Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

Abstract Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

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Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations

Journal of Hydrometeorology ◽

10.1175/2010jhm1285.1 ◽

2010 ◽

Vol 11 (6) ◽

pp. 1423-1429 ◽

Cited By ~ 143

Author(s):

Diego G. Miralles ◽

Wade T. Crow ◽

Michael H. Cosh

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Sampling Error ◽

Microwave Remote Sensing ◽

Spatial Sampling ◽

Sampling Errors ◽

Point Scale ◽

Land Surface Modeling ◽

Ground Station ◽

Intrinsic Error

Abstract The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.

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Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs13020228 ◽

2021 ◽

Vol 13 (2) ◽

pp. 228

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Spatial Density ◽

Linear Regression Method ◽

Spatiotemporal Resolution ◽

Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

Remote Sensing ◽

10.3390/rs12030570 ◽

2020 ◽

Vol 12 (3) ◽

pp. 570 ◽

Cited By ~ 3

Author(s):

Gerard Portal ◽

Thomas Jagdhuber ◽

Mercè Vall-llossera ◽

Adriano Camps ◽

Miriam Pablos ◽

...

Keyword(s):

Soil Moisture ◽

Iberian Peninsula ◽

Spatial Scale ◽

Temporal Dynamics ◽

Microwave Radiometer ◽

Added Value ◽

Optical Data ◽

Enhanced Resolution ◽

The Impact

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

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Estimation of high-resolution soil moisture using machine learning, satellite observations and ground measurements. A case study in a hilly agricultural region

10.5194/egusphere-egu2020-8338 ◽

2020 ◽

Author(s):

Luca Zappa ◽

Matthias Forkel ◽

Angelika Xaver ◽

Wouter Dorigo

Keyword(s):

Soil Moisture ◽

Random Forest ◽

Spatial Resolution ◽

Vegetation Cover ◽

Temporal Dynamics ◽

Remotely Sensed ◽

Training Data ◽

Future Research ◽

Microwave Sensors

Remotely sensed data from microwave sensors have been successfully used to retrieve soil moisture on a global scale. In particular, passive and active microwave sensors with large footprints can observe the same location with a (sub-)daily frequency, but typically are characterized by spatial resolutions in the order of tens of km. Therefore, such coarse scale products can accurately capture the temporal dynamics of soil moisture but are inadequate in providing spatial details. However, several agricultural and hydrological applications could greatly benefit from soil moisture observations with a sub-kilometer spatial resolution while preserving a daily revisit time.Here, we present a framework for downscaling coarse resolution satellite soil moisture products (ASCAT and SMAP) to high spatial resolution. In particular, we build robust relationships between remotely sensed soil moisture and ancillary variables on soil texture, topography, and vegetation cover. Such relationship is built through Random Forest regressions, trained against in-situ measurements of soil moisture. The proposed approach is developed and tested in an agricultural catchment equipped with a high-density network of in-situ sensors. Our results show a strong consistency between the downscaled and the observed spatio-temporal patterns of soil moisture. Furthermore, including a proxy of vegetation cover in the Random Forest regressions results in considerable improvements of the downscaling performance. Finally, if only limited training data can be used, priority should be given to increase the number of sensor locations to adequately cover the spatial heterogeneity, rather than expanding the duration of the measurements.&#160;Future research will focus on including additional ancillary variables as model predictors, e.g. Land Surface Temperature or backscatter, and on applying the downscaling framework to other regions with similar environmental and climatic conditions.

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A dynamic approach for evaluating coarse scale satellite soil moisture products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-7263-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 7263-7303 ◽

Cited By ~ 1

Author(s):

A. Loew ◽

F. Schlenz

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Sampling Error ◽

Coarse Scale ◽

Dynamic Approach ◽

In Situ Data ◽

Novel Approach ◽

Area Sampling ◽

Satellite Product

Abstract. Validating coarse scale remote sensing soil moisture products requires a comparison of gridded data to point-like ground measurements. The necessary aggregation of in situ measurements to the footprint scale of a satellite sensor (>100 km2) introduces uncertainties in the validation of the satellite soil moisture product. Observed differences between the satellite product and in situ data are therefore partly attributable to these aggregation uncertainties. The present paper investigates different approaches to disentangle the error of the satellite product from the uncertainties associated to the up-scaling of the reference data. A novel approach is proposed, which allows for the quantification of the remote sensing soil moisture error using a temporally adaptive technique. It is shown that the point-to-area sampling error can be estimated within 0.0084 [m3/m3].

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Validation of AMSR-E Soil Moisture Retrievals over Huaihe River Basin, in China

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.507.855 ◽

2014 ◽

Vol 507 ◽

pp. 855-858 ◽

Cited By ~ 2

Author(s):

Xing Mei Xie ◽

Jing Wen Xu ◽

Jun Fang Zhao ◽

Shuang Liu ◽

Peng Wang

Keyword(s):

Soil Moisture ◽

River Basin ◽

Temporal Dynamics ◽

Absolute Error ◽

Huaihe River Basin ◽

Huaihe River ◽

Earth Observing System ◽

X Band ◽

Precipitation Events

The two soil moisture retrieval methods based on the Advanced Microwave Scanning Radiometer of the Earth Observing System (AMSR-E) data, the standard algorithm by NASA and Land Parameter Retrieval Model (LPRM) have been validated at Xuchang site in Huaihe River basin, in China. The NASA dataset fails to capture main fluctuations of soil moisture, while the LPRM exhibits stronger agreement with the temporal dynamics and precipitation events associated with in situ soil moisture. The LPRM X-band product over ascending pass performs best with correlation coefficient value of 0.42, root mean square error ranging from 0.18 and mean absolute error of 0.14. Generally, the useful soil moisture information can be extracted over HRB from AMSR-E passive microwave data.

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Validation of ECMWF Multi-Layer Reanalysis Soil Moisture Based on the OzNet Hydrology Network

Water ◽

10.3390/w10091123 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1123 ◽

Cited By ~ 4

Author(s):

Wenlong Jing ◽

Jia Song ◽

Xiaodan Zhao

Keyword(s):

Soil Moisture ◽

Temporal Dynamics ◽

Root Zone ◽

Water Cycle ◽

In Situ Measurements ◽

Model Errors ◽

Time Variations ◽

Mean Square Difference ◽

Terrain Features

Soil moisture reanalysis products can provide soil water information for the surface and root zone soil layers, which are significant for understanding the water cycle and climate change. However, the accuracy of multi-layer soil moisture datasets obtained from reanalysis products remains unclear in some areas. In this study, we evaluated the root zone soil moisture of the ERA-Interim soil moisture product, as well as the surface soil moisture based on in situ measurements from the OzNet hydrological measurement network over southeast Australia. In general, the ERA-Interim soil moisture product presents good agreement with in situ soil moisture values and can nicely reflect time variations, with correlation coefficient (R) values in the range of 0.73 to 0.84 and unbiased root mean square difference (ubRMSD) values from 0.035 m3·m−3 to 0.060 m3·m−3. Although the ERA-Interim soil moisture also can reflect temporal dynamics of soil moisture at root zone layer at depths of 28–100 cm, low correlations were found in winter. In addition, the ERA-Interim soil moisture product overestimates in situ measurements at depths of 0–7 cm and 7–28 cm, whereas the product shows underestimated values compared with in situ soil moisture at the root zone of 28–100 cm. Consequently, the ERA-Interim soil moisture product has both high absolute and temporal accuracy at depths of 7–28 cm, and the ERA-Interim soil moisture product can nicely capture temporal dynamics at all the evaluated soil level depths, except for the depth of 28–100 cm during the winter months. The contributions of terrain, vegetation cover, and soil texture to the model error were addressed by feature importance estimations using the random forest (RF) algorithm. Results indicate that terrain features may have an impact on the model errors. It is clear that the accuracy of the ERA-Interim soil moisture can be improved by adjusting the assimilation scheme, and the results of this study are expected to provide a comprehensive understanding of the model errors and references for optimizing the model.

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Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

Remote Sensing ◽

10.3390/rs12193173 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3173

Author(s):

Cheng Tong ◽

Hongquan Wang ◽

Ramata Magagi ◽

Kalifa Goïta ◽

Luyao Zhu ◽

...

Keyword(s):

Soil Moisture ◽

Local Time ◽

Temporal Dynamics ◽

Ordinary Least Squares ◽

Optical Data ◽

Support Vector ◽

Learning Approaches ◽

Station Data ◽

Level 2

This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

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