Spatio‐temporal variation of near‐surface soil water content in China from 1988 to 2016

Liao, Kaihua; Liu, Ya; Zhu, Qing

doi:10.1111/sum.12600

Cited by 8 publications

(3 citation statements)

References 64 publications

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“…Since soil moisture stations are unevenly and sparsely distributed in China, the soil moisture observational data series is shorter and limited (Wang et al., 2011). Remote sensing techniques provide soil moisture data with a finer spatiotemporal resolution and can only monitor a few centimeters of soil moisture below the surface (Abowarda et al., 2021; Liao et al., 2020; Liu et al., 2020; Long et al., 2019; Yuan et al., 2015). The surface soil moisture obtained through remote sensing is more easily affected by vegetation cover and atmospheric conditions, and the issue of a shorter sequence remains (Berg & Sheffield, 2018; Long et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Wu¹,

Su²,

Singh

et al. 2021

Water Resources Research

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Monitoring and prediction of agricultural drought are paramount to food security at the global and regional scales, particularly under the influence of climate change and anthropogenic activities. Soil moisture is an effective indicator for monitoring and characterizing agricultural drought. Soil moisture (agricultural drought) is mainly affected by precipitation (meteorological drought) and temperature (hot conditions). Owing to the flexibility of vine copulas in handling multidimensional variables by decomposing them into pair copula constructions (PCCs), we propose a novel drought prediction method using three predictors, namely antecedent meteorological drought, previous hot conditions, and persistent agricultural drought, based on the conditional distributions of C‐vine copulas in a four‐dimensional scenario. The proposed model was applied to agricultural drought (characterized by the standardized soil moisture index (SSI)) prediction with 1–2‐months lead time for the summer season (i.e., August at a 6‐months timescale) in China. Taking two severe agricultural drought events that occurred in many regions across China in August of 2006 and 2014 as validation cases, the SSI predictions with 1–2‐months lead time using the conditional C‐vine copulas model were found to be generally consistent with the corresponding historical SSI observations in most parts of China. Performance evaluation using the Nash‐Sutcliffe efficiency (NSE), coefficient of determination (R2), and F1 score (F1S) for different climate regions also indicated that this model provided a reliable prediction of agricultural drought for most areas of China. The outcome of this study can serve as a guidance for drought prediction, early warning, and drought mitigation.

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Section: Discussionmentioning

confidence: 99%

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Wu¹,

Su²,

Singh

et al. 2021

Water Resources Research

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“…and fully disclose the changing trend of the system in different temporal scales. At present, wavelet analysis is becoming a common tool for analyzing non-stationary time series in signal processing, seismic exploration, hydrometeorology and many other fields (Biswas et al, 2013;Liao et al, 2020). A wavelet is a wave-like oscillation with amplitude that begins at zero, increases, and then decreases back to zero.…”

Section: Methodsmentioning

confidence: 99%

Influence of river-lake isolation on the water level variations of Caizi Lake, lower reach of the Yangtze River

Liao

Zhu

et al. 2021

J. Geogr. Sci.

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In order to explore the water level variations of Caizi Lake under river-lake isolation, the monthly water level of the Chefuling station in Caizi Lake from 1989 to 2018 and the daily water level, rainfall and flow of local hydrological stations in 2018 were analyzed by using the Mann-Kendall trend test and wavelet analysis. Results showed that the difference of the average water level of Caizi Lake between the flood and dry seasons was 3.34 m, with a multi-year average water level of 10.42 m above sea level. The first and second main periods of the water level of Caizi Lake were 128 and 18 months, respectively, with 4 and 29 "up-down" cycles, respectively. From 2018, the next 3-4 years were likely to be the low water level period. The water level of Caizi Lake was significantly correlated with that of the Anqing hydrological station of the Yangtze River (r=0.824, P<0.01). In addition, the current hydrological staging of Caizi Lake was about 30 days behind than before the sluice was built. Under the dual influences of the river-lake isolation and the Yangtze-to-Huaihe Water Diversion Project (YHWD), the hydrological regime change of Caizi Lake and its eco-environmental effect needed long-term monitoring and research.

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“…This is because the collected dataset is retrieved in real time over a short period of time and at repeated time intervals. Furthermore, RS is a high‐efficiency, widely distributed technique that is ideal for quantifying and monitoring soil VWC (Blatchford et al., 2019; Goward et al., 2002; Liao et al., 2021; Weidong et al., 2002; Zhang et al., 2014). Some examples are the Sentinel and Landsat groups of satellite images that are available free of charge and the newly developed unmanned aerial vehicles (UAVs) with multispectral and thermal or even hyperspectral cameras.…”

Section: Introductionmentioning

confidence: 99%

Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices

Abebrese,

Biney,

Kara

et al. 2023

Soil Use and Management

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Knowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point‐based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large‐scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in‐situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no‐till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel‐2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems.

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Spatio‐temporal variation of near‐surface soil water content in China from 1988 to 2016

Cited by 8 publications

References 64 publications

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

Influence of river-lake isolation on the water level variations of Caizi Lake, lower reach of the Yangtze River

Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices

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