Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods

Lu, Muyuan; Liu, Yuan; Liu, Guijian

doi:10.1016/j.compag.2022.107246

Cited by 11 publications

(5 citation statements)

References 64 publications

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“…Research found that environmental covariates VARI, GCI, and GNDVI introduced into the model had a positive effect. Among them, VARI improved the ability of soil identification and resistance to atmospheric interference [75]; GCI can measure the content of chlorophyll in various plants, reflecting the physiological state of vegetation, that is, showing the activity and abundance of vegetation growth, reflecting the level of nutrients such as organic matter from the side [76]; GNDVI can monitor the nitrogen content of vegetation, and nitrogen has a high degree of correlation and parallelism with organic matter, also expressing organic matter content to a certain extent [77]. For SOM model prediction, these vegetation indices may provide a new idea, through in-depth analysis of this vegetation index, providing a new perspective for future research.…”

Section: Discussionmentioning

confidence: 99%

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Mei,

Tong,

Zhang

et al. 2024

Sustainability

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Traditional soil organic matter mapping is mostly polygonal drawing, which is even more difficult to accurately depict in complex terrain areas. The spatial distribution of soil organic matter is closely related to agricultural production, natural resources, environmental governance, and socio-economic development. Efficiently, economically, and accurately obtaining information on changes in soil organic matter in areas with diverse topography is an urgent problem to be solved. Mingguang City has a high research value because of its unique topography and natural landscape. To solve the problem of soil organic matter mapping in this area, this study will construct an excellent organic matter prediction model. Using 173 soil survey samples (123 for training and 50 for testing), the optimal feature variable subsets selected from 31 environmental variables through Pearson correlation, stepwise regression-variance inflation factor, and recursive feature elimination models based on different algorithms were employed. Each selected feature subset was then used to construct organic matter prediction models using multiple advanced machine learning algorithms. By comparing accuracy validation and model performance, the organic matter prediction model suitable for Mingguang City (RFE-RF_SVM) was obtained, that is, the prediction model of organic matter based on support vector machines with the feature variables screened by the feature recursive elimination algorithm of random forest with RMSE = 3.504, VSI = 0.036, and R-squared = 0.730. Furthermore, the analysis focused on assessing the significance of the predictive factors. The mapping results of this study show that the soil organic matter content in the central and northwestern parts of the study area is low, and the reasons for this situation are different. The central part is mainly caused by the change of land use and topography, while the northwestern part is caused by the loose soil structure caused by the parent material. The government can take targeted measures to improve the soil in the areas with poor organic matter.

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Section: Discussionmentioning

confidence: 99%

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Mei,

Tong,

Zhang

et al. 2024

Sustainability

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“…The authors used Sentinel images (10 m) and the random forest algorithm, which obviously affects the accuracy of the model. Some studies (Lu, Liu and Liu, 2022) suggest that the environmental variables (mainly clay index) have the greatest influence on the prediction accuracy of SOM. Using time series 7 MOD09A1 images (Zhang et al, 2021) noted that the pixel dates of the training samples and precipitation data were the main factors controlling the model performance.…”

Section: Discussionmentioning

confidence: 99%

“…While the primary application of NDVI lies in vegetation analysis, it can indirectly infer SOM levels as high vegetation areas often correlate with increased SOM content. Many techniques underscore the use of the NDVI as an identifier for bare soil surfaces (Ding et al, 2016;Tian et al, 2021;Lu, Liu and Liu, 2022). Guidelines have been established, which suggest scales of NDVI values for reflectance in the RED (0.25) and NIR (0.3) ranges for open ground (the average NDVI value is thus is equal to 0.025) (Koroleva et al, 2017;Kulyanitsa et al, 2017;Luo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…A multitude of studies have established a correlation between SOM content in soils with pixel values of multispectral satellite data, often employing multiple linear regression (MLR) or alternative mathematical methodologies (Lu, Liu and Liu, 2022;Luo et al, 2021;Ahmed and Iqbal, 2014;Bouasria et al, 2020). Some authors (Gasmi et al, 2021;Demattê et al, 2007) have successfully used MLR to predict clay content.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of remote sensing methods for statistical estimation of organic matter in soils

Belenok,

Hebryn-Baidy,

Bіelousova

et al. 2023

Earth sci. res. j.

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The availability of reliable information on the physicochemical properties of soils is a necessary tool for maintaining and improving fertility and effective optimization of agricultural land management in many countries. However, ground-based research methods require significant financial and time resources. It has been established that methods based on remote sensing data are an efficient, accurate, and less costly solution for studying different types of soil cover parameters. This work aims to determine the predicted indicator of humus content in soils in selected regions of the Kyiv region (Ukraine) with the corresponding soil types. For this, the spectral properties of chernozem soils were investigated based on Landsat 8 OLI satellite images. A mosaic of the mean spectral reflectance values for the study period (2013-2015) was created using the Google Earth Engine. The vegetation indices NDSI, NDWI, NDBI, MSAVI, and NDVI were used to identify bare soils. Using multiple linear regression, an optimal F-Comparing Nested Model was created for predicting humus content in soils, including seven parameters. The model's accuracy was estimated with such indicators R=0.95, R2= 0.90, σy = 0.16 %. The approach based on the proposed model can be used to support the adoption of the necessary management decisions to improve soil fertility and maintain balanced land use.

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“…SOM is traditionally estimated based on field soil sampling for laboratory assay analysis by using geostatistical methods and synergistic landscape environmental factors [7,8]. However, as SOM exhibits obvious variability in the spatial distribution in larger-scale areas, geostatistical methods need the collection of a sufficiently large number of sample points to ensure their representativeness and high precision [9].…”

Section: Introductionmentioning

confidence: 99%

Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land

Wang

Yong

2022

Agriculture

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Soil organic matter (SOM) is vital for assessing the quality of arable land. A fast and reliable estimation of SOM is important to predict the soil carbon stock in cropland. In this study, we aimed to explore the potential of combining multitemporal Sentinel-2A imagery and random forest (RF) to improve the accuracy of SOM estimates in the plough layer for cultivated land at a regional scale. The field data of SOM content were utilized along with multitemporal Sentinel-2A images acquired over three years during the bare soil period to develop spectral indices. The best bands and spectral indices were selected as prediction variables by using the RF algorithm. Partial least squares (PLS), geographically weighted regression (GWR), and RF were employed to calibrate spectral indices for the SOM content, and the optimal calibration model was used for the mapping of the SOM content in arable land at a regional scale. The results showed the following. (1) The multitemporal image estimation model outperformed the single-temporal image estimation model. The estimation model that utilized the optimal bands and spectral indices as prediction variables usually had better accuracy than the models based on full spectral data. (2) For the SOM content estimates, the performance was better with RF than with PLS and GWR in almost all cases. (3) The most accurate SOM estimation in the case area was achieved by using multitemporal images from 2018 and the RF calibration model based on the optimal bands and spectral indices as prediction variables, with R2val (coefficient of determination of the validation data set) = 0.67, RMSEval (root mean square error of the validation dataset) = 2.05, and RPIQval(ratio of performance to interquartile range of the validation dataset) = 3.36. (4) The estimated SOM content in the plough layer for cultivated land throughout the study area ranged from 16.17 to 36.98 g kg−1 and exhibited an increasing trend from north to south. In the current study, we developed a framework that combines multitemporal remote sensing imagery and RF for the SOM estimation, which can improve the accuracy of quantitative SOM estimations, provide a dynamic, rapid, and low-cost technique for understanding soil fertility, and offer an early warning of changes in soil quality.

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Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods

Cited by 11 publications

References 64 publications

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Application of remote sensing methods for statistical estimation of organic matter in soils

Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land

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