Comprehensive assessments on the reliability of remotely sensed soil moisture products are undeniably essential for their advancement and application. With the establishment of extensive dense ...networks across the globe, mismatches between satellite footprints and ground single-point observations can be feasibly relieved. In this study, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two Soil Moisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2) and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks. Distinguished from previous studies, several perturbing factors comprising the surface temperature, vegetation optical depth (VOD), surface roughness and spatial heterogeneity were taken into account in this investigation. Furthermore, products' skills under various climate regions were also evaluated.
Through the results, the SMAP product captures temporal trends of ground soil moisture, exhibiting an averaged R of 0.729, whereas for overall accuracy, ESA CCI outperformed other products with a slightly smaller ubRMSE of 0.041 m3 m−3 and a bias of −0.005 m3 m−3. This complementarity between SMAP and ESA CCI was further demonstrated under different climate conditions and can afford the reference of their integration for a more reliable global soil moisture product. Though some underestimations still exist, the newly developed SMOS- INRA-CESBIO (SMOS-IC) was illustrated to gain considerable upgrades with regard to R and ubRMSE compared to SMOS-L3 product, especially in dense VOD conditions achieving the highest R compared to other products.
Generally, the underestimations of the European Centre for Medium-Range-Weather Forecasts (ECMWF) surface temperature used for SMOS under moderate or high VOD, heterogeneity, and most surface roughness conditions were consistent with the underestimations of the soil moisture product and provide the directions of product promotions. As for LPRM surface temperature, the worse skills can partially explain the unsatisfactory performances for LPRM soil moisture products. In spite of relatively acceptable skills of SMAP and SMOS-IC soil moisture products concerning R under moderate or dense VOD, small surface roughness, low heterogeneity conditions and temperate and cold climate types, advances in soil moisture products under high or even slightly low VOD, high roughness or topography complexity and heterogeneity, as well as in tropical or desert regions, remain challenging. It is expected that these findings can contribute to algorithm refinements, product enhancements (e.g., fusion and disaggregation) and hydrometeorological usages.
•The impacts of perturbing factors, heterogeneity and climate types were assessed.•SMOS-IC showed better performance concerning R and ubRMSE.•The complementarity between SMAP and ESA CCI was observed.•The underestimation of SMOS surface temperature contributed to the dry bias in SMOS soil moisture products.
Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods. The most recent source of soil moisture is ...the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will in turn support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first operational passive L-band system. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based upon another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The ascending pass overall root mean square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks. There are bias issues at some sites that need to be addressed as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information on the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated and it is expected that the SMOS estimates will improve.
Soil moisture is an essential climate variable influencing land–atmosphere interactions, an essential hydrologic variable impacting rainfall–runoff processes, an essential ecological variable ...regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large‐scale soil moisture monitoring has advanced in recent years, creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication; and, for some applications, the science required to utilize large‐scale soil moisture data is poorly developed. In this review, we describe the state of the art in large‐scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of (i) emerging in situ and proximal sensing techniques, (ii) dedicated soil moisture remote sensing missions, (iii) soil moisture monitoring networks, and (iv) applications of large‐scale soil moisture measurements. Significant near‐term progress seems possible in the use of large‐scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to spatial variability and model structures remain. Little progress has been made in the use of large‐scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large‐scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.
Core Ideas
Satellites, particularly at L‐band frequency, can globally map near‐surface soil moisture.
Near‐surface moisture is extended to the root zone using models and data assimilation.
Validation ...uses core monitoring sites, monitoring networks, field campaigns, and multi‐satellite comparisons.
Efforts are underway to associate soil moisture variability dynamics with land surface attributes.
This is an update to the special section “Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point” Vadose Zone Journal 12(3). Satellites (e.g., Soil Moisture Active Passive SMAP and Soil Moisture and Ocean Salinity SMOS) using passive microwave techniques, in particular at L‐band frequency, have shown good promise for global mapping of near‐surface (0–5‐cm) soil moisture at a spatial resolution of 25 to 40 km and temporal resolution of 2 to 3 d. C‐ and X‐band soil moisture records date back to 1978, making available an invaluable data set for long‐term climate research. Near‐surface soil moisture is further extended to the root zone (top 1 m) using process‐based models and data assimilation schemes. Validation of remotely sensed soil moisture products has been ongoing using core monitoring sites, sparse monitoring networks, intensive field campaigns, as well as multi‐satellite comparison studies. To transfer empirical observations across space and time scales and to develop improved retrieval algorithms at various resolutions, several efforts are underway to associate soil moisture variability dynamics with land surface attributes in various energy‐ and water‐rich environments. We describe the most recent scientific and technological advances in soil moisture remote sensing. We anticipate that remotely sensed soil moisture will find many applications in vadose zone hydrology in the coming decades.
Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture ...from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.
•Applications of high-resolution satellite-based soil moisture products are reviewed.•The gaps between product characteristics and user requirements are identified.•Open issues and opportunities for developments of high-resolution satellite-based soil moisture products are discussed.
Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily ...temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products.
The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3m−3; and the bias values were (−0.0460, 0.0010, and 0.0418) m3m−3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438m3m−3). Overall, SMAP showed a dry bias (−0.0460m3m−3) and AMSR2 had a wet bias (0.0418m3m−3); while ASCAT showed the least bias (0.0010m3m−3) among all the products.
Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD<0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10<VOD<0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD>0.40) ASCAT showed comparatively better performance than did the other products.
Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone.
The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro-meteorological problems.
•SMAP strongly agreed with the temporal dynamics of in-situ observations.•All remotely sensed soil moisture products were sound over moderately vegetated areas.•Over densely vegetated areas, ASCAT performed better than the other products.•The maximized R method was utilized to show the importance of individual datasets.•The SMAP-ASCAT combination performed better than the other combined products.
Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soilmoisture products is the disparity in spatial scales between ...satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m3/m3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.
Core Ideas
The CS655 measures two‐way travel time and attenuation of a low‐frequency EM pulse to derive Ka, EC, and SWC.
Laboratory and field evaluations for five central Texas soils found the sensor ...robust but RMSE could be reduced by a soil‐ or site‐specific function.
The factory calibration has a cross‐over point at Ka ∼15, with low bias below and high bias above.
The TxSON network mean was unaffected by calibration but the variance was significantly reduced.
Soil moisture sensors infer volumetric soil water content (SWC) from other properties of the bulk porous media. The CS655 water content reflectometer is a relatively new, low‐frequency electromagnetic sensor that determines relative permittivity (Ka) using the two‐way travel period and voltage attenuation of the applied signal along two 12‐cm rods. This measured attenuation is quadratically related to bulk electrical conductivity (EC). Along with an onboard thermistor, the CS655 allows a more robust correction of propagation time and Ka, which its predecessors, the CS615 and CS616, lacked. However, with new sensors it is necessary to quantify their practical accuracy in the field. Here, we present an overview of the CS655 sensor and an evaluation under both laboratory and field conditions, using five surface soils (0–10‐cm depth) in the laboratory and gravimetric samples collected in the field. Overall, a site‐specific calibration using a two‐term linearization of the SWC–Ka function reduced the root mean square error (RMSE) of the factory‐derived SWC of 0.073 and 0.043 m3 m−3 during batch and infiltration experiments, respectively, to 0.025 and 0.028 m3 m−3. Results further indicate that a soil‐specific calibration additionally reduced the RMSE to <0.02 m3 m−3. Field evaluation across the Texas Soil Observation Network found that calibration reduced the variance across the network but did not affect the arithmetic mean or the RMSE against gravimetric sampling, which remained ∼0.05 m3 m−3 regardless of the SWC–Ka–EC function applied. At the regional scale, a global calibration is sufficient.
The contrast between the point‐scale nature of current ground‐based soil moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve soil moisture poses a ...significant challenge for the validation of data products from current and upcoming soil moisture satellite missions. Given typical levels of observed spatial variability in soil moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint‐scale soil moisture estimates obtained from sparse ground‐based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite soil moisture products. This review paper describes the magnitude of the soil moisture upscaling problem and measurement density requirements for ground‐based soil moisture networks. Since many large‐scale networks do not meet these requirements, it also summarizes a number of existing soil moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite soil moisture validation using spatially sparse ground‐based observations.
Key Points
Satellite soil moisture retrievals are obtained at coarse spatial resolutions
It is difficult to validate them using point‐scale ground observations
Credible soil moisture upscaling strategies exist to address the problem
•A physically-based soil temperature module was tested in a temperate region.•Module performance between SWAT and modified SWAT was compared.•Both SWAT and modified SWAT well simulated soil ...temperature.•The physically-based soil temperature module reduced soil temperature simulation biases.•Insulation effect of crop residue was better addressed by the physically-based soil temperature module.
Although the Soil and Water Assessment Tool (SWAT) has been widely used in temperate regions, the performance of its soil temperature module has not been extensively assessed. The aim of the present study is to evaluate the performance of the SWAT model’s built-in empirical soil temperature module and a physically-based soil temperature module using four years of daily soil temperature measurements at three depths (i.e., 5, 10, and 50 cm) across 10 monitoring stations in and around the Choptank River Watershed, Maryland, USA. Model performance is assessed according to three coefficients of accuracy, i.e., Bias, Nash-Sutcliffe coefficient (NS), and coefficient of determination (R2). Model performance of SWAT and the modified version of SWAT (i.e., MSWAT; equipped with the physically-based soil temperature module) is also compared in winter and non-winter seasons. Results show that R2 and NS for different soil depths across 10 stations are all greater than 0.95 and 0.90, respectively, for both SWAT and MSWAT. This indicates that both SWAT and MSWAT reproduce well variations of measured soil temperatures at all soil depths for all stations. The results also show that SWAT and MSWAT tend to underestimate soil temperatures in both winter and non-winter seasons at all soil depths across 10 stations, and MSWAT improves soil temperature simulation by reducing absolute values of Bias, especially at surface soil layers in winter. We found out that a better representation of surface residue is needed in the physically-based soil temperature module for applications in agricultural watersheds of temperature regions. Without a physical accounting, a calibration procedure may be required to account for residue effects on heat transfer based on the current version of the physically-based module.
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