scholarly journals Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

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.

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

2020 ◽  
Vol 12 (3) ◽  
pp. 570 ◽  
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.

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

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.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

scholarly journals Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

2020 ◽  
Vol 12 (22) ◽  
pp. 3785
Author(s):  
Xiaoyong Xu
Keyword(s):  
Soil Moisture ◽  
Great Lakes ◽  
Root Zone ◽  
Evaluation Studies ◽  
Reference Network ◽  
Great Lakes Region ◽  
Sparse Network ◽  
Level 2 ◽  
The U.S

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

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

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

<p>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.</p><p>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. </p><p>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.</p>

scholarly journals Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna

2020 ◽  
Vol 12 (11) ◽  
pp. 1701
Author(s):  
Carlos Román-Cascón ◽  
Marie Lothon ◽  
Fabienne Lohou ◽  
Nitu Ojha ◽  
Olivier Merlin ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
High Resolution ◽  
Satellite Data ◽  
Tree Cover ◽  
Optical Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
In Situ Data

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

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

2014 ◽  
Vol 507 ◽  
pp. 855-858 ◽  
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.

scholarly journals Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Amal Chakhar ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Miguel A. Moreno
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data ◽  
Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

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

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1123 ◽  
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.

scholarly journals Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1981
Author(s):  
Yan Li ◽  
Chengcai Zhang ◽  
Weidong Heng
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Multiple Linear Regression Model ◽  
Optical Data ◽  
Support Vector ◽  
Surface Soil Moisture ◽  
Vegetation Fraction ◽  
Sentinel 2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

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