Ground Soil Moisture Measurements Research Articles

Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples

Synthetic Aperture Radar (SAR) data is increasingly popular as a data source for global near-surface soil moisture mapping, but large-scale applications are still challenging due to the complex scattering process and the cumbersome data preprocessing. The emergence of deep learning methods has allowed advances in the remote sensing of large-scale surface parameters, but its application in SAR soil moisture retrieval has suffered from the availability of ground soil moisture measurements. Accordingly, this study proposed a cross-resolution transfer learning framework, with the assumption that sophisticated models for different spatial resolutions share a similar model architecture and trainable parameters. A robust high-resolution model can thus be trained with fewer samples by using coarse models. Accordingly, 25 deep learning models were pre-trained taking ∼387,000 Soil Moisture Active Passive (SMAP) Level-3 9 km enhanced passive soil moisture measurements as the truth, with an average validation RMSE of 0.03 m3/m3. They were then transferred to finer grids of 0.1–1 km using a small number of in-situ samples. A total of ∼190,000 daily soil moisture measurements from the international soil moisture network (ISMN) were used to evaluate the proposed framework in three scenarios. The results show that 1) 5000–6000 random samples are sufficient to achieve a target RMSE of 0.06 m3/m3; 2) training samples from a short period (2 or 4 months for Sentinel-1) of 2021 resulted in an overall RMSE of ∼0.068 m3/m3 in an independent period of 2016–2020; 3) the transfer learning also improved the retrieval accuracy (10–30% in relative) over areas without ground samples used for training but failed to yield an acceptable accuracy over mountainous areas. The promising results from this study confirmed the effectiveness of using “pre-trained models + scenario specific models” for regional to global soil moisture retrieval from Sentinel-1.

The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance (NTR) and the normalized difference vegetation index (NDVI) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the NTR−NDVI trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers.

High-Resolution Quantitative Retrieval of Soil Moisture Based on Multisource Data Fusion with Random Forests: A Case Study in the Zoige Region of the Tibetan Plateau

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m3/m3 and 0.3943 m3/m3. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.

A COMPARISON OF TREE-BASED REGRESSION MODELS FOR SOIL MOISTURE ESTIMATION USING SAR DATA

Abstract. Soil moisture content plays a pivotal role in biomass development of vegetation coverage at various growth stages. Moisture content of the soil is considered as a crucial parameter for agricultural studies which directly leads to higher fertility rate. Remote sensing techniques, specifically Synthetic Aperture Radar (SAR) sensors, provides suitable opportunity for continuous soil moisture monitoring at various spatial and temporal resolutions. In this study, field campaigns conducted to measure soil surface parameters, including soil moisture and roughness, synchronized with Sentinel-1 pass over an agricultural region near Mohammadshahr, Iran. Fieldwork for soil moisture sampling have done during plants’ (canola and winter wheat) growth stages. The Gradient Boosted Regression Tree (GBRT), eXtreme Gradient Boosted (XGB), and Random Forest (RF) machine learning algorithms were employed to model the relationship between the ground measured soil moisture and polarimetric SAR derived features from Sentinel-1 imageries. The results showed promising results obtained for soil moisture estimation using the dual-polarized SAR dataset over crop-covered agricultural fields with R2 = 0.95 and RMSE = 0.023 m3 m−3 using the GBRT regression model.

Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product

ABSTRACT Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product.

Soil moisture estimation using RISAT-1 and SENTINEL-1 data using modified Dubois model in comparison with averaged NDVI

In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. It involves retrieving the backscattering coefficient from RISAT-1 and SENTINEL-1 datasets to derive the surface roughness and soil moisture conditions. The estimated soil moisture retrieved from microwave SAR parameters is validated with field measurements provides soil moisture spatial variability over different land use classes and bare soil condition. The RISAT-1 derived soil moisture has R2 = 0.53, whereas SENTINEL-1 shows R2 = 0.84. It also confirms the possibility of two different polarization σ°HH and σ°VV backscatter involving MDM. It observes that SENTINEL-1 was found well correlated with ground-measured soil moisture. Also, the averaged NDVI sounds reliable with soil moisture ratio, which helps to understand the impact of agricultural drought monitoring.

SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network

Soil moisture (SM) is the primary variable regulating the soil temperature (ST) differences between daytime and night-time, providing protection to crop rooting systems against sharp and sudden changes. It also has a number of practical applications in a range of disciplines. This study presents an approach to incorporating the effect of ST for the accurate estimation of SM using Earth Observation (EO) data from NASA’s SMAP sensor, one of the most sophisticated satellites currently in orbit. Linear regression analysis was carried out between the SMAP-retrieved SM and ground-measured SM. Subsequently, SMAP-derived ST was incorporated with SMAP-derived SM in multiple regression analysis to improve the SM retrieval accuracy. The ability of the proposed method to estimate SM under different seasonal conditions for the year 2016 was evaluated using ground observations from the Wales Soil Moisture Network (WSMN), located in Wales, United Kingdom, as a reference. Results showed reduced retrieval accuracy of SM between the SMAP and ground measurements. The R2 between the SMAP SM and ground-observed data from WSMN was found to be 0.247, 0.183, and 0.490 for annual, growing and non-growing seasons, respectively. The values of RMSE between SMAP SM and WSMN observed SM are reported as 0.080 m3m−3, 0.078 m3m−3 and 0.010 m3m−3, with almost zero bias values for annual, growing and non-growing seasons, respectively. Implementation of the proposed scheme resulted in a noticeable improvement in SSM prediction in both R2 (0.558, 0.440 and 0.613) and RMSE (0.045 m3m−3, 0.041 m3m−3 and 0.007 m3m−3), with almost zero bias values for annual, growing and non-growing seasons, respectively. The proposed algorithm retrieval accuracy was closely matched with the SMAP target accuracy 0.04 m3m−3. In overall, use of the new methodology was found to help reducing the SM difference between SMAP and ground-measured SM, using only satellite data. This can provide important assistance in improving cases where the SMAP product can be used in practical and research applications.

Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy–Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium

The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm−2 are found for ground measured soil moisture and latent heat flux, respectively.

The spatio-temporal soil moisture variation along the major tributaries of Zambezi River in the Mbire District, Zimbabwe

This study uses the Surface Energy Balance System (SEBS) and the TOPographic driven MODEL (TOPMODEL) in estimating soil moisture for Mbire district in Zimbabwe and validates these estimates with ground based gravimetric measurements baseded on fifty-four soil moisture sampling sites. Five atmospherically corrected MODIS images for the period 2013were processed and used to estimate evapotransiration. The result of the relative evaporation from the SEBS algorithm was multiplied by the average of the porosity and the field capacity to get the spatial and temporal soil moisture estimates. The TOPMODEL rainfall-runoff model whose land surface inputs were obtained from remote sensing was calibrated with runoff data for the period ranging 2008−2013. After successful calibration the model was used to to simulate spatial and temporal soil moisture estimates and compare with insitu-measured soil moisture from the fifty-two sampling sites. An upscaling procedure to improve the comparison between soil moisture retrieval point measurements, remote sensing and TOPMODEL outputs was accomplished by the use of geostatistical tools. Land suitability analysis for flood recession farming was performed using soil moisture maps, distance from stream network, vertical channel distance, and land use/cover datasets. The model performance indicators for TOPMODEL simulations show an acceptable match with measured discharge, (NSE = 0.765 and 0.812 and PBIAS= -6.04 % and -10.5 % for Manyame and Angwa sub catchments respectively). Results show that the SEBS approach show spatial and temporal soil moisture variability across the Mbire district simulated. There is a strong relationship (R2 = 0.796) between upscaled insitu-based soil moisture measurements and SEBS based techniques for the period of March to July 2013. The study further revealed that there is a fair relationship (R2 = 0.60) between upscaled ground soil moisture measurements and hydrological modelling (TOPMODEL) for the same period. TOPMODEL soil moisture increases towards the Angwa and Manyame river tributaries. Results also show that approximately 22800 ha of Mbire district has suitable to moderately suitable flood recession farming area. The study concludes that the use of geostatistical approaches, remote sensing and hydrologic models is promising in planning and management of soil moisture and water resources in data scarce and water limited environments.

Toward P-Band Passive Microwave Sensing of Soil Moisture

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) and European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 $\text{m}^{3}/\text{m}^{3}$ target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band (wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top $\sim 10$ -cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

Evaluation of modified Dubois model for estimating surface soil moisture using dual polarization RISAT-1 C-band SAR data

The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with ‘s’ parameter for retrieval of dielectric constant which is further used in Topp’s model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation.

Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements.

The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product’s coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m3/m3 over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies.

MODIS remote sensing approaches to monitoring soil moisture in Tibet, China

ABSTRACTSatellite remote sensing from space offers a unique way to monitor surface soil moisture on a large geographical scale. In this work, the general approaches to monitoring surface soil moisture for Tibet area were developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observations. The result shows that MODIS Vegetation Supply Water Index (VSWI) and band-7 reflectance are significantly correlated with ground measured soil moisture and MODIS band 7 is a simple and more effective method to monitor soil moisture in Tibet with higher spatial resolution at 500 m in comparison with VSWI at 1000 m resolution.

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields.

Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient (). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient () and the AIEM-simulated backscattering coefficient (). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

Investigation of SMAP Active–Passive Downscaling Algorithms Using Combined Sentinel-1 SAR and SMAP Radiometer Data

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal.

A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain

In this research, the active, passive and combined Climate Change Initiative (CCI) Soil Moisture (SM) products were evaluated in comparison with in situ SM measurements from five networks in Spain that have different spatial and temporal scales, densities and environmental conditions. Three of these networks, namely Rinconada, Morille and the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), are small- to medium-scale networks and have high station densities, whereas the other two (Inforiego and FluxNet) are sparse and large-scale networks.The results of the comparisons with the former v02.2 version (before the inclusion of the SM retrieved by the Soil Moisture and Ocean Salinity mission, SMOS, in the CCI dataset) showed that the combined CCI performed better than the active or passive, affording correlation coefficients (R) above 0.8 and errors between 0.03 and 0.08 m3 m−3 for the area-average, with biases close to zero. Regarding the land uses and environmental conditions, the stations that were located in the agricultural areas and some forested areas showed the best results, and those that were located in pasture and certain specific agricultural locations showed the poorest results.To test the opportunity of including SMOS in CCI, both datasets were compared over the same areas and coincident periods. After the results, the combined CCI and SMOS SM products matched very well (R = 0.83 on average), although the SMOS and CCI under- and overestimate the ground soil moisture measurements, respectively.Finally, the new version of the combined CCI (v03.2, after including SMOS) showed similar correlations to the previous one, but it significantly reduced the bias, leading to slightly lower errors (RMSD and cRMSD). Hence, it was shown that including SMOS in the CCI database enhanced its performance.The results in this work may improve knowledge of the CCI SM and its potential applications.

Information theoretic evaluation of satellite soil moisture retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surface soil moisture measurements over continental and global scales. A consistent assessment of the errors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluation of soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAP instruments using information theory-based metrics. These metrics rely on time series analysis of soil moisture retrievals for estimating the measurement error, level of randomness (entropy) and regularity (complexity) of the data. The results of the study indicate that the measurement errors in the remote sensing retrievals are significantly larger than that of the ground soil moisture measurements. The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levels of complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity of remotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT, SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content.

Comparison of SMMI, PDI and its applications in Shendong mining area

Soil moisture has become the dominant factor in the Shendong mining area for vegetation growth and recovery. Soil moisture monitoring index (SMMI) and the Perpendicular Drought Index (PDI) were widely used to monitor soil moisture, which were all obtained from the NIR-Red spectral space. To validate whether SMMI and PDI were all can be applied to evaluate the soil moisture conditions, SPOT-6 images on October 5, 2015 in Shendong mining area are used to calculate the SMMI and PDI over ground measuring points. The SMMI and PDI are then compared to ground-measured soil moisture data with the satellite overpass. This study concludes that the SMMI and PDI provide quite similar results for soil moisture monitoring and they are quite suitable to assess the soil moisture statue in 0-5-cm-depth. The highest correlation of R2=0.54 for the SMMI and R2=0.52 for the PDI is obtained when compared with relative soil moisture from 0 to 5 cm soil depth. It is evident from the results showing the spatial distribution of drought in Shendong mining area that the SMMI and PDI are highly accordant with landform types and underlying.