Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics

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

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

Statistical analysis and combination of active and passive microwave remote sensing methods for soil moisture retrieval

Knowledge about soil moisture and its spatio-temporal dynamics is essential for the improvement of climate and hydrological modeling, including drought and flood monitoring and forecasting, as well as weather forecasting models. In recent years, several soil moisture products from active and passive microwave remote sensing have become available with high temporal resolution and global coverage. Thus, the validation and evaluation of spatial and temporal soil moisture patterns are of great interest, for improving soil moisture products as well as for their proper use in models or other applications. This thesis analyzes the different accuracy levels of global soil moisture products and identifies the major influencing factors on this accuracy based on a small catchment example. Furthermore, on global scale, structural differences betweenthe soil moisture products were investigated. This includes in particular the representation of spatial and temporal patterns, as well as a general scaling law of soil moisture variability with extent scale. The results of the catchment scale as well as the global scale analyses identified vegetation to have a high impact on the accuracy of remotely sensed soil moisture products. Therefore, an improved method to consider vegetation characteristics in pasive soil moisture retrieval from active radar satellite data was developed and tested. The knowledge gained by this thesis will contribute to improve soil moisture retrieval of current and future microwave remote sensors (e.g. SMOS or SMAP)

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Statistical analysis and combination of active and passive microwave remote sensing methods for soil moisture retrieval

Knowledge about soil moisture and its spatio-temporal dynamics is essential for the improvement of climate and hydrological modeling, including drought and flood monitoring and forecasting, as well as weather forecasting models. In recent years, several soil moisture products from active and passive microwave remote sensing have become available with high temporal resolution and global coverage. However, for the improvement of a soil moisture product and for its proper use in models or other applications, validation and evaluation of its spatial and temporal patterns are of great importance. In chapter 2 the Level 2 Soil Moisture and Ocean Salinity (SMOS) soil moisture product and the Advanced Scatterometer (ASCAT) surface soil moisture product are validated in the Rur and Erft catchments in western Germany for the years 2010 to 2012 against a soil moisture reference created by a hydrological model, which was calibrated by in situ observations. Correlation with the modeled soil moisture reference results in an overall correlation coefficient of 0.28 for the SMOS product and 0.50 for ASCAT. While the correlation of both products with the reference is highly dependent ontopography and vegetation, SMOS is also strongly influenced by radiofrequency interferences in the study area. Both products exhibit dry biases as compared to the reference. The bias of the SMOS product is constant in time, while the ASCAT bias is more variable. For the investigation of spatio temporal soil moisture patterns in the study area, a new validation method based on the temporal stability analysis is developed. Through investigation of mean relative differences of soil moisture for every pixel the temporal persistence of spatial patterns is analyzed. Results indicate a lower temporal persistence for both SMOS and ASCAT soil moisture products as compared to modeled soil moisture. ASCAT soil moisture, converted to absolute values, shows highest consistence of ranks and therefore most similar spatio-temporal patterns with the soil moisture reference, while the correlation of ranks of mean relative differences is low for SMOS and relative ASCAT soil moisture products. Chapter 3 investigates the spatial and temporal behavior of the SMOS and ASCAT soil moisture products and additionally of the ERA Interim product from a weather forecast model reanalysis on global scale. Results show similar temporal patterns of the soil moisture products, but high impact of sensor and retrieval types and therefore higher deviations in absolute soil moisture values. Results are more variable for the spatial patterns of the soil moisture products: While the global patterns are similar, a ranking of mean relative differences reveals that ASCAT and ERA Interim products show most similar spatial soil moisture patterns, while ERA and SMOS products show least similarities. Patterns are generally more similar between the products in regions with low vegetation. [...