Explore the vast range of titles available.

This paper attempts to realize the identification and classification of slope failure/landslide patterns in the early warning system (EWS) based on Soil Water Index (SWI), for fuzzy evaluation of the slope failure scale based on meteorological data. For this purpose, the stability analysis and shear strength parametric discussions of a homogeneous slope model composed of two kinds of soil, i.e., volcanic soil and Toyoura sand, are performed under 22 kinds of designed rainfall conditions. In a total of 8,976 simulated slope stability scenarios, 374 slope failures with a factor of safety (FOS) less than 1.0 for the first time were identified. After that, the depths of the potential slip surface of the slope failure patterns were collected and analyzed. Results indicate that the SWI-based EWS can identify and classify the four landslide patterns. As rainfall intensity rises, the slope failure pattern gradually changes from Pattern I (Sliding) during long-term low-intensity (LL) type rainfall, to Pattern II (Buckling), to Pattern III (Toppling), and finally to Pattern IV (Crumbling) during short-term high-intensity (SH) type rainfall. In addition, the correlation between the slope failure pattern and the potential slip depth and SWI is very poor, but there is a strong correlation between the landslide pattern and the potential slip depth and water storage height ( H 2 ) in the second tank layer. Therefore, in the SWI-based EWS, the water storage height ( H 2 ) in the second tank layer might be used to evaluate the scale of slope failure.

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths ( T ), requires root zone depth optimization ( T opt ) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy ( R  ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

This study introduces the soil water index (SWI) approach, the sum of water depths in the three-layer tank model, with rainfall data to understand landslide occurrence in Taiwan. A total of 236 historical instance data were compiled to investigate the characteristics of different types of landslides: debris flows (DF), shallow landslides (SL), and large-scale landslides (LL). The data shows that most DF occurred near the time of maximum rainfall intensity. However, several SL and LL events indicate that the occurrence of SL and LL has correlations with more than just rainfall intensity or rainfall amount. The SWI and the water depth in the tanks (from top to bottom: H1, H2, H3) provide an intriguing approach to correlate soil moisture content at occurrence time with the landslide types. DF events show the highest H1/SWI value, which indicates the correlation with water content in the surficial soil. LL events after rainfall have the highest H3/SWI ratio, indicating that prolonged rainfall events cause rainwater to infiltrate a deeper soil layer and trigger these LL. We developed a ternary diagram of the water depth-to-SWI ratios to show characteristics of the three landslide types. This study found that the H2/SWI value is typically in the range of 0.2–0.4. In this range, the landslide type gradually shifts from DF to SL, and then to LL, along the H3/SWI axis. Therefore, the ternary diagram can explain landslide type changes with the water depth-to-SWI ratios and better understand landslide occurrence.

This study applied the soil water index (SWI), which can represent the conceptual soil water contents as influenced by present and antecedent rainfall, for analyzing rainfall-induced mass movements in Taiwan. The SWI has been used in Japan for nationwide mass movement warnings. This study examined whether the SWI can be also applied to Taiwan, which has a climatic condition and high-relief topography similar to Japan. We used data for mass movements for 2006–2012 ( n  = 263) for the main analyses and those for 2013 ( n  = 19) for verification. The SWI values before the rainfall events that triggered mass movements were used as the indicator of the antecedent rainfall condition. We found that when SWI values before rainfall events increased from <17.5 to >35, the upper threshold of rainfall conditions needed for triggering mass movements significantly decreased. The mass movements in 2013 support this finding. We classified rainfall conditions for triggering mass movements into two types, short duration–high intensity (SH) and long duration–low intensity (LL), based on a principal component analysis (PCA). The SH type is associated with a rapid increase in SWI, and the LL type is associated with a gradual rise and subsequent constancy of SWI except in some extremely long rainfall events. Based on this result, we modeled the general trend of the time series changes in SWI for the two types, which was verified using the mass movements in 2013.

Agricultural drought (AD) refers to a decline in crop yield as a result of a decrease in soil moisture, which is closely associated with land use type and soil texture. In semi-arid climates, however, it is unclear what role land use type and soil texture play in AD. Here, soil water deficit index (SWDI) considering the physiological state of vegetation is used to characterize AD. This study systematically investigated the effects of land use type and soil texture on the evolution of AD, and also analyzed the temporal changes of soil moisture, water deficiency in the vadose zone, and precipitation at annual and monthly scales. The results indicate that there is a broad similarity between the annual variation trends of soil moisture and precipitation in the Ziya River Basin (ZRB), while water shortages in the vadose zone are opposite. It is evident that SWDI has good applicability in characterizing AD in ZRB by comparing it with meteorological drought (MD). For impacts of different land use types and soil textures, Forest and Grassland exhibit greater drought resistance with the lowest duration ( D , 4.12 and 3.92) and severity ( S , 7.76 and 7.93), whereas Cropland, Waters, and Urban Land ( D  > 5.39; S  > 11) are more susceptible to severe droughts. For soil textures, Sand ( D , 4.56; S , 8.32) and Loam ( D , 5.09; S , 10.32) are more prone to AD than Clay ( D , 3.24; S , 5.13). Overall, these findings contribute to understanding the role of the land use types and soil textures in the evolution of AD in semi-arid regions and provide practical recommendations for early identifying AD.

Recent improvements in soil moisture mapping using satellites provide estimates at higher spatial and temporal resolutions. The accuracy in alpine regions is, however, still not well understood. The main objective of this study is to evaluate the accuracy of the experimental ASCAT-DIREX soil moisture product in a small alpine catchment and to identify factors that control the soil moisture agreement between the satellite estimates and in situ observations in open and forest sites. The analysis is carried out in the experimental mountain catchment of Jalovecký Creek, situated in the Western Tatra Mountains (Slovakia). The satellite soil moisture estimates are derived by merging the ASCAT and Sentinel-1 retrievals (the ASCAT-DIREX dataset), providing relative daily soil moisture estimates at 500 m spatial resolution in the period 2012–2019. The soil water estimates represent four characteristic timescales of 1, 2, 5, and 10 days, which are compared with in situ topsoil moisture observations. The results show that the correlation between satellite-derived and in situ soil moisture is larger at the open site and for larger characteristic timescales (10 days). The correlations have a strong seasonal pattern, showing low (negative) correlations in winter and spring and larger (more than 0.5) correlations in summer and autumn. The main reason for low correlations in winter and spring is insufficient masking of the snowpack. Using local snow data masks and soil moisture retrieval in the period December–March, improves the soil moisture agreement in April was improved from negative correlations to 0.68 at the open site and 0.92 at the forest site. Low soil moisture correlations in the summer months may also be due to small-scale precipitation variability and vegetation dynamics mapping, which result in satellite soil moisture overestimation.

Drought is one of the extreme weather events that has been occurring with increasing frequency and complexity as well as having negative effects on water resources and agricultural production. The focus of the present study is to investigate the climate change effects on agricultural drought in the Be River Basin. The SWAT model was applied to simulate the soil moisture content and Standardized Soil Water Index (SSWI) was utilized to estimate the characteristics of agricultural drought. In addition, the future climate conditions for the three periods (2022–2040, 2042–2060, and 2062–2080) were generated by the delta change method based on the outputs of five global climate models. The results show that agricultural drought is anticipated to increase in the frequency, intensity, and duration (up to 168.82%, depending on time and emission scenarios). Moreover, drought events and water shortage in the dry season tend to be more likely to happen soon in the Be River Basin. These results are consistent with the changing trends of related soil moisture. Besides, the results contribute reliable scientific evidence to help managers and policy makers having appropriate plans in the future.

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (gs) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, gs was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that gs at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the gs value and SWSI value was high (R2 > 0.79). Therefore, the gs value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R2 increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R2: 0.69–0.87, RMSE: 0.04–0.08 mol m−2 s−1; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m−2 s−1) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation.

A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998–2000 and 2008–2010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998–2000 was close to that of 2008–2010 (the difference being 13.10 mm). From 1998–2000 to 2008–2010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions.