Physics-Based Retrieval of Surface Roughness Parameters for Bare Soils from Combined Active-Passive Microwave Signatures

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two default parameters to describe the surface roughness are the standard deviation of the surface height variation and the surface correlation length with its corresponding autocorrelation function. Both parameters (,) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve and by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that and can be estimated simultaneously when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured -values, and of 3.13 cm and 3.04 cm for -values. The first validation of the results reveals that the influence of the autocorrelation function, needed within the retrieval, is distinct

Simultaenous Retrieval of Surface Roughness Parameters from Combined Active-Passive SMAP Observations

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). The algorithm is tested with the global active-passive microwave observations of the SMAP mission. The developed roughness retrieval algorithm shows independence of permittivity for > 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of non-vegetated areas become evident on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined and confirm rather smooth to semi-rough surface roughness patterns with small vertical RMS heights and corresponding higher horizontal correlation lengths

Simultaenous Retrieval of Surface Roughness Parameters from Combined Active-Passive SMAP Observations

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). The algorithm is tested with the global active-passive microwave observations of the SMAP mission. The developed roughness retrieval algorithm shows independence of permittivity for e_s > 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of non-vegetated areas become evident on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined s and l confirm rather smooth to semi-rough surface roughness patterns with small vertical RMS heights and corresponding higher horizontal correlation lengths

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

A Typical Review of Current and Prospective Microwave and Optical Remote Sensing Datasets for Soil Moisture Retrieval

Soil Moisture content is a vital indicator of both the weather and the water cycle. It has been a long-standing difficulty for the field of remote sensing to make sense of soil moisture's spatial and temporal distribution. For over five decades, researchers across the world have exclusively investigated the optical and microwave datasets for estimating soil moisture by developing various models, and algorithms. Nevertheless, challenges are faced in the consistent retrieval of SM at local, and global scales with higher accuracy in space and time resolution. The review was conducted in-depth, looking at the methods using optical and microwave data to determine soil moisture, and outlining the benefits and drawbacks considering the current needs.  With this research, a new age of widespread use of space technology for remote sensing of soil moisture has been ushered in. The study also acknowledges the scientific challenges of utilizing remote sensing datasets for soil moisture measurement

Microwave Remote Sensing of Soil Moisture Science and Applications

Soil moisture is a fundamental link between global water and carbon cycles and has major applications in predicting natural hazards such as droughts and floods (National Research Council, 2007). From precipitation data, soil wetness can be estimated by hydrological land-surface models. In the United States, preliminary precipitation data are based on measurements gathered from many active stations nationwide each month, and it takes 3–4 months to assemble final, quality-controlled data. In the western United States, some climate divisions may have no stations reporting in a particular month or may lack first- or second-order stations, and significant blockages by mountains limit the capability of precipitation measurement by surface rain radars (Maddox et al., 2002)

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

An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets