Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (489)

Search Parameters:
Keywords = soil moisture (SM)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
23 pages, 6991 KiB  
Article
Comparing the Accuracy of Soil Moisture Estimates Derived from Bulk and Energy-Resolved Gamma Radiation Measurements
by Sonia Akter, Johan Alexander Huisman and Heye Reemt Bogena
Sensors 2025, 25(14), 4453; https://doi.org/10.3390/s25144453 - 17 Jul 2025
Viewed by 321
Abstract
Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost [...] Read more.
Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference 40K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGRG–M and spectrometry-based 40K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from 40K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGRG–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGRG–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGRG–M than on 40K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGRG–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article
(This article belongs to the Special Issue Sensors in Smart Irrigation Systems)
Show Figures

Figure 1

29 pages, 6561 KiB  
Article
Correction of ASCAT, ESA–CCI, and SMAP Soil Moisture Products Using the Multi-Source Long Short-Term Memory (MLSTM)
by Qiuxia Xie, Yonghui Chen, Qiting Chen, Chunmei Wang and Yelin Huang
Remote Sens. 2025, 17(14), 2456; https://doi.org/10.3390/rs17142456 - 16 Jul 2025
Viewed by 424
Abstract
The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly [...] Read more.
The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm3/cm3, 0.019 cm3/cm3, and 0.034 cm3/cm3, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm3/cm3 (ASCAT) and 0.0662 cm3/cm3 (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
Show Figures

Figure 1

26 pages, 7975 KiB  
Article
Soil Moisture Prediction Using the VIC Model Coupled with LSTMseq2seq
by Xiuping Zhang, Xiufeng He, Rencai Lin, Xiaohua Xu, Yanping Shi and Zhenning Hu
Remote Sens. 2025, 17(14), 2453; https://doi.org/10.3390/rs17142453 - 15 Jul 2025
Viewed by 510
Abstract
Soil moisture (SM) is a key variable in agricultural ecosystems and is crucial for drought prevention and control management. However, SM is influenced by underlying surface and meteorological conditions, and it changes rapidly in time and space. To capture the changes in SM [...] Read more.
Soil moisture (SM) is a key variable in agricultural ecosystems and is crucial for drought prevention and control management. However, SM is influenced by underlying surface and meteorological conditions, and it changes rapidly in time and space. To capture the changes in SM and improve the accuracy of short-term and medium-to-long-term predictions on a daily scale, an LSTMseq2seq model driven by both observational data and mechanism models was constructed. This framework combines historical meteorological elements and SM, as well as the SM change characteristics output by the VIC model, to predict SM over a 90-day period. The model was validated using SMAP SM. The proposed model can accurately predict the spatiotemporal variations in SM in Jiangxi Province. Compared with classical machine learning (ML) models, traditional LSTM models, and advanced transformer models, the LSTMseq2seq model achieved R2 values of 0.949, 0.9322, 0.8839, 0.8042, and 0.7451 for the prediction of surface SM over 3 days, 7 days, 30 days, 60 days, and 90 days, respectively. The mean absolute error (MAE) ranged from 0.0118 m3/m3 to 0.0285 m3/m3. This study also analyzed the contributions of meteorological features and simulated future SM state changes to SM prediction from two perspectives: time importance and feature importance. The results indicated that meteorological and SM changes within a certain time range prior to the prediction have an impact on SM prediction. The dual-driven LSTMseq2seq model has unique advantages in predicting SM and is conducive to the integration of physical mechanism models with data-driven models for handling input features of different lengths, providing support for daily-scale SM time series prediction and drought dynamics prediction. Full article
Show Figures

Figure 1

33 pages, 9362 KiB  
Article
Multi-Layer and Profile Soil Moisture Estimation and Uncertainty Evaluation Based on Multi-Frequency (Ka-, X-, C-, S-, and L-Band) and Quad-Polarization Airborne SAR Data from Synchronous Observation Experiment in Liao River Basin, China
by Jiaxin Qian, Jie Yang, Weidong Sun, Lingli Zhao, Lei Shi, Hongtao Shi, Chaoya Dang and Qi Dou
Water 2025, 17(14), 2096; https://doi.org/10.3390/w17142096 - 14 Jul 2025
Viewed by 349
Abstract
Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial [...] Read more.
Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm3/cm3 to 0.079 cm3/cm3, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm3/cm3 to 0.069 cm3/cm3. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm3/cm3. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm3/cm3 to 0.064 cm3/cm3. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. Full article
Show Figures

Figure 1

36 pages, 2263 KiB  
Review
Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities
by Manoj Lamichhane, Sushant Mehan and Kyle R. Mankin
Remote Sens. 2025, 17(14), 2397; https://doi.org/10.3390/rs17142397 - 11 Jul 2025
Cited by 1 | Viewed by 927
Abstract
Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to [...] Read more.
Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances—the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability—have shown promise in addressing key challenges in SM estimation. Full article
Show Figures

Graphical abstract

26 pages, 9032 KiB  
Article
Relative Humidity and Air Temperature Characteristics and Their Drivers in Africa Tropics
by Isaac Kwesi Nooni, Faustin Katchele Ogou, Abdoul Aziz Saidou Chaibou, Samuel Koranteng Fianko, Thomas Atta-Darkwa and Nana Agyemang Prempeh
Atmosphere 2025, 16(7), 828; https://doi.org/10.3390/atmos16070828 - 8 Jul 2025
Viewed by 523
Abstract
In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather [...] Read more.
In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade−1). Winter (0.27 °C decade−1) and spring (0.20 °C decade−1) exhibited the strongest warming, followed by autumn (0.18 °C decade−1) and summer (0.10 °C decade−1). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article
(This article belongs to the Special Issue Precipitation in Africa (2nd Edition))
Show Figures

Figure 1

19 pages, 7486 KiB  
Article
Advancing GNOS-R Soil Moisture Estimation: A Multi-Angle Retrieval Algorithm for FY-3E
by Xuerui Wu, Junming Xia, Weihua Bai and Yueqiang Sun
Remote Sens. 2025, 17(13), 2325; https://doi.org/10.3390/rs17132325 - 7 Jul 2025
Viewed by 287
Abstract
Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order [...] Read more.
Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm3) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques. Full article
Show Figures

Figure 1

30 pages, 16359 KiB  
Article
Simultaneous Reductions in Forest Resilience and Greening Trends in Southwest China
by Huiying Wu, Tianxiang Cui and Lin Cao
Remote Sens. 2025, 17(13), 2227; https://doi.org/10.3390/rs17132227 - 29 Jun 2025
Viewed by 536
Abstract
As an essential part of terrestrial ecosystems, forests are key to sustaining ecological balance, supporting the carbon cycle, and offering various ecosystem services. In recent years, forests in Southwest China have experienced notable greening. However, the rising occurrence and severity of droughts present [...] Read more.
As an essential part of terrestrial ecosystems, forests are key to sustaining ecological balance, supporting the carbon cycle, and offering various ecosystem services. In recent years, forests in Southwest China have experienced notable greening. However, the rising occurrence and severity of droughts present a significant threat to the stability of forest ecosystems in this region. This study adopted the near-infrared reflectance of vegetation (NIRv) and the lag-1 autocorrelation of NIRv as indicators to assess the dynamics and resilience of forests in Southwest China. We identified a progressive decline in forest resilience since 2008 despite a dominant greening trend in Southwest China’s forests during the last 20 years. By developing the eXtreme Gradient Boosting (XGBoost) model and Shapley additive explanation framework (SHAP), we classified forests in Southwest China into coniferous and broadleaf types to evaluate the driving factors influencing changes in forest resilience and mapped the spatial distribution of dominant drivers. The results showed that the resilience of coniferous forests was mainly driven by variations in elevation and land surface temperature (LST), with mean absolute SHAP values of 0.045 and 0.038, respectively. In contrast, the resilience of broadleaf forests was primarily influenced by changes in photosynthetically active radiation (PAR) and soil moisture (SM), with mean absolute SHAP values of 0.032 and 0.028, respectively. Regions where elevation and LST were identified as dominant drivers were mainly distributed in coniferous forest areas across central, eastern, and northern Yunnan Province as well as western Sichuan Province, accounting for 32.9% and 20.0% of the coniferous forest area, respectively. Meanwhile, areas where PAR and SM were dominant drivers were mainly located in broadleaf forest regions in Sichuan and eastern Guizhou, accounting for 29.9% and 27.7% of the broadleaf forest area, respectively. Our study revealed that the forest greening does not necessarily accompany an enhancement in resilience in Southwest China, identifying the driving factors behind the decline in forest resilience and highlighting the necessity of differentiated restoration strategies for forest ecosystems in this region. Full article
(This article belongs to the Section Forest Remote Sensing)
Show Figures

Figure 1

25 pages, 5080 KiB  
Article
Study on 2007–2021 Drought Trends in Basilicata Region Based on the AMSU-Based Soil Wetness Index
by Raffaele Albano, Meriam Lahsaini, Arianna Mazzariello, Binh Pham-Duc and Teodosio Lacava
Land 2025, 14(6), 1239; https://doi.org/10.3390/land14061239 - 9 Jun 2025
Viewed by 496
Abstract
Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more [...] Read more.
Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more relevant for all studies related to extreme event (e.g., floods, droughts, and landslides) mitigation and assessment. In this study, data acquired by the advanced microwave sounding unit (AMSU) onboard the European Meteorological Operational Satellite Program (MetOP) satellites were used for the first time to extract information on the variability of SM by implementing the original soil wetness index (SWI). Long-term monthly SWI time series collected for the Basilicata region (southern Italy) were analyzed for drought assessment during the period 2007–2021. The accuracy of the SWI product was tested through a comparison with SM products derived by the Advanced SCATterometer (ASCAT) over the 2013–2016 period, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the relevance of the long-term achievements in terms of drought analysis. The results indicate a satisfactory accuracy of the SWI, with the mean correlation coefficient values with ASCAT higher than 0.7 and a mean normalized root mean square error less than 0.155. A negative trend in SWI during the 15-year period was found using both the original and deseasonalized series (linear and Sen’s slope ~−0.00525), confirmed by SPEI (linear and Sen’s slope ~−0.00293), suggesting the occurrence of a marginal long-term dry phase in the region. Although further investigations are needed to better assess the intensity and main causes of the phenomena, this result indicates the contribution that satellite data/products can offer in supporting drought assessment. Full article
(This article belongs to the Section Land – Observation and Monitoring)
Show Figures

Figure 1

20 pages, 10937 KiB  
Article
Adaptive Analysis of Ecosystem Stability in China to Soil Moisture Variations: A Perspective Based on Climate Zoning and Land Use Types
by Yuanbo Lu, Yang Yu, Xiaoyun Ding, Lingxiao Sun, Chunlan Li, Jing He, Zengkun Guo, Ireneusz Malik, Malgorzata Wistuba and Ruide Yu
Remote Sens. 2025, 17(12), 1971; https://doi.org/10.3390/rs17121971 - 6 Jun 2025
Viewed by 405
Abstract
In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones [...] Read more.
In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen’s Slope Estimation and the Mann–Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low–low (L-L) clusters in the north and high–high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150–200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management. Full article
Show Figures

Figure 1

15 pages, 5392 KiB  
Article
Validating Data Interpolation Empirical Orthogonal Functions Interpolated Soil Moisture Data in the Contiguous United States
by Haipeng Zhao, Haoteng Zhao and Chen Zhang
Agriculture 2025, 15(11), 1212; https://doi.org/10.3390/agriculture15111212 - 1 Jun 2025
Cited by 1 | Viewed by 463
Abstract
Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data [...] Read more.
Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data for the Contiguous United States (CONUS), a 1-km SMAP product has been produced using the THySM model in support of USDA NASS operations. However, the current 1-km product contains substantial data gaps, which poses challenges for applications that require continuous daily data. Data Interpolation Empirical Orthogonal Functions (DINEOF+) is an interpolation technique that uses singular value decomposition (SVD) to address missing data problems. Previous studies have applied DINEOF+ to reconstruct the 1-km daily SM dataset but without further analysis of the reconstruction errors. In this study, we perform a comprehensive validation of DINEOF+ reconstructed SM by using both the original SMAP data and in situ measurements across the CONUS. Our results show that the reconstructed SM closely aligns with the original SM with R2 > 0.65 and bias ranging from 0.01 to 0.02 m3/m3. When compared to in situ SM, the mean absolute error (MAE) ranges between 0.01 and 0.04 m3/m3 and the time series correlation coefficient ranges from 0.6 to 0.8. Our findings suggest that DINEOF+ effectively recovers missing data and improves the temporal resolution of SM time series. However, we also note that the accuracy of the reconstructed SM is dependent on the quality of the original SMAP data, emphasizing the need for continued improvements in SM retrievals by satellite. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
Show Figures

Figure 1

32 pages, 962 KiB  
Review
An Overview of Machine-Learning Methods for Soil Moisture Estimation
by Mercedeh Taheri, Mostafa Bigdeli, Hanifeh Imanian and Abdolmajid Mohammadian
Water 2025, 17(11), 1638; https://doi.org/10.3390/w17111638 - 28 May 2025
Cited by 1 | Viewed by 1625
Abstract
Soil moisture (SM) is crucial for sustainable applications in agriculture, meteorology, and hydrology. While direct measurement provides superior accuracy, it is unfeasible when applied over extensive geographical areas because of its costly and time-intensive nature. On the other hand, parameterization, complexity, and assumptions [...] Read more.
Soil moisture (SM) is crucial for sustainable applications in agriculture, meteorology, and hydrology. While direct measurement provides superior accuracy, it is unfeasible when applied over extensive geographical areas because of its costly and time-intensive nature. On the other hand, parameterization, complexity, and assumptions used in empirical and physical models lead to challenging SM estimations using these models. By handling extensive datasets and identifying complex connections within the data, the machine-learning (ML) approach has become an attractive solution to address the aforementioned limitations. This approach can estimate SM by effectively capturing the complex relationships among environmental variables and soil moisture data. Although the ML approach is a powerful tool for estimating SM, it has several limitations, such as data dependency, scalability, and high dimensionality. This paper aims to present an overview of ML methods used for modeling SM while also discussing their challenges and notable achievements within this field. These models vary in suitability depending on data availability and context. DL models excel in capturing spatiotemporal complexity but require abundant data. SVMs are robust in noisy or sparse datasets, and hybrid models offer improved flexibility and predictive accuracy. Incorporating remote sensing, satellite data, and hybrid physical-AI frameworks can further enhance performance. However, the opaque “black-box” nature of ML remains a barrier to trust and operational use, emphasizing the need for explainable AI (XAI) to improve transparency. The findings underscored the importance of prioritizing the transferability of AI-based models across varied environmental conditions to ensure scalable and dependable soil moisture monitoring. Full article
Show Figures

Figure 1

32 pages, 4186 KiB  
Article
Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm
by Yana Zou and Xiangrong Wang
Sustainability 2025, 17(11), 4836; https://doi.org/10.3390/su17114836 - 24 May 2025
Cited by 1 | Viewed by 485
Abstract
With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between [...] Read more.
With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (Rh) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O3, NO2, and SO2) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m2/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m2/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO2, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions. Full article
Show Figures

Figure 1

32 pages, 8105 KiB  
Article
Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya
by Asnake Kassahun Abebe, Xiang Zhou, Tingting Lv, Zui Tao, Abdelrazek Elnashar, Asfaw Kebede, Chunmei Wang and Hongming Zhang
Remote Sens. 2025, 17(10), 1763; https://doi.org/10.3390/rs17101763 - 19 May 2025
Cited by 1 | Viewed by 1772
Abstract
Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced [...] Read more.
Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m3/m3, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m3/m3 and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. Full article
Show Figures

Figure 1

15 pages, 3602 KiB  
Article
Non-Linear Models for Assessing Soil Moisture Estimation
by Rui Li, Susu Wang, Han Wu, Hao Dong, Dezhi Kong, Hanxue Li, Dorothy S. Zhang and Haitao Chen
Horticulturae 2025, 11(5), 492; https://doi.org/10.3390/horticulturae11050492 - 30 Apr 2025
Viewed by 383
Abstract
Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and [...] Read more.
Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R2 of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R2 values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m3·m3, 0.015 m3·m3, and 0.006 m3·m3 at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA’s robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring. Full article
Show Figures

Figure 1

Back to TopTop