ALBERT

Description: An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult in complex terrain. The main objective of this work was to investigate the potential of a Two-Source-Energy-Balance model (TSEB) driven by the Landsat and MODIS data for estimating ET over a complex mountain region. The complexity is associated with the type of the vegetation canopy as well as the changes in topography. For validating purposes, a large-aperture scintillometer (LAS) was set up over a heterogeneous transect of about 1.4 km to measure sensible (H) and latent heat (LE) fluxes. Additionally, two towers of eddy covariance (EC) systems were installed along the LAS transect. First, the model was tested at the local scale against the EC measurements using multi-scale remote sensing (MODIS and Landsat) inputs at the satellite overpasses. The obtained averaged values of the root mean square error (RMSE) and correlation coefficient (R) were about 72.4 Wm−2 and 0.79 and 82.0 Wm−2 and 0.52 for Landsat and MODIS data, respectively. Secondly, the potential of the TSEB model for evaluating the latent heat fluxes at large scale was investigated by aggregating the derived parameters from both satellites based on the LAS footprint. As for the local scale, the comparison of the latent heat fluxes simulated by TSEB driven by Landsat data performed well against those measured by the LAS (R = 0.69, RMSE = 68.0 Wm−2), while slightly more scattering was observed when MODIS products were used (R = 0.38, RMSE = 99.8 Wm−2). Based on the obtained results, it can be concluded that (1) the TSEB model can be fairly used to estimate the evapotranspiration over the mountain regions; and (2) medium- to high-resolution inputs are a better option than coarse-resolution products for describing this kind of complex terrain.

Description: The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

Description: Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection.

Description: In semiarid areas, agricultural production is restricted by water availability; hence, efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the normalized difference vegetation index, NDVI) and water availability under water stress (through the surface temperature Tsurf), which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat flux LE) in the Kairouan plain (central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) model fed by low-resolution remote sensing data (Terra and Aqua MODIS). The work's goal was to assess the operational use of the SPARSE model and the accuracy of the modeled (i) sensible heat flux (H) and (ii) daily ET over a heterogeneous semiarid landscape with complex land cover (i.e., trees, winter cereals, summer vegetables). SPARSE was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass times. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 and 53.85 W m−2 for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS) H measurements along a path length of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scales (RMSE = 47.20 and 43.20 W m−2 for Terra and Aqua, respectively, and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modeled (SPARSE) and observed (XLAS) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

Description: 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.

Description: In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 W/m-2 and 53.85 W/m-2; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scale (RMSE = 47.20 W/m-2 and 43.20 W/m-2; for Terra and Aqua, respectively and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

Description: Agricultural landscapes often include 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 as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow 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 flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. 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.

Description: Irrigation is a major issue for water resources management agencies as it is the main component of human fresh water consumption. However, irrigation can be monitored at plot scale but not at larger scales, i.e. from river basin to global scale. Hence, simulating the irrigation process in models is of great interest, not only to forecast the water availability, but also to provide realistic lower boundary conditions for atmosphere and climate models. This process is relatively well represented in agronomical or agro-hydrological models, designed for crop and water management at the plot scale. But this kind of model is not adapted for water management at the basin scale or even larger scale, due to their complexity. Land Surface Models (LSMs) are used for this purpose. However, irrigation is not well represented in LSMs. These models use basic decision rules to estimate irrigation volumes. Most of the time, it only consists in triggering an irrigation event when the soil moisture in the root zone drops below a fixed threshold. This threshold is unique at global scale, being independent of the crop type or the common irrigation practices in the simulated area. Then an irrigation amount is applied based on the volume needed to replenish the soil reservoir to a fixed level. There is no consideration about actual agricultural practices. These simple irrigation schemes do not have the flexibility needed to adapt to the wide variety of crops and irrigation practices encountered at large scales. The present study aims at developing and evaluating an irrigation scheme very similar to the one used in agronomical or agro-hydrological models for the SURFEX-ISBA LSM developed by Meteo-France. Particularly, it allows adapting the triggering threshold spatially and temporally and relating it to the actual phenology of the crop and to the irrigation practices. But increasing the flexibility of a model also means that it needs more input information to constrain it. High-resolution remote sensing products, like those derived from Sentinel-2, can provide part of this information spatially. This study thus presents a method to determine irrigation parameters, and particularly the triggering soil moisture threshold, from high-resolution remotely sensed leaf area index. This method is compared to three other experiments: a reference simulation with the current irrigation scheme of SURFEX-ISBA, a second experiment designed to show the contribution of remotely sensed irrigation period determination in the current scheme and a third which uses a single threshold over the season. The comparison is done on several maize plots in southwestern France. The results show that the method using remote sensing to modulate the triggering soil moisture threshold shows the best performances in estimating annual irrigation volumes. Indeed, it shows a bias around 10 mm per year and a RMSE around 30 mm whereas the standard scheme shows a bias around 50 mm per year and a RMSE around 60 mm. The sensitivity to the estimation of the soil maximal available water content is then performed. It shows that all the experiments are very sensitive when the maximal available water content in the soil is low. Finally, the impact on evapotranspiration is evaluated. It shows small differences between experiments and with the measured evapotranspiration. This study thus shows the potential of using high resolution remote sensing products to improve the irrigation simulation in LSMs. Indeed, it allows increasing the realism of the irrigation scheme while keeping it generic enough to simulate at regional to global scale.