Development of a Downscaling Scheme for a Coarse Scale Soil Water Estimation Method

Many river basins worldwide, especially in semi-arid regions, are adversely impacted by poor hydrological infrastructure or are poorly characterized due to limited or no hydrologic data. This condition challenges water-management authorities, who benefit from reliable prediction of the hydrological dynamics that can be made by means of hydrological models. Because of the lack of sufficient or reliable data, often such models are difficult to calibrate and to validate. This study addresses this data limitation by formulating and testing an independent validation tool for hydrological models that can be applied to downscale macro-scale soil water data derived from a remotely sensed scatterometer dataset. This proposed method uses the concept of hydrological response units (HRU) to analyze the spatial variability within one scatterometer footprint. The HRUs are treated as model entities in the process oriented hydrological model J2000 that was applied to the Great Letaba River catchment (ca. 4.700 km²) in South Africa. The soil water time series results were then compared to the remotely sensed data set and the downscaling scheme derived. First, the analysis conducted on footprint scale highlights the similarities in predicting the soil water generation over the long term and in seasonal terms. It also exhibits that the absolute values of both time series can not be used for further investigation, due to differences in the observed soil water volume. Second, the resulted simulated soil water time series were used to establish the downscaling method. Here, the study provides promising results that allow the downscaling of the coarse scale soil water calculated dataset, based upon the landscape related parameters of land cover, soil group and precipitation. The study findings indicate that, by linking the two concepts, hydrological modeling and remote sensing, water management authorities should be able to reduce certain prediction uncertainties of the applied models

Relating surface backscatter response from TRMM precipitation radar to soil moisture: Results over a semi-arid region

The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σº) of the surface. σº is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σº primarily depends on the soil water content. In this study we relate TRMMPR σº measurements to soil water content (m(s)) in the Lower Colorado River Basin (LCRB). σº dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σº that couples incidence angle, m(s), and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated m(s) is estimated using the Variable Infiltration Capacity (VIC) model and measured m(s) is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σº model is calibrated using VIC and WGEW m(s) data during 1998 and the calibrated model is used to derive m(s) during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with a correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σº derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σº dependence on soil water content in the arid regions

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Development of a time series-based methodology for estimation of large-area soil wetness over India using IRS-P4 microwave radiometer data

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day-night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June-July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R>0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0-5 cm) and subsurface (5-10 cm) soil moisture

Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements

Long-term global satellite and reanalysis soil moisture products have been available for several years. In this study, in situ soil moisture measurements from 2008 to 2012 over Southwest China are used to evaluate the accuracy of four satellite-based products and one reanalysis soil moisture product. These products are the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E),the Advanced Scatterometer (ASCAT),the Soil Moisture and Ocean Salinity (SMOS),the European Space Agency's Climate Change Initiative soil moisture (CCI SM),and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim). The evaluation of soil moisture absolute values and anomalies shows that all the products can capture the temporal dynamics of in situ soil moisture well. For AMSR-E and SMOS, larger errors occur, which are likely due to the severe effects of radio frequency interference (RFI) over the test region. In general, the ERA-Interim (R = 0.782, ubRMSD = 0.035 m(3)/m(3)) and CCI SM (R = 0.723, ubRMSD = 0.046 m(3)/m(3)) perform the best compared to the other products. The accuracy levels obtained are comparable to validation results from other regions. Therefore, local hydrological applications and water resource management will benefit from the long-term ERA-Interim and CCI SM soil moisture products

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France