Coarse-resolution Soil Moisture Research Articles

Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm

It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m3/m3]).

Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass.

Study of downscaling method for coarse-resolution soil moisture product using combined AIRSAR and PALS Radiometer data

As an important component of soil, soil moisture plays an important role in material and energy transmission between hydrosphere, biosphere and atmosphere. It is of great significance to accurately understand the spatial and temporal distribution of soil moisture in large areas. At present, the spaceborne passive microwave remote sensing could achieve the accurate surface soil moisture of large areas, but at the low spatial resolution (10-50km). Therefore, its application was restricted. In this paper, a downscaling algorithm of passive microwave soil moisture products based on C-band backscattering coefficient is developed, and the algorithm is verified by L-band PALS Radiometer data and C-band AIRSAR data, the correlation coefficient R between the soil moisture content and the measured value is 0.71, and the root mean square error is 0.053cm3 · cm−3. According to the verification results of measured data, the downscaling algorithm of soil moisture retrieval products based on C-band backscattering coefficient is reliable.

Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

AbstractThe coarse resolution soil moisture (SM) data from NASA SMAP mission have been steadily produced with the expected performance since April 2015. These coarse resolution observations could be downscaled to fine resolution using fine scale observations of SM sensitive quantities from existing satellite sensors. For operational users who need near‐real‐time (NRT) high resolution SM data, the downscaling approach should be feasible for operational implementation, requiring limited ancillary information and primarily depending on readily available satellite observations. Based on these principles, nine potential candidate downscaling schemes were selected for developing an optimal downscaling strategy. Using remotely sensed land surface temperature (LST) and enhanced vegetation index (EVI) observations, the optimal downscaling approach was tested for operational producing a NRT 1 km SM data product from SMAP. Comprehensive assessments on the 1 km SM product were conducted based on agreement statistics with in‐situ SM measurements. Statistical results show that the accuracy of the original coarse spatial resolution SMAP SM product can be significantly improved by 8% by the downscaled 1 km SM. With respect to the in‐situ measurements, the 1 km SM mapping capability developed here presents a clear advantage over the SMAP/Sentinel SM data product; and it also provides better data availability for users. This study suggests that a NRT 1 km SMAP SM data product could be routinely generated from SMAP at the centre for Satellite Applications and Research of NOAA NESDIS for operational users.

A review of spatial downscaling of satellite remotely sensed soil moisture

AbstractSatellite 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.

Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India

Detection and monitoring of seasonal agricultural drought at sub-regional scale is a complex theme due to inefficient spatiotemporal indicators. This study presents a new time-based function of spaceborne soil moisture as an efficient indicator. Bundelkhand of Central India, a frequently agricultural drought affected region, was used as the study area. Rabi agricultural season (October–May) being the dominant agricultural return period, was chosen as the study period. Coarse resolution soil moisture (SMc) obtained from European space agency under climate change initiative program was spatially downscaled (SMd) to meet spatial scale at sub-regional level with overall root-mean-square error under 0.065 cm3/cm3. Indirect validation of SMd was done using temporal impact of rainfall/dry spell on SMd and spatiotemporal impact of SMd on vegetation condition. SMd was found to agree with phenomenon as expected in natural processes and hence it was assumed to be validated. The time-based function derived from spatiotemporal SMd (FSMs) was found to be better related with fluctuations in seasonal crop yield (Ys) at district level as compared to a similar function (FVCIs) derived using vegetation condition index (VCI) from Moderate Resolution Imaging Spectroradiometer. FSMs outperformed FVCIs having better correlation coefficient (R ≥0.8) and Nash–Sutcliffe efficiency coefficient (NSE) than FVCIs for most of the districts. Unlike FVCIs, it also efficiently detected the lowest and highest Ys for majority of the districts representing better association with agricultural drought. Subsequently, frequent soil moisture deficit areas were mapped by using FSMs to visualize the spatiotemporal severity of agricultural drought in the region during Rabi season.

Estimating error cross‐correlations in soil moisture data sets using extended collocation analysis

AbstractGlobal soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite‐based soil moisture data into water balance models or merging multisource soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse‐resolution soil moisture data sets, the estimation of error cross covariances remains an unresolved challenge. Here we propose a method—referred to as extended collocation (EC) analysis—for estimating error cross‐correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross‐correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross‐correlation levels. Applied to real soil moisture retrievals from Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) C‐band and X‐band observations together with advanced scatterometer (ASCAT) retrievals, modeled data from Global Land Data Assimilation System (GLDAS)‐Noah and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong nonzero error cross‐correlations between the two AMSR‐E products. Against expectation, nonzero error cross‐correlations are also found between ASCAT and AMSR‐E. We conclude that the proposed EC method represents an important step toward a fully parameterized error covariance matrix for coarse‐resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme.

Soil Moisture Mapping Using Combined Active/Passive Microwave Observations Over the East of the Netherlands

A coarse resolution soil moisture product is downscaled to 1, 5, and 10 km using synthetic aperture radar (SAR) observations acquired over the east of the Netherlands. The combination of phased array L-band SAR (PALSAR) backscatter and VUA-NASA C-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product is adopted to mimic the radar/radiometer setup as will be available from NASA's soil moisture active passive (SMAP) mission. The validation of retrievals is based on measurements collected by a sparse network of 20 stations distributed across 50 × 75 km study area selected as one of the key validation sites for the SMAP soil moisture products. Reasonable agreements between the measurements and soil moisture retrieved at 1-, 5-, and 10-km resolution are found that lead to coefficients of determination of 0.37, 0.36, and 0.36, respectively. The retrievals, however, severely overestimate the measured soil moisture, which is attributed to the well-known positive bias of the selected AMSR-E product. After bias-correction, root mean squared differences reach as low as 0.046 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for individual locations and are 0.067, 0.068, and 0.069 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> on average for the soil moisture retrieved at 1-, 5-, and 10-km resolutions, respectively. These error levels do not satisfy SMAP's targeted accuracy of 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , but the radar/radiometer setup as well as the characterization of the soil moisture conditions representative are not optimal. On the other hand, it is demonstrated that the sequence of soil moisture maps does capture valuable hydrological and hydrometeorological information.

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

Abstract. Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October–April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct–Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

Assessment of SMOS soil moisture retrieval parameters using tau–omega algorithms for soil moisture deficit estimation

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Approximate estimation of soil moisture from NDVI and Land Surface Temperature over Andong region, Korea

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The 0.25°coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model

Microwave remote sensing and mesoscale weather models have high potential to monitor global hydrological processes. The latest satellite soil moisture dedicated mission SMOS and WRF-NOAH Land Surface Model (WRF-NOAH LSM) provide a flow of coarse resolution soil moisture data, which may be useful data sources for hydrological applications. In this study, four data fusion techniques: Linear Weighted Algorithm (LWA), Multiple Linear Regression (MLR), Kalman Filter (KF) and Artificial Neural Network (ANN) are evaluated for Soil Moisture Deficit (SMD) estimation using the SMOS and WRF-NOAH LSM derived soil moisture. The first method (and most simplest) utilizes a series of simple combinations between SMOS and WRF-NOAH LSM soil moisture products, while the second uses a predictor equation generally formed by dependent variables (Probability Distributed Model based SMD) and independent predictors (SMOS and WRF-NOAH LSM). The third and fourth techniques are based on rigorous calibration and validation and need proper optimisation for the final outputs backboned by strong non-linear statistical analysis. The performances of all the techniques are validated against the probability distributed model based soil moisture deficit as benchmark; estimated using the ground based observed datasets. The observed high Nash Sutcliffe Efficiencies between the fused datasets with Probability Distribution Model clearly demonstrate an improved performance from the individual products. However, the overall analysis indicates a higher capability of ANN and KF for data fusion than the LWA or MLR approach. These techniques serve as one of the first demonstrations that there is hydrological relevant information in the coarse resolution SMOS satellite and WRF-NOAH LSM data, which could be used for hydrological applications.

The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products

Abstract. A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite and model products of soil moisture and soil temperature. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) consists of three regional scale in-situ reference networks, including the Naqu network in a cold semiarid climate, the Maqu network in a cold humid climate and the Ngari network in a cold arid climate. These networks provide a representative coverage of the different climate and land surface hydrometeorological conditions on the Tibetan plateau. In this paper the details of the Tibet-Obs are reported. To demonstrate the uniqueness of the Tibet-Obs in quantifying and explaining soil moisture uncertainties in existing coarse satellite products, an analysis is carried out to assess the reliability of several satellite products for the Naqu and the Maqu network areas. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

Impact of Soil Moisture Dynamics on ASAR σo Signatures and Its Spatial Variability Observed over the Tibetan Plateau.

This paper reports on the analysis of a 2.5 year-long time series of ASAR wide swath mode (WSM) observations for characterizing the soil moisture dynamics. The employed ASAR WSM data set consists of 152 VV-polarized scenes acquired in the period between April 2005 and September 2007 over the Naqu river basin located on the Tibetan Plateau. For four different spatial domains, with areas of 30×30 km2, 5×5 km2 and (two domains of) 1×1 km2, the mean backscatter (σo) and the standard deviation (stdev) have been computed for each ASAR acquisition. Comparison of the mean σo values with the stdev values results in a specific triangular distribution of data points for all spatial domains. Analysis of the mean σo and stdev with respect to in-situ soil moisture measurements demonstrates that this triangular shaped distribution can be explained by soil moisture dynamics during monsoon and winter periods. This shows that the relationship between the spatial mean soil moisture and variability is not uniquely defined and may change throughout seasons. Downscaling of coarse resolution soil moisture products should, therefore, be ideally based on additional near real time data sources. In this context, the presented results could form a basis for the development of SAR-based soil moisture downscaling methodologies.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R2) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR).

The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R²) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.

The Value of Coarse-Scale Soil Moisture Observations for Regional Surface Energy Balance Modeling

Using high-resolution (1 km) hydrologic modeling of the 575 000-km2 Red‐Arkansas River basin, the impact of spatially aggregating soil moisture imagery up to the footprint scale (32‐64 km) of spaceborne microwave radiometers on regional-scale prediction of surface energy fluxes is examined. While errors in surface energy fluxes associated with the aggregation of soil moisture are potentially large ( . 50 Wm 22), relatively simple representations of subfootprint-scale variability are capable of substantially reducing the impact of soil moisture aggregation on land surface model energy flux predictions. This suggests that even crude representations of subgrid soil moisture statistics obtained from statistical downscaling procedures can aid regional-scale surface energy flux prediction. One possible soil moisture downscaling procedure, based on an assumption of spatial scaling (i.e., a power-law relationship between statistical moments and scale), is demonstrated to improve TOPmodel-based Land‐Atmosphere Transfer Scheme (TOPLATS) prediction of grid-scale surface energy fluxes derived from coarse-resolution soil moisture imagery.