SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch

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

Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %.Peer ReviewedPostprint (published version

Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula

The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDición de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (¿. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version

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

Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm

Soil moisture is important to agriculture, weather, and climate. Current soil moisture networks measure at single points, while large spatial averages are needed for some crop, weather, and climate models. Large spatial average soil moisture can be measured by microwave satellites. Two missions, the European Space Agency\u27s Soil Moisture Ocean Salinity mission (SMOS) and NASA\u27s Soil Moisture Active Passive mission (SMAP), can or will measure L-band microwave radiation, which can see through denser vegetation and deeper in to the soil than previous missions that used X-band or C-band measurements. Both SMOS and SMAP require knowledge of vegetation optical thickness (τ) to retrieve soil moisture. SMOS is able to measure τ directly through multi-angular measurements. SMAP, which will measure at a single incidence angle, requires an outside source of τ data. The current SMAP baseline algorithm will use a climatology of optical vegetation measurements, the normalized difference vegetation index (NDVI), to estimate τ. SMAP will convert the NDVI climatology to vegetation water content (VWC), then convert VWC to τ through the b parameter. This dissertation aimed to validate SMOS τ using county crop yield estimates in Iowa. SMOS τ was found to be noisy while still having a clear response to vegetation. Counties with higher yields had higher increases in $tau; over growing seasons, so it appears that SMOS τ is valid during the growing season. However, SMOS τ had odd behavior outside of growing seasons which can be attributed to soil tillage and residue management. Next, this dissertation attempted to estimate values of the b parameter at the satellite scale using SMOS τ data, county crop yields, and allometric relationships, such as harvest index. A new allometric relationship was defined, theta_gv_max, which is the ratio of maximum VWC to maximum dry biomass. While uncertainty in the estimated values of b was large, the values were close in magnitude to those found in literature for field-based studies. Finally, this dissertation compared SMOS τ to τ from SMAP\u27s NDVI-based algorithm. At the peak of the growing season, SMAP τ was similar in timing to SMOS τ, while SMAP τ was larger in magnitude than SMOS τ. The larger SMAP τ could be attributed to SMAP\u27s handling of vegetation scattering in its soil moisture retrieval algorithm. For one example case, the difference between SMAP τ and SMOS τ at the peak of the growing season did not appear to cause a large difference in retrieved soil moisture

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. 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)

The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

AbstractAccurate soil moisture information is critically important for hydrological modelling and natural hazards (landslide & debris flow). However, its effective utilisation in those areas is still in a state of infancy. This paper focuses on exploring the advances and potential issues in current application of satellite soil moisture observations in hydrological modelling. It has proposed that hydrological application of soil moisture data requires two inter-connected components: 1) soil moisture data relevant to hydrology, and 2) appropriate hydrological model structure compatible with such data. In order to meet these two requirements, the following three research tasks are suggested: the first is to carry out comprehensive evaluations of satellite soil moisture observations for hydrological modelling; the second is that the soil moisture representations in hydrological models may need to be modified so that they are more compatible with the real field soil moisture variations; and the third is that a soil moisture product (i.e., soil moisture deficit) directly applicable to hydrological modelling should be developed