Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Zhou, Tao; Geng, Yajun; Chen, Jie; Sun, Chuanliang; Haase, Dagmar; Lausch, Angela

doi:10.3390/rs11242934

Cited by 19 publications

(11 citation statements)

References 92 publications

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“…The kernel function we select can affect the prediction performance of the SVM model. The RBF kernel, which has been used widely in soil properties mapping studies (Ahmadet al., 2010; B. Wang et al., 2018; Zhou et al., 2019; Zhou et al., 2020), was used as the kernel function in this study. There are two parameters that need to be defined for the RBF kernel: the bandwidth parameter “σ” and the penalty parameter “C.” These parameters were determined by the “caret” package of the R software (Kuhn, 2008).…”

Section: Methodsmentioning

confidence: 99%

“…Yang & Guo, 2019). But the application of SAR images has been limited in predicting SOC characteristics of complexity, diversity, and availability (Ma et al, 2017;Veloso et al, 2017;Zhou et al, 2019). The Sentinel series satellites launched by the European Space Agency provide a large number of optical and SAR RS imagery with high spatial resolution (Sentinel-1, 5-20 m; Sentinel-2, 10-60 m) and short revisit period (Sentinel-1, 6 d; Sentinel-2, 5 d), which can make it possible to predict SOC at a higher accuracy.…”

Section: Core Ideasmentioning

confidence: 99%

“…Recently, some studies have explored the potential of SAR data to predict soil chemical proprieties, showing that the backscatter coefficient has a large correlation with soil properties such as SOC and soil N (Takada et al., 2009; R. Yang & Guo, 2019). But the application of SAR images has been limited in predicting SOC characteristics of complexity, diversity, and availability (Ma et al., 2017; Veloso et al., 2017; Zhou et al., 2019). The Sentinel series satellites launched by the European Space Agency provide a large number of optical and SAR RS imagery with high spatial resolution (Sentinel‐1, 5–20 m; Sentinel‐2, 10–60 m) and short revisit period (Sentinel‐1, 6 d; Sentinel‐2, 5 d), which can make it possible to predict SOC at a higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Yang

Xiao-min

Guo

et al. 2022

Soil Science Soc of Amer J

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Soil organic C (SOC) plays an important role in soil quality. Thus, it is of great significance to know the spatial distribution characteristics of SOC. Environmental variables, such as natural predictors, remote sensing (RS) variables, and digital soil mapping approaches have been widely used in SOC prediction. However, it is still challenging to determine which methods and variables are effective to map SOC in farmland. In this study, we compare the performance of three machine learning models, including random forest, Cubist, and the support vector machine model for the prediction of SOC contents using RS variables, topographic variables, climate variables, soil property variables, and spatial position variables as environmental covariates. The recursive feature elimination (RFE) technique was used to select optimal variables. Of the 130 sampling sites of the cultivated layer that were collected in Shanggao county, 70% of the data were used for training and 30% for validation. Prediction accuracy and uncertainty was quantified using the bootstrap approach with 100 iterations. The performance of the model was verified by the mean absolute error, RMSE, R2, and Lin's concordance correlation coefficient (CCC); the SD was used as a measure of prediction uncertainty. Results showed that the Cubist model combined with the RFE method (Cubist_RFE) had a mean R2, RMSE, and SD of .84, 2.39 g kg–1, and 0.73 g kg–1, respectively, which showed the highest prediction accuracy and the lowest uncertainty. The soil property variables (soil alkaline hydrolysis N and pH) and RS variables (soil adjusted vegetation index, enhanced vegetation index, and band7 of Sentinel‐2) were the controlling factors of SOC prediction. Thus, the results indicate the potential of the Cubist model in SOC prediction, and the combination of the optical and radar RS data is a viable way for predicting SOC.

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Section: Methodsmentioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Yang

Xiao-min

Guo

et al. 2022

Soil Science Soc of Amer J

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“…However, the estimation accuracy of the N content of field soils was generally low when remote sensing spectroscopy data was applied [36][37][38][39][40][41][42][43], on account of the restrictions as the variability of the land cover, the spectral variation due to the atmospheric influence, the data uncertainty due to the variation of the temperature and the humidity on a large spatial scale, etc. [16,31,44].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Chen

Zhu

et al. 2022

Remote Sensing

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Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.

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“…Recently, various machine learning (ML) algorithms have become very popular in digital soil mapping, including: multiple linear regression (MLR) [23], regression trees (RT) [8], regression kriging (RK) [11,23], generalized additive model (GAM) [24], random forest (RF) [21], artificial neural networks (ANN) [25], and support vector regression (SVR) [26]. Early reviews on the strengths and weaknesses of these machine learning algorithms have been discussed in some published articles [6,27,28].…”

Section: Introductionmentioning

confidence: 99%

Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

Luo

Jin

et al. 2020

Remote Sensing

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Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

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Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Cited by 19 publications

References 92 publications

Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

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