Abstract
Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km × 24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.
Highlights
In an agricultural river basin, farmers’ practices have an impact on crop functioning
The simulated ET of both experiments has been compared to the measured ET on the study sites Auradé and Lamasquère. It shows that using leaf area index (LAI) derived from Formosat2 data in the SURFEX simulation improves the correlation and root mean square error (RMSE) of almost every year with respect to the ECOCLIMAP experiment (Tables 2 and 3)
A first experiment was performed with LAI forcing from ECOCLIMAP-II database which was generated from MODIS data
Summary
In an agricultural river basin, farmers’ practices have an impact on crop functioning. Select variety, decide the seeding and harvest dates, and organize irrigation supplements. In such basins, more accurate description of crop dynamics and their effects on hydrometeorological fluxes is critical to improve the monitoring of water resources (Foley et al, 2005; Martin et al, 2016). Land surface models (LSMs), such as the Variable Infiltration Capacity (VIC; Liang et al, 1994) or Interactions between the Surface Biosphere Atmosphere (ISBA; Noilhan and Planton, 1989) models, are increasingly used as distributed hydrological models to study and forecast water resource evolution (e.g., Habets et al, 2008; Tesemma et al, 2015). Etchanchu et al.: Effects of high spatial and temporal resolution Earth observation
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