Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content

Ye, Zhishan; Sheng, Ziheng; Liu, Xiaoyan; Ma, Youhua; Wang, Ruochen; Ding, Shiwei; Liu, Mengqian; Li, Zijie; Wang, Qiang

doi:10.3390/su132414055

Cited by 12 publications

(3 citation statements)

References 80 publications

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“…Gaofen-6 (GF-6), which was successfully launched on 2 June 2018, is a satellite specially designed for monitoring agricultural applications [29], and it is mainly applied to industries such as precision agriculture observation and forestry resources survey. Regarding the research progress on LULC classification using GF-6 PMS data, several papers have reported the effectiveness of its application in urban land cover, agricultural monitoring, and forest resources survey [30][31][32][33]. These studies show the potential advantages of GF-6 satellite data in distinguishing fine-grained surface features, especially in the detailed delineation of farmland boundaries.…”

Section: Introductionmentioning

confidence: 99%

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Cao,

Dai,

Zhang

et al. 2024

Agriculture

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This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively.

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Section: Introductionmentioning

confidence: 99%

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Cao,

Dai,

Zhang

et al. 2024

Agriculture

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“…Current SOM mapping based on remote sensing images is mainly based on a single satellite sensor [14]. Ye et al used Gaofen-6 images to predict soil SOM in Hefei, China, based on random forest (RF), light gradient boosting machine (LightGBM), gradient boosting tree (GBDT), and extreme boosting machine (XGBoost) prediction models [15].…”

Section: Introductionmentioning

confidence: 99%

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Ma,

Luo,

Meng

et al. 2024

Sustainability

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Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R² is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.

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“…This model established the relationship between the spectral reflectance of ground samples and their SOM content. When non-sampling points spectral reflectance data is fed into it, the SOM content is the result of the regression equation's calculation (Ye et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Modelling and Mapping of Soil Organic Matter in Doukkala Plain, Moroccan Semi-Arid Region

Bel-Lahbib¹,

Namr²,

Hafyani³

et al. 2022

Ecol. Eng. Environ. Technol.

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The Doukkala plains one of the largest irrigated areas in Morocco with a very important agricultural potential. With the integration of new technologies in agriculture, the plain has been subjected to intensive agriculture which has negative impacts on soil quality especially the soil organic matter loss. Therefore, the objective of this study is to combine remote sensing and modelling for monitoring of organic matter content. The obtained results showed that all the examined models showed satisfactory results in the prediction of organic matter with a coefficient of determination R 2 ranging from 0.58 to 0.71 and the Root Mean Square Error (RMSE) varied 0.25 and 0.26%. Based on the findings, we can infer that this approach is both efficient and valid for modelling and mapping soil organic matter and may moreover be applied for other areas with same characteristics.

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Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content

Cited by 12 publications

References 80 publications

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Modelling and Mapping of Soil Organic Matter in Doukkala Plain, Moroccan Semi-Arid Region

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