Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

He, Xiaohua; Yang, Lin; Li, Anqi; Zhang, Lei; Shen, Feixue; Cai, Yanyan; Zhou, Chenhu

doi:10.1016/j.catena.2021.105442

Cited by 64 publications

(31 citation statements)

References 84 publications

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“…Our results indicated that the Sentinel‐2 data set had a higher contribution than Landsat 8, and the SAR data of Sentinel‐1 were also effective variables for SOC estimating. The SAVI and EVI of Sentinel‐2 (11 Sept. 2021) had the highest RMSE loss in RS covariates (Figure 4); this is because the SAVI and EVI have a significant correlation with vegetation coverage, the SAVI is sensitive to both green and senescent vegetations, and the SAVI and EVI effectively reflect the changes of SOC content, especially in farmland (He et al., 2021). Followed by band7 and band 6 of Sentinel‐2 (25 Mar.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…The image is freely available at Sentinel Scientific Data Hub ( https://scihub.copernicus.eu ). The median of Sentinel-2A satellite images for bare soil periods (December 2020) and green periods of cultivated areas (June 2021) were used to decrease the weak observation effect and obtain more accurate soil surface reflection [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

“…The vegetation indices can reduce the spectrum errors, and thus improve the accuracy of SOC estimation [ 56 ]. The equations used to calculate the EVI [ 50 , 57 ] and NDVI [ 50 , 58 ] are as follows (Eqs 2 and 3 ) …”

Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Kılıç

Gündoğan²,

Günal³

et al. 2022

PLoS ONE

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This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

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“…For a given range of measured SOC contents, The magnitude of error is the highest for national to continental scales, where it typically reaches 15 g•kg −1 or even more, exceeding the observed range of values for single fields or some small homogeneous regions. For a given range of measured SOC contents, When the intraregional variance of the measured SOC contents is small, with a range of measured SOC contents of c. 15 g•kg −1 , models may not be reliable whatever the acquisition date, with very low RPD values, close to 1 or even less: this was observed for Mediterranean soils in the vineyards of the La Peyne Valley region, France [49], but also for luvisols in Northern Wallonia, Belgium [48], or for paddy soils in Lanxi County, China [80]. In such situations, the spatial structure of the data may be of particular relevance to improve the prediction performance, through mixed models incorporating spectral information into spatial models (see Section 3.3).…”

Section: Performances Of Purely Spectral Modelsmentioning

confidence: 98%

“…Basics stats of topsoil SOC content values (top SOC, g•kg −1 ) across 46 study areas with full description of measured sampled sets[18,26,31,[34][35][36]41,44,[47][48][49][50]52,54,55,57,58,61,64,67,[72][73][74][75][76][77][78][79][80][81][82], q1, first quartile; µ, mean; q3; third quartile; σ, standard deviation.…”

mentioning

confidence: 99%

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

et al. 2022

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There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

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Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

Cited by 64 publications

References 84 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

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

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