Single-pass soil moisture retrievals using GNSS-R: lessons learned

In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly.This work has been funded by the Spanish MCIU and EU ERDF project (RTI2018-099008-B-C21) “Sensing with pioneering opportunistic techniques” and grant to ”CommSensLab-UPC” Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-600), and by a Doctorat Industrial grant from ICGC.Peer ReviewedPostprint (published version

Coupled land surface and radiative transfer models for the analysis of passive microwave satellite observations

Soil moisture is one of the key variables controlling the water and energy exchanges between Earth’s surface and the atmosphere. Therefore, remote sensing based soil moisture information has potential applications in many disciplines. Besides numerical weather forecasting and climate research these include agriculture and hydrologic applications like flood and drought forecasting. The first satellite specifically designed to deliver operational soil moisture products, SMOS (Soil Moisture and Ocean Salinity), was launched 2009 by the European Space Agency (ESA). SMOS is a passive microwave radiometer working in the L-band of the microwave domain, corresponding to a frequency of roughly 1.4 GHz and relies on a new concept. The microwave radiation emitted by the Earth’s surface is measured as brightness temperatures in several look angles. A radiative transfer model is used in an inversion algorithm to retrieve soil moisture and vegetation optical depth, a measure for the vegetation attenuation of the soil’s microwave emission. For the application of passive microwave remote sensing products a proper validation and uncertainty assessment is essential. As these sensors have typical spatial resolutions in the order of 40 – 50 km, a validation that relies solely on ground measurements is costly and labour intensive. Here, environmental modelling can make a valuable contribution. Therefore the present thesis concentrates on the question which contribution coupled land surface and radiative transfer models can make to the validation and analysis of passive microwave remote sensing products. The objective is to study whether it is possible to explain known problems in the SMOS soil moisture products and to identify potential approaches to improve the data quality. The land surface model PROMET (PRocesses Of Mass and Energy Transfer) and the radiative transfer model L-MEB (L-band microwave emission of the Biosphere) are coupled to simulate land surface states, e.g. temperatures and soil moisture, and the resulting microwave emission. L-MEB is also used in the SMOS soil moisture processor to retrieve soil moisture and vegetation optical depth simultaneously from the measured microwave emission. The study area of this work is the Upper Danube Catchment, located mostly in Southern Germany. Since model validation is essential if model data are to be used as reference, both models are validated on different spatial scales with measurements. The uncertainties of the models are quantified. The root mean squared error between modelled and measured soil moisture at several measuring stations on the point scale is 0.065 m3/m3. On the SMOS scale it is 0.039 m3/m3. The correlation coefficient on the point scale is 0.84. As it is essential for the soil moisture retrieval from passive microwave data that the radiative transfer modelling works under local conditions, the coupled models are used to assess the radiative transfer modelling with L-MEB on the local and SMOS scales in the Upper Danube Catchment. In doing so, the emission characteristics of rape are described for the first time and the soil moisture retrieval abilities of L-MEB are assessed with a newly developed LMEB parameterization. The results show that the radiative transfer modelling works well under most conditions in the study area. The root mean squared error between modelled and airborne measured brightness temperatures on the SMOS scale is less than 6 – 9 K for the different look angles. The coupled models are used to analyse SMOS brightness temperatures and vegetation optical depth data in the Upper Danube Catchment in Southern Germany. Since the SMOS soil moisture products are degraded in Southern Germany and in different other parts of the world these analyses are used to narrow down possible reasons for this. The thorough analysis of SMOS brightness temperatures for the year 2011 reveals that the quality of the measurements is degraded like in the SMOS soil moisture product. This points towards radio frequency interference problems (RFI), that are known, but have not yet been studied thoroughly. This is consistent with the characteristics of the problems observed in the SMOS soil moisture products. In addition to that it is observed that the brightness temperatures in the lower look angles are less reliable. This finding could be used to improve the brightness temperature filtering before the soil moisture retrieval. An analysis of SMOS optical depth data in 2011 reveals that this parameter does not contain valuable information about vegetation. Instead, an unexpected correlation with SMOS soil moisture is found. This points towards problems with the SMOS soil moisture retrieval, possibly under the influence of RFI. The present thesis demonstrates that coupled land surface and radiative transfer models can make a valuable contribution to the validation and analysis of passive microwave remote sensing products. The unique approach of this work incorporates modelling with a high spatial and temporal resolution on different scales. This makes detailed process studies on the local scale as well as analyses of satellite data on the SMOS scale possible. This could be exploited for the validation of future satellite missions, e.g. SMAP (Soil Moisture Active and Passive) which is currently being prepared by NASA (National Aeronautics and Space Administration). Since RFI seems to have a considerable influence on the SMOS data due to the gained insights and the quality of the SMOS products is very good in other parts of the world, the RFI containment and mitigation efforts carried out since the launch of SMOS should be continued

Coupled land surface and radiative transfer models for the analysis of passive microwave satellite observations

Soil moisture is one of the key variables controlling the water and energy exchanges between Earth’s surface and the atmosphere. Therefore, remote sensing based soil moisture information has potential applications in many disciplines. Besides numerical weather forecasting and climate research these include agriculture and hydrologic applications like flood and drought forecasting. The first satellite specifically designed to deliver operational soil moisture products, SMOS (Soil Moisture and Ocean Salinity), was launched 2009 by the European Space Agency (ESA). SMOS is a passive microwave radiometer working in the L-band of the microwave domain, corresponding to a frequency of roughly 1.4 GHz and relies on a new concept. The microwave radiation emitted by the Earth’s surface is measured as brightness temperatures in several look angles. A radiative transfer model is used in an inversion algorithm to retrieve soil moisture and vegetation optical depth, a measure for the vegetation attenuation of the soil’s microwave emission. For the application of passive microwave remote sensing products a proper validation and uncertainty assessment is essential. As these sensors have typical spatial resolutions in the order of 40 – 50 km, a validation that relies solely on ground measurements is costly and labour intensive. Here, environmental modelling can make a valuable contribution. Therefore the present thesis concentrates on the question which contribution coupled land surface and radiative transfer models can make to the validation and analysis of passive microwave remote sensing products. The objective is to study whether it is possible to explain known problems in the SMOS soil moisture products and to identify potential approaches to improve the data quality. The land surface model PROMET (PRocesses Of Mass and Energy Transfer) and the radiative transfer model L-MEB (L-band microwave emission of the Biosphere) are coupled to simulate land surface states, e.g. temperatures and soil moisture, and the resulting microwave emission. L-MEB is also used in the SMOS soil moisture processor to retrieve soil moisture and vegetation optical depth simultaneously from the measured microwave emission. The study area of this work is the Upper Danube Catchment, located mostly in Southern Germany. Since model validation is essential if model data are to be used as reference, both models are validated on different spatial scales with measurements. The uncertainties of the models are quantified. The root mean squared error between modelled and measured soil moisture at several measuring stations on the point scale is 0.065 m3/m3. On the SMOS scale it is 0.039 m3/m3. The correlation coefficient on the point scale is 0.84. As it is essential for the soil moisture retrieval from passive microwave data that the radiative transfer modelling works under local conditions, the coupled models are used to assess the radiative transfer modelling with L-MEB on the local and SMOS scales in the Upper Danube Catchment. In doing so, the emission characteristics of rape are described for the first time and the soil moisture retrieval abilities of L-MEB are assessed with a newly developed LMEB parameterization. The results show that the radiative transfer modelling works well under most conditions in the study area. The root mean squared error between modelled and airborne measured brightness temperatures on the SMOS scale is less than 6 – 9 K for the different look angles. The coupled models are used to analyse SMOS brightness temperatures and vegetation optical depth data in the Upper Danube Catchment in Southern Germany. Since the SMOS soil moisture products are degraded in Southern Germany and in different other parts of the world these analyses are used to narrow down possible reasons for this. The thorough analysis of SMOS brightness temperatures for the year 2011 reveals that the quality of the measurements is degraded like in the SMOS soil moisture product. This points towards radio frequency interference problems (RFI), that are known, but have not yet been studied thoroughly. This is consistent with the characteristics of the problems observed in the SMOS soil moisture products. In addition to that it is observed that the brightness temperatures in the lower look angles are less reliable. This finding could be used to improve the brightness temperature filtering before the soil moisture retrieval. An analysis of SMOS optical depth data in 2011 reveals that this parameter does not contain valuable information about vegetation. Instead, an unexpected correlation with SMOS soil moisture is found. This points towards problems with the SMOS soil moisture retrieval, possibly under the influence of RFI. The present thesis demonstrates that coupled land surface and radiative transfer models can make a valuable contribution to the validation and analysis of passive microwave remote sensing products. The unique approach of this work incorporates modelling with a high spatial and temporal resolution on different scales. This makes detailed process studies on the local scale as well as analyses of satellite data on the SMOS scale possible. This could be exploited for the validation of future satellite missions, e.g. SMAP (Soil Moisture Active and Passive) which is currently being prepared by NASA (National Aeronautics and Space Administration). Since RFI seems to have a considerable influence on the SMOS data due to the gained insights and the quality of the SMOS products is very good in other parts of the world, the RFI containment and mitigation efforts carried out since the launch of SMOS should be continued

Modeling L-Band Microwave Emission From Soil-Vegetation System

During a field campaign covering the 2002 corn growing season, a dual polarized tower mounted L-band (1.4 GHz) radiometer (LRAD) provided brightness temperature (T¬B) measurements at preset intervals, incidence and azimuth angles. These radiometer measurements were supported by an extensive characterization of land surface variables including soil moisture, soil temperature, vegetation biomass, and surface roughness. During the period from May 22, 2002 to August 30, 2002 a range of vegetation water content (W) of 0.0 to 4.3 kg m-2, ten days of radiometer and ground measurements were available. Using this data set, the effects of corn vegetation on surface emissions are investigated by means of a semi-empirical radiative transfer model. Additionally, the impact of roughness on the surface emission is quantified using T¬B measurements over bare soil conditions. Subsequently, the estimated roughness parameters, ground measurements and horizontally (H)-polarized TB are employed to invert the H-polarized transmissivity (γh) for the monitored corn growing season

Estimation de l’humidité du sol en milieu agricole par combinaison des données polarimétriques radar en bande C et des micro-ondes passives en bande L

L’humidité du sol a un rôle majeur dans la régulation des éléments du climat (précipitations, température, H2O atmosphérique) et du cycle de l’eau. Pour étudier l’humidité du sol à l’échelle globale, la télédétection spatiale micro-onde présente un fort potentiel. Dans le cas du satellite Soil Moisture Active Passive (SMAP), les méthodes initialement développées permettaient d’obtenir trois produits d’humidité du sol : actif, passif et actif-passif avec une résolution spatiale fine de 3 km, grossière de 40 km et moyenne de 9 km, respectivement. Cependant, six mois après le lancement du satellite, son radar s’est détérioré, empêchant SMAP de générer des produits d’humidité du sol à fine et moyenne résolution spatiale. Dès lors, des équipes de recherche ont étudié la possibilité de combiner des mesures micro-ondes actives et passives avec des capteurs installés sur des plateformes différentes et opérant à des fréquences différentes. Ce projet propose une approche de combinaison des mesures micro-ondes actives et passives de satellites différents pour estimer l’humidité du sol à 1 km de résolution spatiale sur le site de la campagne terrain SMAPVEX16-MB, situé dans une zone agricole du Manitoba. La méthode est basée sur une désagrégation de la température de brillance (TB) de SMAP, de 40 km à 1 km de résolution spatiale, en utilisant les données polarimétriques en bande C de Radarsat-2 corrigées de l’effet de la végétation (la contribution de surface : Ps), plus sensible à l’humidité du sol. La contribution de surface (Ps) est obtenue en appliquant la décomposition polarimétrique de Freeman-Durden. Le résultat de la désagrégation est une température de brillance à 1 km de résolution spatiale, qui est ensuite utilisée dans l’algorithme du Single Chanel Algorithm pour estimer l’humidité du sol à 1 km de résolution spatiale. En ce qui concerne l’estimation de l’humidité du sol, pour tous les dix champs considérés, nous avons obtenu les meilleurs résultats en utilisant les TBV : coefficients de corrélation de Pearson (R) compris entre 0,42 et 0,86, p-values comprises entre 0,003 et 0,27 et erreurs quadratiques moyennes (RMSE) comprises entre 0,02 m3.m -3 et 0,05 m3.m -3. Lorsque nous utilisons les TBH pour estimer l’humidité du sol, nous obtenons : R compris entre 0,39 et 0,75, p-values comprises entre 0,02 et 0,30 et RMSE comprises entre 0,02 m3.m -3 et 0,15 m3.m -3. Ce projet nous a permis d’implémenter une méthode innovatrice de combinaison de données micro-ondes actives et passives pour l’étude de l’humidité du sol. L’approche proposée utilise les Ps au lieu de σ^0 contrairement à la plupart des méthodes que l’on trouve dans la littérature depuis la détérioration du radar de SMAP.Abstract : Soil moisture plays a major role in the regulation of climate elements (precipitation, temperature, atmospheric H2O) and water balance. To study the soil moisture at a global scale, spaceborne microwave remote sensing has a great potential. In the case of the Soil Moisture Active Passive (SMAP) satellite, the initially developed methods provided three soil moisture products : active, passive and active-passive with a fine spatial resolution of 3 km, coarse 40 km and medium 9 km, respectively. However, six months after the launch of the satellite, its radar failed, preventing SMAP from generating soil moisture products at fine (3 km) and medium (9 km) spatial resolutions. Since then, research teams have studied the possibility of combining active and passive measurements with sensors installed on different platforms and operating at different frequencies. This project proposes a combined approach of active and passive microwave measurements of different satellites to estimate soil moisture at 1 km spatial resolution at the SMAPVEX16-MB field campaign site, located in an agricultural area of Manitoba. The method is based on a disaggregation of the brightness temperature (TB) of SMAP, from 40 km to 1 km spatial resolution, using Radarsat-2 polarimetric C-band data corrected for vegetation effects. These are represented by the surface contribution (Ps), which is more sensitive to soil moisture and extracted by applying the polarimetric decomposition of Freeman-Durden (Freeman and Durden, 1998) to Radarsat-2 data. Regarding the estimation of the soil moisture, for all the ten fields considered, we obtained the best results by using TBV: (Pearson correlation R between 0.42 and 0.86, p-values between 0.003 and 0.27, and root mean square errors (RMSE) between 0.02 m3.m -3 and 0.05 m3.m -3). When TBH was used to estimate soil moisture, the results were less accurate (R between 0.39 and 0.75 p-values between 0.02 and 0.30; and RMSE between 0.02 m3.m -3 and 0.15 m3.m -3). This project allowed us to implement an innovative methodology using Ps instead of 0 in contrast to most of the approaches combining active and passive microwave data for soil moisture estimation, since the failure of the radar onboard SMAP

Understanding and Improving the Soil Moisture Retrieval Algorithm under Space, Time and Heterogeneity

The spatial and temporal monitoring of soil moisture from remote sensing platforms plays a pivotal role in predicting the future food and water security. That is, improving soil moisture estimation at remote sensing platforms has remarkable impacts in the fields of meteorology, hydrology, agriculture, and global climate change. However, remote sensing of soil moisture for long is hindered by spatial heterogeneity in land surface variables (soil, biomass, topography, and temperature) which cause systematic and random errors in soil moisture retrievals. Most soil moisture improvement methods to date focused on the downscaling of either coarse resolution soil moisture or brightness temperature based on fine scale ancillary information of land surface variables. Comparatively little work has been done on improving the parameterization of most sensitive variables to radiative transfer model that impact soil moisture retrieval accuracy. In addition, the classic radiative transfer model assumes the vegetation and surface roughness parameters, as constant with space and time which undermines the retrieval accuracy. Also, it is largely elusive so far the discussion on the non-linearity of microwave radiative transfer model and its relationship with energy and water fluxes. In order to address the above mentioned limitations, this dissertation aims to develop and validate a soil moisture modeling framework with associated improved parameterizations for surface roughness and vegetation optical depth (VOD) in the homogeneous and heterogeneous environments. To this end, the following research work is specifically conducted: (a) conduct comprehensive sensitivity analysis on radiative transfer model with space, time and hydroclimates; (b) develop multi-scale surface roughness model which incorporates small (soil) and large (topography) surface undulations to improve soil moisture retrievals; (c) improve the parameterization of vegetation topical depth (VOD) using within-pixel biomass heterogeneity to improved soil moisture accuracy; (d) investigate the non-linearity in microwave radiative transfer model, and its association with thermal energy fluxes. The results of this study showed that: (a) the total (linear + non-linear) sensitivity of soil, temperature and biomass variables varied with spatial scale (support), time, and hydro climates, with higher non-linearity observed for dense biomass regions. This non-linearity is also governed by soil moisture availability and temperature. Among these variables, surface roughness and vegetation optical depth are most sensitive variables to radiative transfer model (RTM); (b) considering the spatial and temporal variability in parameterization of surface roughness and VOD has improved soil moisture retrieval accuracy, importantly in cropland and forest environments; and (c) the soil moisture estimated through evaporative fraction (EF) correlates higher with VOD corrected soil moisture

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space