Estimation de l'humidité du sol à partir de données radiométriques en bande-L : préparation de la mission SMOS

Les travaux de cette thèse s'inscrivent dans le cadre de la préparation de la mission SMOS (Soil Moisture and Ocean Salinity). Sur les terres émergées, le satellite SMOS fournira une cartographie globale de l'humidité du sol à partir de données radiométriques en bande-L (1.4 GHz). Le principe physique repose sur la sensibilité de la bande- au contenu en eau de la surface. Le couvert végétal contribue à l'émission en fonction de son contenu en eau, et ses effets doivent être corrigés pour estimer l'humidité du sol. Le satellite SMOS obtiendra des mesures à deux polarisations (horizontale et verticale) et à multiples angles d'incidence. Ce système surdéfini permettera l'inversion de plusieurs paramètres, notamment l'humidité du sol et l'opacité de la végétation. L'objectif de cette thèse est de tester et d'améliorer si possible les modèles micro-ondes pour les scènes naturelles, en tenant compte les particularités de la configuration SMOS, notamment la diversité d'angles d'incidence. La première partie est consacrée à l'émission d'un sol nu. Dans un premier temps, deux approches (télédétection et capteurs in situ) pour estimer l'humidité du sol ont été comparées. Dans un deuxième temps, la signature angulaire et polarimétrique de l'émission d'un sol nu rugueux est étudiée, un modèle semi-empirique est développé pour prendre en compte des effets de la rugosité aux différents angles et polarisations. La seconde partie est consacrée à l'émission d'un couvert végétal. L'influence de la rosée et les variations journalières de teneur en eau de la végétation sur l'émission ont été analysées. ABSTRACT : This thesis has been developped in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission preparation. Over land, SMOS will provide global mapping of soil moisture from L-band (1.4 Ghz) radiometric measurements. The principle for soil moisture monitoring is the high sensitivity of L-band measurements to soil water content. Vegetation contributes as well to the emission and its effects must to be considered to correctly estimate soil moisture. SMOS will provide measurements at multiple incidence angles and for two polarizations. This overdefined system will allow for several parameters retrieval, namely soil moisture and vegetation optical depth. This thesis aims at testing and improving when possible the existing microwave models for the emission of natural surfaces, taking into account the SMOS configuration, namely the multiangularity. The first part of this thesis deals with a bare soil surface. Two approaches (remote sensing and in situ sensors) have been compared. Then a new roughness model has been developped considering the angular and polarimetric signature of the emission. The second part of the thesis focuses on the emission of a natural fallow. Dew and diurnal variations of vegetatation water content effects on the signal have been assesse

High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.This research was funded by the Torrres Quevedo program of the Spanish Science Ministry, MICINN (grant number PTQ-16-08766)

Disaggregation of SMOS soil moisture to 100m resolution using MODIS optical/thermal and sentinel-1 radar data: evaluation over a bare soil site in morocco

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m3 m−3).This work is a contribution to the REC project funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action under grant agreement no: 645642. In addition, this work has been partially funded by a public grant of Ministerio de Economía y Competitividad (DI-14-06587) and AGAUR-Generalitat de Catalunya (DI-2015-058)

SMOS based high resolution soil moisture estimates for Desert locust preventive management

This paper presents the first attempt to include soil moisture information from remote sensing in the tools available to desert locust managers. The soil moisture requirements were first assessed with the users. The main objectives of this paper are: i) to describe and validate the algorithms used to produce a soil moisture dataset at 1 km resolution relevant to desert locust management based on DisPATCh methodology applied to SMOS and ii) the development of an innovative approach to derive high-resolution (100 m) soil moisture products from Sentinel-1 in synergy with SMOS data. For the purpose of soil moisture validation, 4 soil moisture stations where installed in desert areas (one in each user country). The soil moisture 1 km product was thoroughly validated and its accuracy is amongst the best available soil moisture products. Current comparison with in-situ soil moisture stations shows good values of correlation (R>0.7R>0.7) and low RMSE (below 0.04 m3 m−3). The low number of acquisitions on wet dates has limited the development of the soil moisture 100 m product over the Users Areas. The Soil Moisture product at 1 km will be integrated into the national and global Desert Locust early warning systems in national locust centres and at DLIS-FAO, respectively