Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China

Chen, Can; Lv, Qing; Tang, Qing-Nan

doi:10.2166/ws.2022.032

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…This is because the adopted gradient descent algorithm of the BPANN method makes it tend to be trapped in a local minimum and ignore the overall minimum value, which caused the overestimation of the FD-BPANN [37]. To fairly compare the performance of the BPANN method in different areas, this study summarized results and further calculated the RSR values of the previous studies [38,40,41,75,[94][95][96]. As shown in Table 6, for the BPANN method, the ranges of R, RMSE, and RSR are 0.830-0.900, 0.013-0.089 mm 3 /mm 3 , and 0.149-0.470 in the humid area, respectively.…”

Section: The Impact Of Different Methods Mechanisms On Data Fusionmentioning

confidence: 99%

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Zhu,

Zhang,

et al. 2023

Remote Sensing

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In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

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Section: The Impact Of Different Methods Mechanisms On Data Fusionmentioning

confidence: 99%

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Zhu,

Zhang,

et al. 2023

Remote Sensing

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“…Within this area, 27,451 ha are dedicated to paddy fields, 5,600 ha are allocated for irrigation and 9,214 ha are dry land. The area is located in a transitional region characterized by subtropical monsoon, humid climate with recorded yearly rainfall ranging from 1100 to 1200 mm and a temperature of 16 °C [48]. Moreover, the predominant cropping systems within the study area are rice (9.355 tons ha −1 ) and wheat (5.03 tons ha −1 ).…”

Section: Study Areamentioning

confidence: 99%

Evaluating the Efficacy of Sentinel-2B and Landsat-8 for Estimating and Mapping Wheat Straw Cover in Rice–Wheat Fields

Memon,

Chen,

Niu

et al. 2023

Agronomy

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Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region.

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The effect of soil porosity size distribution on integral energy content of water in different moisture slopes

Dai

2023

Water Supply

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The water required by the plant is provided through the porous environment of the soil around the root. Therefore, indicators such as soil texture and effective porosity have been defined to measure the amount of water available to the plant. The pore size determines water energy and the plant's ability to absorb moisture, which is evaluated in this article. Multivariate regression analysis between the integral energy of water in different ranges of humidity as the dependent variable and the indices of soil pore size distribution as the independent variable has been investigated. Due to the fact that none of the indices of the pore size distribution curve have a significant relationship with the integral energy value of water in the permanent wilting point, therefore, it was not possible to estimate this index using the indices of the pore size distribution curve. The results showed that the elongation index of soil pore size distribution curve is the only factor determining the amount of integral energy of water in different soil water contents.

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Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China

Cited by 3 publications

References 29 publications

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Evaluating the Efficacy of Sentinel-2B and Landsat-8 for Estimating and Mapping Wheat Straw Cover in Rice–Wheat Fields

The effect of soil porosity size distribution on integral energy content of water in different moisture slopes

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