Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data
The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential for water resource management and sustainable agricultural development. However, natural factors introduce uncertainty and result in poor alignment when predicting farmland SWC, leading to low ac...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/12/3039 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850239884664504320 |
|---|---|
| author | Weihao Yang Ruofan Zhen Fanyue Meng Xiaohang Yang Miao Lu Yingqiang Song |
| author_facet | Weihao Yang Ruofan Zhen Fanyue Meng Xiaohang Yang Miao Lu Yingqiang Song |
| author_sort | Weihao Yang |
| collection | DOAJ |
| description | The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential for water resource management and sustainable agricultural development. However, natural factors introduce uncertainty and result in poor alignment when predicting farmland SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include the largest patch index (LPI), edge density (ED), aggregation index (AI), patch cohesion index (COH), contagion index (CON), landscape division index (DIV), percentage of like adjacencies (PLA), Shannon evenness index (SHEI), and Shannon diversity index (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed to leverage these indices for predicting the spatial variability of SWC. Statistical analysis revealed that landscape indices exhibited skewed distributions and weak linear correlations with SWC (r < 0.2). Despite this, incorporating landscape index variables into the BO–DF model significantly improved prediction accuracy, with R<sup>2</sup> increasing by 35.85%. This model demonstrated a robust nonlinear fitting capability for the spatial variability of SWC. Spatial mapping of SWC using the BO–DF model indicated that high-value areas were predominantly located in the eastern and southern regions of the Yellow River Delta in China. Furthermore, the SHapley additive explanation (SHAP) analysis highlighted that landscape indices were key drivers in predicting SWC. These findings underscore the potential of landscape indices as valuable variables for spatial SWC prediction, supporting regional strategies for sustainable agricultural development. |
| format | Article |
| id | doaj-art-d6262cbe826c439e99a31c8285d464bf |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-d6262cbe826c439e99a31c8285d464bf2025-08-20T02:01:01ZengMDPI AGAgronomy2073-43952024-12-011412303910.3390/agronomy14123039Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture DataWeihao Yang0Ruofan Zhen1Fanyue Meng2Xiaohang Yang3Miao Lu4Yingqiang Song5School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaThe accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential for water resource management and sustainable agricultural development. However, natural factors introduce uncertainty and result in poor alignment when predicting farmland SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include the largest patch index (LPI), edge density (ED), aggregation index (AI), patch cohesion index (COH), contagion index (CON), landscape division index (DIV), percentage of like adjacencies (PLA), Shannon evenness index (SHEI), and Shannon diversity index (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed to leverage these indices for predicting the spatial variability of SWC. Statistical analysis revealed that landscape indices exhibited skewed distributions and weak linear correlations with SWC (r < 0.2). Despite this, incorporating landscape index variables into the BO–DF model significantly improved prediction accuracy, with R<sup>2</sup> increasing by 35.85%. This model demonstrated a robust nonlinear fitting capability for the spatial variability of SWC. Spatial mapping of SWC using the BO–DF model indicated that high-value areas were predominantly located in the eastern and southern regions of the Yellow River Delta in China. Furthermore, the SHapley additive explanation (SHAP) analysis highlighted that landscape indices were key drivers in predicting SWC. These findings underscore the potential of landscape indices as valuable variables for spatial SWC prediction, supporting regional strategies for sustainable agricultural development.https://www.mdpi.com/2073-4395/14/12/3039landscape indexmachine learningsoil water contentfarmland |
| spellingShingle | Weihao Yang Ruofan Zhen Fanyue Meng Xiaohang Yang Miao Lu Yingqiang Song Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data Agronomy landscape index machine learning soil water content farmland |
| title | Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data |
| title_full | Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data |
| title_fullStr | Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data |
| title_full_unstemmed | Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data |
| title_short | Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data |
| title_sort | spatial prediction of soil water content by bayesian optimization deep forest model with landscape index and soil texture data |
| topic | landscape index machine learning soil water content farmland |
| url | https://www.mdpi.com/2073-4395/14/12/3039 |
| work_keys_str_mv | AT weihaoyang spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata AT ruofanzhen spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata AT fanyuemeng spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata AT xiaohangyang spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata AT miaolu spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata AT yingqiangsong spatialpredictionofsoilwatercontentbybayesianoptimizationdeepforestmodelwithlandscapeindexandsoiltexturedata |