Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

Kang, Yingdong; Li, Xiaoyan; Mao, Dehua; Wang, Zongming; Liang, Mingxuan

doi:10.3390/s20247005

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…SOC measurement methods have traditionally utilized chemometric methods; however, the collection and processing of large numbers of samples and indoor experimental analysis are labor intensive and time consuming [ 9 , 10 ]. With the rapid development of remote sensing technology, remote sensing has become widely used for digital mapping of soil properties (DSM) [ 11 , 12 , 13 ]. DSM is an effective method to reduce the field sampling and experimental costs associated with monitoring, analyzing and managing SOC from spatial and temporal perspectives [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Xie

Ding

et al. 2022

Sensors

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Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

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Section: Introductionmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Xie

Ding

et al. 2022

Sensors

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“…The geographical and vertical distributions of wetland SOCc in China's Liao River Basin (LRB) were investigated using a combination method that included ANN and ordinary kriging (OK). ANN-OK proved successful in predicting wetland SOCc on a regional scale [99,100]…”

Section: To Measure Soil Organic Carbonmentioning

confidence: 99%

Application of Artificial Neural Networks in Soil Science Research

Keerthana

2024

ACRI

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Artificial Neural Networks utilize high-performance computation and large data technology, allowing research to generate new prospects in agriculture. ANN is currently a preferred technique for crop yield prediction, forecasting, and classification in biological science domains. Among different agriculture fields, soil science research plays a vital role in understanding and managing the complex processes occurring within the soil environment as oil is a complex system with dynamic surface layers that differ from the other parts of the matrix. Due to the increasing accessibility of innovative computing techniques, Artificial Neural Networks (ANNs) have developed into useful tools for modeling and forecasting soil-related activities. The numerous applications of ANNs in soil science research, with a focus on how well they can classify soils, assess soil fertility, forecast soil erosion, and estimate soil moisture. They are vital tools for identifying soil types, evaluating fertility levels, predicting erosion, and soil moisture estimation. ANN models were effective at predicting soil characteristics like pH, organic carbon concentration, and clay content. By training on vast datasets that contain the chemical, biological, and physical properties of soil, ANNs are able to accurately predict different soil types and enable land-use planning, precision farming, and environmental management. This mini-review focuses on ANN approaches that possess the potential to increase our understanding of soil science and encourage informed decisions for soil management and conservation.

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Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou

Guo,

Wang,

Zhang

et al. 2024

Environ Geochem Health

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Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

Cited by 6 publications

References 51 publications

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Application of Artificial Neural Networks in Soil Science Research

Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou

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