Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

摘要

The growing availability of high-resolution satellite image series offersnew opportunities in agro-hydrological research and modeling. Weinvestigated the possibilities offered for improving crop-growth dynamicsimulation with the distributed agro-hydrological model: topography-basednitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map seriesderived from 105 Formosat-2 (F2) images covering the period 2006–2010. TheTNT2 model (Beaujouan et al., 2002), calibrated against discharge andin-stream nitrate fluxes for the period 1985–2001, was tested on the2005–2010 data set (climate, land use, agricultural practices, and dischargeand nitrate fluxes at the outlet). Data from the first year (2005) were usedto initialize the hydrological model. A priori agricultural practices obtained froman extensive field survey, such as seeding date, crop cultivar, and amountof fertilizer, were used as input variables. Continuous values of LAI as afunction of cumulative daily temperature were obtained at the crop-fieldlevel by fitting a double logistic equation against discretesatellite-derived LAI. Model predictions of LAI dynamics using the a priori inputparameters displayed temporal shifts from those observed LAI profiles that areirregularly distributed in space (between field crops) and time (betweenyears). By resetting the seeding date at the crop-field level, we havedeveloped an optimization method designed to efficiently minimize thistemporal shift and better fit the crop growth against both the spatialobservations and crop production. This optimization of simulated LAIhas a negligible impact on water budgets at the catchment scale (1 mm yr?1 onaverage) but a noticeable impact on in-stream nitrogen fluxes (around 12%), which is of interest when considering nitrate stream contaminationissues and the objectives of TNT2 modeling. This study demonstrates thepotential contribution of the forthcoming high spatial and temporalresolution products from the Sentinel-2 satellite mission for improvingagro-hydrological modeling by constraining the spatial representation ofcrop productivity.