Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

Luo, Chong; Zhang, Xinle; Wang, Yihao; Men, Zhibo; Liu, Huanjun

doi:10.1016/j.still.2022.105325

Cited by 46 publications

(10 citation statements)

References 48 publications

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“…The QA band of the L8 products was applied as a cloud F I G U R E 1 Location of sampling points and digital elevation model (DEM) across the Europe mask to minimize the effects of cloud and cloud shadows. According to previous studies (Luo, Zhang, Meng, et al, 2022;Luo, Zhang, Wang, et al, 2022), the multiyear synthetic images could provide more robust results for soil prediction models. Hence, the image acquisition date was extended from the years of 2013 to 2016, and all L8 images acquired here were used to calculate median composite images.…”

Section: Remote Sensing Variablesmentioning

confidence: 99%

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Xiao

Ming-hao

Geng

et al. 2023

Soil Use and Management

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Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co‐kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten‐fold cross‐validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.

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Section: Remote Sensing Variablesmentioning

confidence: 99%

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Xiao

Ming-hao

Geng

et al. 2023

Soil Use and Management

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“…In the past few years, advanced data mining methods, such as deep learning, have been explored to improve spatio-temporal soil carbon modeling (Wadoux 2019. A potential advantage of deep learning is its ability to deal with multiple source data (both spatial and spatio-temporal data) (Wang et al 2022). Due to the 'black-box' characteristics of deep learning or ML methods, the incorporation of pedological knowledge would improve the model performance in mapping soil carbon distribution conforming to the law of soil genesis.…”

Section: Incorporating Pedological Knowledge Into Statistical/ml Modelsmentioning

confidence: 99%

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

Huang¹,

Yang²,

Zhang³

et al. 2022

Environ. Res. Lett.

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Cropland soil carbon not only serves food security but also contributes to the stability of the terrestrial ecosystem carbon pool due to the strong interconnection with atmospheric carbon dioxide. Therefore, the better monitoring of soil carbon in cropland is helpful for carbon sequestration and sustainable soil management. However, severe anthropogenic disturbance in cropland mainly in gentle terrain creates uncertainty in obtaining accurate soil information with limited sample data. Within the past twenty years, digital soil mapping has been recognized as a promising technology in mapping soil carbon. Herein, to advance existing knowledge and highlight new directions, the article reviews the research on mapping soil carbon in cropland from 2005 to 2021. There is a significant shift from linear statistical models to machine learning models because nonlinear models may be more efficient in explaining the complex soil-environment relationship. Climate covariates and parent material play an important role in soil carbon on the regional scale, while on a local scale, the variability of soil carbon often depends on topography, agricultural management, and soil properties. Recently, several kinds of agricultural covariates have been explored in mapping soil carbon based on survey or remote sensing technique, while, obtaining agricultural covariates with high resolution remains a challenge. Based on the review, we concluded several challenges in three categories: sampling, agricultural covariates, and representation of soil processes in models. We thus propose a conceptual framework with four future strategies: representative sampling strategies, establishing standardized monitoring and sharing system to acquire more efficient crop management information, exploring time-series sensing data, as well as integrating pedological knowledge into predictive models. It is intended that this review will support prospective researchers by providing knowledge clusters and gaps concerning the digital mapping of soil carbon in cropland.

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“…In the calibration and validation modelling, we applied 3 statistical indicators to analyse the model accuracy, namely, the coefficient of determination (R 2 ), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ). The best model has the highest R 2 and RPIQ and the lowest RMSE. In addition, we used a 1:1 line to measure the deviation in the measured SOM values from the estimated SOM values.…”

Section: Statistical Analysis and Model Evaluationmentioning

confidence: 99%

“…Soil organic matter (SOM) is a vital indicator for assessing the quality of arable land [1,2]. SOM can loosen soil and improve its physicochemical properties by accelerating the formation of soil agglomerates [3].…”

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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Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

Cited by 46 publications

References 48 publications

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

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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