Soil Moisture Retrieval Research Articles

High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau

ABSTRACT As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. In this study, Copernicus Sentinel-1 synthetic aperture radar data, Sentinel-2 multi-spectral data, and other auxiliary data (land cover types, soil texture, etc.) were used to retrieve surface soil moisture (10 m) in the cloud environment (Google Earth Engine + Google Colab + Google Drive) over the Tibetan Plateau, and an entirely data-driven machine learning-based model called Deep Forest was adopted. We discussed the application of the Deep Forest model and compared it with other machine learning models. Overall, on the basis of 10-fold cross-validation, the modified Deep Forest model performed the best, with estimate accuracy of 0.834 and 0.038 m3·m−3 in terms of coefficient of determination ( R 2 ) and unbiased Root Mean Square Error (ubRMSE), respectively. It also demonstrated the best performance in site-based validation ( R 2 of 0.606 and ubRMSE of 0.092 m3·m−3). In addition, the framework for the data acquisition, data preprocessing, model training, and soil moisture mapping in this study was completed in the cloud environment, which facilitated the entire retrieval process. This work provides new ideas beyond the retrieval model for other related studies.

Soil moisture retrieval by a novel hybrid model based on CYGNSS and Sun-induced fluorescence data

Using Global Navigation Satellite System-Reflectometry (GNSS-R) for soil moisture (SM) retrieval has recently gained importance due to its high temporal-spatial resolution. However, the current methods, i.e., constructing a single machine learning (ML)-based model, have large model uncertainty resulting from ML networks and input schemes. Moreover, traditional Normalized Difference Vegetation Index (NDVI) cannot capture the rapid vegetation changes well. In this paper, a new SM retrieval method of constructing a hybrid model based on Bayesian model averaging (BMA) is employed to reduce the model uncertainty. Meanwhile, novel Sun-induced fluorescence (SIF) data is used as ancillary data to represent the rapid change of vegetation. We validate the proposed method at point and regional scales using in-situ data and the Global Land Data Assimilation System (GLDAS) product. The results demonstrate that our method has high accuracy and low uncertainty in SM retrieval. At the point scale, as accuracy indices, the average R (μR) of BMA increases from 0.90 to 0.93 and the average root-mean-square-error (μRMSE) decreases from 0.034 cm3/cm3 to 0.029 cm3/cm3; as indices of uncertainty, the standard deviations of R and RMSE (σR and σRMSE) decrease by 32 % and 9 % compared to the single ML-based model. For the regional scale, the μR increases from 0.79 to 0.81, the μRMSE decreases from 0.024 cm3/cm3 to 0.023 cm3/cm3, and the σR decreases by 19 %. Moreover, we take the point-scale experiment as an example for comparison to compare the performance of SIF with that of NDVI. The μR of BMA trained by SIF is 0.03 higher than that trained by NDVI and the μRMSE decreases by 0.002 cm3/cm3; σR and σRMSE decrease by 25 % and 6 %. Based on these results, the proposed method can reduce the uncertainty and the advantage of SIF has potential for improving the SM retrieval.

An improved change detection method for high-resolution soil moisture mapping in permafrost regions

ABSTRACT Soil moisture plays a crucial role in understanding the hydrological cycle and the ecological environment. This research presents an improved change detection method that leverages time series data from Sentinel-1 radar and Sentinel-2 optical sensors (2019–2021) to estimate surface soil moisture. The response of backscatter to soil moisture in bare soil was expressed in a logarithmic form, and the influence function of the normalized difference vegetation index (NDVI) on the backscatter difference was established for various vegetation-covered surfaces. Therefore, the impact of vegetation on backscatter is effectively mitigated, and the resulting change in backscatter relative to bare soil conditions can be obtained. An empirical function is subsequently formulated to ascertain the reference values of soil moisture in each pixel. The retrieval of soil moisture is demonstrated in the Wudaoliang permafrost region of the Qinghai-Tibet Plateau and validated against ground measurements. The retrieval results of the improved change detection method exhibit correlation coefficients ranging from 0.672 to 0.941, with root mean squared errors (RMSE) ranging from 0.031 m 3 / m 3 to 0.073 m 3 / m 3 . Compared to the Soil Moisture Active Passive (SMAP) 9-km product, our new method demonstrates higher correlation (0.898 vs. 0.867) and lower RMSE (0.037 m 3 / m 3 vs. 0.044 m 3 / m 3 ). The soil moisture retrieved from Sentinel shows a strong correlation with the SMAP 9-km soil moisture in the time series, thereby providing a better representation of the region’s soil moisture heterogeneity. Our method demonstrates the feasibility of combining Sentinel-1 and 2 for high-resolution (100 m) soil moisture mapping in permafrost regions.

Multipath phase based vegetation correction scheme for improved field-scale soil moisture retrieval over agricultural cropland using GNSS-IR technique

Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.).

Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe

ABSTRACT The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution.

Are raw satellite bands and machine learning all you need to retrieve actual evapotranspiration?

Accurately estimating latent heat flux (LE) is crucial for achieving efficiency in irrigation. It is a fundamental component in determining the actual evapotranspiration (ETa), which in turn, quantifies the amount of water lost that needs to be adequately compensated through irrigation. Empirical and physics-based models have extensive input data and site-specific limitations when estimating the LE. In contrast, the emergence of data-driven techniques combined with remote sensing has shown promising results for LE estimation with minimal and easy-to-obtain input data. This paper evaluates two machine learning-based approaches for estimating the LE. The first uses climate data, the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), while the second uses climate data combined with raw satellite bands. In-situ data were sourced from a flux station installed in our study area. The data include air temperatures (Ta), global solar radiation (Rg), and measured LE for the period 2015-2018. The study uses Landsat 8 as a remote sensing data source. At first, 12 raw available bands were downloaded. The LST is then derived from thermal bands using the Split Window algorithm (SW) and the NDVI from optical bands. During machine learning modeling, the CatBoost model is fed, trained, and evaluated using the two data combination approaches. Cross-validation of 3-folds gave an average RMSE of 27.54 W.nr2 using the first approach and 27.05 W.nr2 using the second approach. Results raise the question: Do we need additional computational layers when working with remote sensing products combined with machine learning? Future work is to generalize the approach and test it for other applications such as soil moisture retrieval, and yield prediction.

Assessment of Surface Scattering Models Within the Water Cloud Model Toward Soil Moisture Retrievals Using Sentinel-1 and Sentinel-2 Images

Assessment of Surface Scattering Models Within the Water Cloud Model Toward Soil Moisture Retrievals Using Sentinel-1 and Sentinel-2 Images

Inversion of soil moisture in Duolun County, Inner Mongolia, China using Sentinel-1 data

Soil moisture is an important physical quantity that reflects the surface conditions. Soil moisture retrieval from remote sensing satellite monitoring data is a common method at present and the crucial issue is how to eliminate the influence of other surface and reflect soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic wave. In this paper, the Sentinel-1 SAR data, optical data and other auxiliary data such as the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration are used as data sources. By coupling the Advanced Integral equation (AIEM) model, a method suitable for the inversion of soil moisture content in the Duolun area is established, and the inverted soil moisture map is analyzed.

Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China

Drought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Under-sampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan.

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Systematic Modeling Errors Undermine the Application of Land Data Assimilation Systems for Hydrological and Weather Forecasting

Abstract Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water state–water flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state–flux coupling strength bias—involving both evapotranspiration and runoff—are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias, even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The rescaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water state–water flux coupling strength biases during the operation of an LDAS. Significance Statement Land data assimilation is the process by which land surface model estimates of water states (e.g., soil moisture) and water fluxes (e.g., runoff and evapotranspiration) are improved via the incorporation of observations. Over the past decade, substantial improvements have been made in the precision of land surface model states via the assimilation of satellite-based soil moisture information. However, to date, these improvements have not yet been extended into water flux estimates like runoff and evapotranspiration. This is a critical shortcoming since advances in important weather and hydrologic forecasting applications are dependent on the improved estimation of such fluxes. We demonstrate that this shortcoming is linked to the inability of existing land surface models to accurately describe the impact of variations in water states on water fluxes and propose strategies for overcoming this issue in future land data assimilation systems.

Fusion of Machine Learning and Semi-Empirical Models for Cooperative Retrieval of Soil Moisture With Optical and SAR Remote Sensing: Cyclic or Parallel?

Fusion of Machine Learning and Semi-Empirical Models for Cooperative Retrieval of Soil Moisture With Optical and SAR Remote Sensing: Cyclic or Parallel?

P-Band and L-Band Radiometry Retrieval of Soil Moisture and Temperature Profiles

P-Band and L-Band Radiometry Retrieval of Soil Moisture and Temperature Profiles

Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-Based Replacement of Radiative Transfer and Roughness Model

Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-Based Replacement of Radiative Transfer and Roughness Model

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Модель комплексной диэлектрической проницаемости органо-минеральных почв, учитывающая минеральный состав и содержание органического вещества

The single-frequency dielectric model of thawed and frozen forest soils in the root zone based on the refractive dielectric model was created, taking into account the influence of both the mineral and organic components of the soil mixture. The dielectric model was created on at 435 MHz the basis of the laboratory dielectric measurements of five soils with organic matter content in the range from 11.1 to 54.4 % and clay content from 21.1 to 40.9 %. Dielectric measurements were carried out in the range of the gravimetric moisture from the dry state to the lowest moisture capacity and in the temperature range from –30 to 25 ℃. The coefficient of determination between the predicted and measured values for real and imaginary parts of the complex permittivity are 0.988 - 0.997 and 0.957 - 0.971, respectively. The normalized standard deviations in this case are 6 - 5.6 % and 20.2 - 21.2 % for real and imaginary parts of the complex permittivity, respectively. The developed dielectric model can be used in remote sensing algorithms of soil moisture retrieval of forest soils in the root zone from the data radar and radiometric.

Ground-Based Soil Moisture Retrieval Using the Correlation Between Dual-Polarization GNSS-R Interference Patterns

Ground-Based Soil Moisture Retrieval Using the Correlation Between Dual-Polarization GNSS-R Interference Patterns

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

All-season Liquid Soil Moisture Retrieval from SMAP

All-season Liquid Soil Moisture Retrieval from SMAP

Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias Over the Amazon Rainforest

Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias Over the Amazon Rainforest