Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

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.

Ecohydrological dynamics in the Alps: Insights from a modelling analysis of the spatial variability

“…Using a coarser spatial resolution, beyond neglecting ecosystem spatial heterogeneity, can potentially also affect the overall mean in the domain and thus the large‐scale fluxes (Pappas et al, ). The presented results reinforce several recent studies that have demonstrated the potential of high‐resolution simulations in better capturing ecohydrological dynamics under current or future conditions (Etchanchu et al, ; Le & Kumar, ; Passalacqua et al, ), especially when identification of patterns is sought.…”

On the use of high resolution satellite imagery to estimate irrigation volumes and its impact in land surface modeling

“…The LAI estimation is also well known to saturate (Veloso et al, 2012). But above a certain threshold, the difference between satellite-estimated and real LAI does not affect significantly the evapotranspiration flux (Etchanchu et al, 2017). The estimated LAI may also differ between sensors.…”

“…As the phenological cycle is determinant in the irrigation management, a LAI climatology is not sufficiently accurate in our study. Hence determining the LAI from high resolution satellite imagery allows taking the actual crop cycle into account in the model (Etchanchu et al, 2017).…”

“…Particularly, it can monitor the actual phenological cycle of the crops (Etchanchu et al, 2017). It could thus offer precious information to assess more accurately the crop water needs and consequently, the irrigation amounts and its effects on hydrometeorological fluxes.…”

How to account for irrigation withdrawals in a watershed model

“…The FAO-56 method (Allen et al, 1998) is the most widely used land-surface model, in which the potential evapotranspiration (ET) is adjusted with a crop coefficient and a stress coefficient. The classical method uses standard values of crop coefficient, that implies standard growth conditions, but an increasing number of studies replaces them with remote sensing data (Saadi et al, 2015;Etchanchu et al, 2017;Battude et al, 2017;Yousaf et al, 2021;Kharrou et al, 2021;Maguire et al, 2022), in order to account for the current year conditions. In particular, Etchanchu et al (2017) highlight the contribution of remote sensing data at plot scale, that makes more robust calculation of LAI (leaf area index) and ET in comparison to the classical method.…”