Multi-scale regionalization and monitoring of soil evaporation from readily available data and a mixed modeling approach – MIXMOD-E

The proposed approach consists in developing a phenomenological model (intermediate between theory and experiment) from a multi-site database, a mixed (mecanistic, global and phenomenological) modeling approach, and available remote sensing data such as surface soil moisture, land surface temperature, vegetation cover and surface albedo. This model will be implemented in a disaggregation methodology (DISPATCH) of SMOS/SMAP soil moisture, and in two land surface schemes (ISBA/HTESSEL) to estimate soil evaporation and the evaporation-transpiration partitioning at multiple resolutions and over extended areas. It is planned to test the above approaches over several sites (Southeastern France, Haouz plain in Morrocco, Urgell area in Spain, Chimbarongo area in Chile, and the Murrumbidgee catchment in Australia) where in situ measurements are collected.

Most of the planned field campaigns have been undertaken: deployment and removal 6 months later of 135 ibuttons in the Imlil valley (Morrocco), measurement of extreme soil temperatures and spatialized measurements of surface soil moisture in an irrigated area of the Haouz plain (Morrocco), measurement of the electrical conductivity at Montoussé and Auradé (France), installation and removal 3 months later of a lysimeter at Chimbarongo site (Chile). The sap flow experiment will take place this Spring at Lamasquère (France).
During this first 18-month phase, the modeling work on evaporation process has been organized in three complementary lines: 1) phenomenological modeling from a multi-site database, composed of about 30 sites including the GHGEurope network, 2) mecanistic modeling with the TEC model in collaboration with INRA, and 3) global modeling with GloMo tool. Lines 2 and 3 are jointly dealt by Vivien Stefan in the frame of her PhD co-funded by the ANR projet. Remote sensing activities have brought the following results: the development of a new evapotranspiration model (SEB-1S, Merlin 2013), and of the partitioning between soil evaporation and plant transpiration (SEB-4S, Merlin et al. 2014) from land surface temperature data, 2) the successful integration of a soil energy balance model in SEB-1S (Stefan et al. 2015 submitted to HESS), 3) an evaluation study of 1 km resolution DISPATCH soil moisture data (Malbéteau et al. 2015, submitted to JTARS), and 4) the definition of a new performance metric for data disaggregation methods (Merlin et al. 2015).

During the first 18 months of the projet, a big effort has been made to undertake the field experiments in France, Morrocco and Chili. For now on we are going to focus more on the modeling and its coupling with multi-sensor remote sensing data.
As a prospect of the ANR MIXMOD-E project, the H2020 REC project (recently accepted to the RISE 2013 program) aims at bringing a solution to the need of spatial information on crop water status at a spatio-temporal resolution relevant to daily management of irrigation. It involves the estimation of root zone soil moisture from multi-sensor/multi-resolution remote sensing data. The partners of this new project propose to integrate the MIXMOD-E models and methods in a decision support system. In practice, they seek 1) to refine the water budget at the parcel scale notably by an improved representation of the partitioning between soil evaporation and plant transpiration, and 2) to develop synergies between DISPATCH data and the soil moisture data retrieved from active microwave sensors (SAR) such as Sentinel-1 (the aim of those synergies is to derive a soil moisture product at both high-spatial and high-temporal resolutions). The achievement of REC objectives opens the path for a wide range of future network projects. The partners have already identified several areas of common interest such as water assets accounts, water footprint, drought monitoring, and fire and flood risk indices.

Merlin O, An original interpretation of the wet edge of the surface temperature–albedo space to estimate crop evapotranspiration (SEB-1S), and its validation over an irrigated area in northwestern Mexico. Hydrol. Earth Syst. Sci., 17, 3637-3637, doi:10.5194/hess-17-3623-2013 (2013).
Merlin O., J. Chirouze, A. Olioso, L. Jarlan, G. Chehbouni, and G. Boulet, An image-based four-source surface energy balance model to estimate crop evapotranspiration from solar reflectance/thermal emission data (SEB-4S). Agricultural and Forest Meteorology, 184, 188-203 dx.doi.org/10.1016/j.agrformet.2013.10.002 (2014).
Bandara R., J. P. Walker, C. Rüdiger and O. Merlin, Towards soil property retrieval from space: An application with disaggregated satellite observations. Journal of Hydrology 522 (2015) 582–593, 2015.
Merlin O., Y. Malbéteau, Y. Notfi, S. Bacon, S. Er-Raki, S. Khabba and L. Jarlan, Performance metrics for soil moisture downscaling methods: Application to DISPATCH data in central Morocco, Remote Sensing, Remote Sens. 2015, 7, 1-x; doi:10.3390/, 2015.
Malbéteau Y., O. Merlin, B. Molero, C. Rüdiger, S. Bacon, DisPATCh as a tool for improving validation strategies of coarse-scale remotely sensed soil moisture: Application to SMOS and AMSR-E data in Southeastern Australia. Submitted to International Journal of Applied Earth Observation and Geoinformation, 2015.
Stefan V. G., O. Merlin, S. Er-Raki, M.J. Escorihuela, and S. Khabba. Consistency between in situ, model-derived and image-based soil temperature endmembers: towards a robust data-based model for multi-resolution monitoring of crop evapotranspiration. Submitted to Hydrol. Earth Sci. Syst., hess-2015-91, 2015.

Agronomic, hydrologic, meteorological and climatic predictions rely on our ability to accurately represent soil evaporation (E) process, which is the boundary condition for the soil and atmosphere. For such wide range of applications, E should be modeled over extensive areas at multiple scales.
Since the 60s many E models have been developed. Mechanistic theoretically-based models have been very useful to understand and describe the physical processes regulating E including gravity drainage, capillary rise, vapor diffusion, and the interplay with the atmospheric evaporative limitation. However, their regionalization has been a notorious challenge because of the unavailability and high uncertainty of soil hydraulic properties over extended areas (~100 m - 100 km) and the lack of data at such scales. Simplified models have been generally used across different application scales but their regionalization has been based on empiricism or ad hoc relationships with soil hydraulic properties or texture. In fact, none of the existing E formulations has been validated over an extensive range of soils and soil-atmospheric conditions and no consensus exists on the best way to parameterize E. The fast development of local, regional and global monitoring networks (e.g. Ameriflux, GHGEurope) now permits the advent of improved models.
A related problem is that E is not directly observable using remote sensing platforms although our capability to monitor E-related quantities such as soil moisture is growing rapidly with new remote sensing technologies such as SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) L-band missions. Several studies have shown the potential of combining shortwave-derived vegetation cover, thermal-derived surface temperature, and microwave-derived surface soil moisture to partition evapotranspiration (ETR) into E and plant transpiration (TR) and to indirectly retrieve E. However, no remote sensing method has come up yet because spaceborne temperature and soil moisture products are readily available at different spatial resolutions, and because there is no quasi-instantaneous E model that combines consistently these data.
The objective of this proposal is therefore to fill this gap in the E representation by developing an original phenomenological (intermediate between theory and experiment) model 1) that is valid over a wide range of soils and soil-atmospheric conditions while based on the data available at the application scales (scale-aware model) and 2) that can be coupled to readily-available remotely sensed vegetation cover, surface temperature and surface soil moisture (remote sensing-aware model). The different steps will be: 1) to develop a texture-based E model using an extensive in situ data set and compare it with state-of-the-art models, 2) to investigate three (mechanistic, data-based, mathematical) approaches to quantitatively evaluate the impact of soil moisture profile and soil surface state on E, 3) to implement the new model in a thermal-based disaggregation scheme of SMOS/SMAP soil moisture and to validate the E retrieved at high (~100 m) resolution using in-situ measurements collected under various pedo-hydro-climatic conditions in Chile, France, Morocco and Spain, and 4) to implement the new model in the CNRM (ISBA/SURFEX) and ECMWF (H-TESSEL) land surface models and to validate large scale simulated E using the remotely sensed estimates (previously validated at high resolution using in-situ measurements) aggregated at the corresponding resolutions.