were the Thornthwaite, Blaney-Criddle, and the modified Penman-Monteith. The water balance model was calibrated with the observed monthly runoff. The actual basin-wide evapotranspiration was estimated using the water balance model. Monthly composites of the normalized difference vegetation index (NDVI), derived from the National Oceanic and Atmospheric Administration's (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) data were correlated and linear relationships were developed with the water balance computed monthly actual evapotranspiration rates for four watersheds of Central Thessaly, Greece. The performance of the developed Actual evapotranspiration-NDVI relationships was examined using various statistical tests. The NDVI-derived actual evapotranspiration agrees well, in general, with the actual evapotranspiration calculated from the water balance method for both wet and water-limiting conditions.Wed May 07 20:18:23 CEST 2008Journal of HydrologyJuly1-434--50Use of remotely sensed precipitation and leaf area index in a distributed hydrological model2642002models, sensing and, remote, Remotely sensed precipitation from METEOSAT data and leaf area index (LAI) from NOAA AVHRR data is used as input data to the distributed hydrological modelling of three subcatchments (82,000 km2) in the Senegal River Basin. Further, root depths of annual vegetation are related to the temporal and spatial variation of LAI. The modelling results are compared with results based on conventional input of precipitation and vegetation characteristics. The introduction of remotely sensed LAI shows improvements in the simulated hydrographs, a marked change in the relative proportions of actual evapotranspiration comprising canopy evaporation, soil evaporation and transpiration, while no clear trend in the spatial pattern could be found. The remotely sensed precipitation resulted in similar model performances with respect to the simulated hydrographs as with the conventional raingauge input. A simple merging of the two inputs did not result in any improvement.Wed May 07 20:18:23 CEST 2008Journal of HydrologyDecember250--267Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data212-2131998and, sensing remote, models, The PROMET-family of spatial evapotranspiration models permits modelling of evapotranspiration at field scale, as well as microscale and mesoscale. The model-family consists of a kernel model (a SVAT based on Penman-Monteith and a plant-physiological model for the influence of environmental parameters on canopy resistance) and a spatial modeller, which provides and organises adequate spatial input data on the field , micro and mesoscale. Model results on the field scale show good agreement with measurements. Spatial data is set up using remote sensing and conventional data sources for a 150x100km mesoscale test region in Upper Bavaria and a 7x13 km microscale test region embedded in the mesoscale area. The model is run on both scales. Microscale model results are successfully verified using field measurements and water-balance data taken from literature. Mesoscale model results compare well both with microscale model results and satellite-measured patterns of surface temperature.Wed May 07 20:18:23 CEST 2008Biological and Physical Processes on LandAdvances in Space Research1100--104Evaluation of the MERIS terrestrial chlorophyll index (MTCI)392007sensing remote, The Medium Resolution Imaging Spectrometer (MERIS), one of the payloads on Envisat, has fine spectral resolution, moderate spatial resolution and a 3-day repeat cycle. This makes MERIS a potentially valuable sensor for the measurement and monitoring of terrestrial environments at regional to global scales. The red edge, which results from an abrupt reflectance change in red and near-infrared (NIR) wavelengths has a location that is related directly to the chlorophyll content of vegetation. A new index called the MERIS terrestrial chlorophyll index (MTCI) uses data in three red/NIR wavebands centered at 681.25, 708.75 and 753.75 nm (bands 8, 9 and 10 in the MERIS standard band setting). The MTCI is easy to calculate and can be automated. Preliminary indirect evaluation using model, field and MERIS data suggested its sensitivity to chlorophyll content, notably at high values. As a result this index is now a standard level-2 product of the European Space Agency. For direct MTCI evaluation two approaches were used. First, MTCI/chlorophyll content relationships were determined using a chlorophyll content surrogate for sites in southern Vietnam and second, MTCI/chlorophyll relationships were determined using actual chlorophyll content for sites in the New Forest, UK and for plots in the greenhouse. Forests in southern Vietnam were contaminated heavily with herbicides during the Vietnam War. This led to a long term decrease in chlorophyll content within forests that have long since regained full canopy cover. The amount of herbicide dropped onto the forests between 1965 and 1971 was used as a surrogate (inverse) for contemporary chlorophyll content and was related to current MTCI at selected forest sites. The resulting relationship was both strong and negative. Further per-pixel investigation of the MTCI/herbicide concentration relationship is under way for large forest regions. In the second approach MTCI was related directly to chlorophyll content at two scales and the initial relationships were both of strong and positive. Further plans involve MTCI evaluation at local, regional and eventually global scales.Wed May 07 20:18:23 CEST 2008Remote Sensing of EnvironmentDecember4313--325Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: Principles and validation1052006sensing remote, A neural network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the fraction of vegetation cover (fCover), and the canopy chlorophyll content (LAIxCab). The neural network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the network are first presented. The estimated variables are then compared to other existing products, LAI- and fAPAR-MODIS and MGVI-MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).