Potential soil moisture products from the aquarius radiometer and scatterometer using an observing system simulation experiment

Using an observing system simulation experiment (OSSE), we investigate the potential soil moisture retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in soil moisture retrievals and identify the sources that could cause those errors. The OSSE system includes (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model, and (iv) an inverse soil moisture retrieval model. We execute the OSSE over a 1000 × 2200 km2 region in the central United States, including the Red and Arkansas river basins. Spatial distributions of soil moisture retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSEs of scatterometer retrievals are scattered over the entire domain. The temporal variations of soil moisture are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol/vol for radiometer and scatterometer, respectively. Over the densely vegetated region, soil moisture exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67, respectively, and RMSEs 9.49% and 6.09% vol/vol respectively. The domain-averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving soil moisture. The analysis also demonstrates that the accuracy of the retrieved soil moisture is affected by vegetation coverage and spatial aggregation

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

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

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

Hydrologic data assimilation of multi-resolution microwave radiometer and radar measurements using ensemble smoothing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (leaves 197-208).Previously, the ensemble Kalman filter (EnKF) has been used to estimate soil moisture and related fluxes by merging noisy low frequency microwave observations with forecasts from a conventional though uncertain land surface model (LSM). Here it is argued that soil moisture estimation is a reanalysis-type problem and thus smoothingis more appropriate than filtering. An ensemble moving batch smoother, an extension of the EnKF in which the state vector is distributed in time, is used to merge synthetic ESTAR observations with modeled soil moisture. Results demonstrate that smoothing can improve over filtering. However, augmentation of the state vector increases the computational cost significantly, rendering this approach unsuitable for spatially distributed problems. The ensemble Kalman smoother (EnKS) is an inexpensive alternative as the costly computations are already performed in the EnKF which provides the initial guess. It is used to assimilate observed L-band radiobrightness temperatures during the Southern Great Plains Experiment 1997. Estimated surface and root zone soil moisture is evaluated using gravimetric measurements and flux tower observations. It is shown that the EnKS can be implemented as a fixed-lag smoother with the required lag determined by the memory in subsurface soil moisture. In a synthetic experiment over the Arkansas-Red river basin, "true" soil moisture from the TOPLATS model is used to generate synthetic Hydros observations which are subsequently merged with modeled soil moisture from the Noah LSM using the EnKS.(cont.) It is shown that the EnKS can be used in a large problem, with a spatially distributed state vector, and spatially-distributed multi-resolution observations. This EnKS-based framework is used to study the synergy between passive and active observations, which have different resolutions and error distributions.by Susan Catherin Dunne.Ph.D

An observing system simulation experiment for soil moisture measurements from the SMAP radiometer

Thesis (S.B. in Environmental Engineering Science)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 57-61).The Soil Moisture Active Passive (SMAP) satellite, to be launched in 2013, will use both radiometer and radar data to estimate soil moisture. Improved soil moisture knowledge has many applications in hydroclimatology, numerical weather prediction, flood forecasting, and human health. In this thesis, an observing system simulation experiment (OSSE) was used to study the error structure of radiometer measurements using two different retrieval algorithms. In an OSSE, geophysical fields are used to create a model of surface emission, which is coupled to an orbital sampling module and proposed retrieval algorithms. Comparing output from the retrieval algorithm to the starting soil moisture values demonstrates retrieval error. Significant uncertainty remains about the optimal representation of the effect of dielectric mixing, soil roughness, and vegetation opacity on radiometric emissions at a given soil moisture. The effect of this uncertainty on retrieval algorithms is studied by using different representations for each term in the forward and retrieval modules of the OSSE. Uncertainty due to roughness causes less error than errors in dielectric mixing and vegetation opacity treatment. In both algorithms, the retrieval shows a spatially variable bias, which is particularly large when using a single-polarization retrieval algorithm. The spatial and temporal variation of the bias, and the implications for characterization and removal of this bias as a possible error reduction strategy, are discussed.by Alexandra Georges Konings.S.B.in Environmental Engineering Scienc

Multiscale soil moisture retrievals from microwave remote sensing observations

Memoria de tesis doctoral presentada por María Piles Guillem para optar al grado de Doctora por la Universitat Politècnica de Catalunya (UPC), realizada bajo la dirección del Dr. Adriano Camps y de la Dra. Mercè Vall-llossera.-- 159 pages[EN] Soil moisture is a key state variable of the Earth’s system; it is the main variable that links the Earth’s water, energy and carbon cycles. Soil moisture variations affect the evolution of weather and climate over continental regions, and accurate observations of the Earth’s changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Ph.D. Thesis focuses on measuring the Earth’s surface soil moisture from space at a global and regional scale. [...][ES] La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos de agua del planeta, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. [...]This work has been funded by the Spanish Ministry of Science and Education under the FPU grant AP2005-4912 and projects ESP2007-65667-C04-02 and AYA2008-05906-C02-01/ESPPeer Reviewe

Global evaluation of SMAP/Sentinel-1 soil moisture products

MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Peer ReviewedPostprint (published version

Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing

International audienceMicrowave remote sensing offers emerging capabilities to monitor global hydrological processes. Instruments like the two dedicated soil moisture missions SMOS and HYDROS or the Advanced Scatterometer onboard METOP will provide a flow of coarse resolution microwave data, suited for macro-scale applications. Only recently, the scatterometer onboard of the European Remote Sensing Satellite, which is the precursor instrument of the Advanced Scatterometer, has been used successfully to derive soil moisture information at global scale with a spatial resolution of 50 km. Concepts of how to integrate macro-scale soil moisture data in hydrologic models are however still vague. In fact, the coarse resolution of the data provided by microwave radiometers and scatterometers is often considered to impede hydrological applications. Nevertheless, even if most hydrologic models are run at much finer scales, radiometers and scatterometers allow monitoring of atmosphere-induced changes in regional soil moisture patterns. This may prove to be valuable information for modelling hydrological processes in large river basins (>10 000 km2. In this paper, ERS scatterometer derived soil moisture products are compared to measured runoff of the Zambezi River in south-eastern Africa for several years (1992?2000). This comparison serves as one of the first demonstrations that there is hydrologic relevant information in coarse resolution satellite data. The observed high correlations between basin-averaged soil moisture and runoff time series (R2>0.85) demonstrate that the seasonal change from low runoff during the dry season to high runoff during the wet season is well captured by the ERS scatterometer. It can be expected that the high correlations are to a certain degree predetermined by the pronounced inter-annual cycle observed in the discharge behaviour of the Zambezi. To quantify this effect, time series of anomalies have been compared. This analysis showed that differences in runoff from year to year could, to some extent, be explained by soil moisture anomalies