Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images

Zhou, Tao; Geng, Yajun; Ji, Cheng; Xu, Xiangrui; Wang, Hong; Pan, Jianjun; Bumberger, Jan; Haase, Dagmar; Lausch, Angela

doi:10.1016/j.scitotenv.2020.142661

Cited by 118 publications

(64 citation statements)

References 139 publications

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“…In terms of overall model precision, the overall accuracy of the RF model was superior to the Cubist model, which is consistent with the results presented by Pouladi [81] in predicting soil organic matter distribution, who mapped and compared soil organic matter predictions by five models (Cubist, Random Forest, Cubist-kriging, Random Forest-kriging, Kriging) and obtained the same result as the model selected for this paper. Akpa [82] predicted SOC variation of different soil layers and the prediction showed that the RF model was slightly preferred to the Cubist model, and the classification of SOC at different resolutions has also been studied from the last few years [48]. However, SAR data were not analyzed with two different resolutions of spectral data.…”

Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

“…The Sentinel-3A OLCI image contains bare soil spectrums band for the surface bare soil monitoring capability, and although the spatial resolution is lower, the short revisit period and large land coverage can provide more comprehensive data for predicting SOC [90]. Combining Sentinel-2A and Sentinel-3A spectral data can make up for the deficiencies of both in many aspects, and can enhance the graphical prediction to a collection of spectral resolution from small to medium to large scale, thus enriching the remote sensing monitoring of soil data by spectral data [48].…”

Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

“…In this paper, the data are divided into four scales to analyze the data variation in the case of different scale data. The multi-scale analysis can better reflect the direct influence of the selected data sources on the prediction results [48], and thus infer the optimal variables for SOC prediction analysis at different regional scales.…”

Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

“…There are some studies to obtain certain results on the distribution and content of surface SOC by Sentinel-1A, Sentinel-2A, and Sentinel-3A data, respectively [30,44], which are limited to one and two kinds of satellite data for analysis during the study [45][46][47], and there are fewer studies to combine the three kinds of data. Meanwhile, the differences of different satellite data at different scales also affect the accuracy of SOC prediction [48]. Therefore, the spatial variation of SOC under different satellite data and different scales needs to be considered comprehensively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Ding

Liu

et al. 2021

Remote Sensing

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As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

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Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

Section: Sentinel-1a/2a/3a For Soc Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Ding

Liu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

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“…Therefore, it is necessary to explore the contribution of remote sensing data to SOC prediction. There have been some studies on SOC prediction based on remote sensing data that achieved good prediction results, especially optical data (e.g., Sentinel-2 [15][16][17][18][19][20][21], Landsat [22][23][24][25][26][27][28], and MODIS satellite data [29][30][31]); their bands cover from visible to short-wave infrared, providing more information. However, the application of optical data is susceptible to weather conditions, especially in the Sichuan Basin where clouds occur most frequently [32], so the available optical data are very limited.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Wang

Zhang

et al. 2021

Remote Sensing

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Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Anand

Jain

Shukla

2023

Concurrency and Computation

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Summary The usage of satellite images has grown rapidly in digital applications. Predicting features in the satellite images using conventional techniques is not an easy task as the images captured from satellites are more complex with highly noisy features. Hence, traditional imaging algorithms cannot analyze the image features and specify the soil textures. In this work, the Tarakeswar satellite images were taken to estimate the soil textures and crops yielding. Hence, to find the soil's chemical properties and suitable crops, the novel bat‐based U‐Net feature prediction system (BUFPS) was developed with the required features. The imported satellite images were initially filtered and entered into the classification phase to forecast the present chemical features and suitable crops in specific soils. After detecting the chemical features, soil textures like sandy, silt, and clay were categorized; the crops like jute, rice, lentil, and potato were considered. Finally, the planned model is executed in the MATLAB environment and has gained outstanding results by achieving the lowest error rate of 8% and a high prediction score of 92%. The proposed model has achieved exact soil texture analysis for complex satellite images. The gained texture analysis outcome is quite better than other existing models.

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Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images

Cited by 118 publications

References 139 publications

Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

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