Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data

Yang, Lin; He, Xiaohua; Shen, Feixue; Zhou, Chenghu; Zhu, A‐Xing; Gao, Bingbo; Chen, Ziyue; Li, Manchun

doi:10.1016/j.still.2019.104465

Cited by 60 publications

(32 citation statements)

References 92 publications

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“…This approach can also reduce reflection spectrum errors and thereby improve the accuracy of SOM prediction [79,80]. We calculated the following indices: ratio spectral indices, such as R 11(6)/4(1) [53,54,56], the ratio vegetation index (RVI) [50], and the enhanced vegetation index (EVI) [50,81,82]; difference spectral indices, such as the difference vegetation index (DVI) [50,83]; and normalized difference spectral indices, such as the normalized difference water index (NDWI) [50,[84][85][86] and the normalized difference vegetation index (NDVI) [50,87,88]. Prior studies have proven that the ratio index, difference index, and normalized difference index are conducive to predicting SOM [54,58,89].…”

Section: Spectral Index Constructionmentioning

confidence: 99%

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Zhang

Yang

et al. 2021

Remote Sensing

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Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.

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Section: Spectral Index Constructionmentioning

confidence: 99%

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Zhang

Yang

et al. 2021

Remote Sensing

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“…Several ML models have successfully linked SOC to environmental covariates to extrapolate SOC to unknown locations [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Some of the most popular models are multivariate regression, classical artificial neural networks [13], support vector regression [20], regression trees [17,20], and random forests [15,20,30].…”

Section: Introductionmentioning

confidence: 99%

Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

et al. 2020

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Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.

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“…Vegetation phenology is defined as the development, differentiation, and initiation of plant organs [1]. Accurate retrieval of crop phenology information is a prerequisite for evaluating crop adaptation to climate change, modeling agricultural ecosystem carbon exchange, and predicting future agricultural production [2][3][4][5]. The Intergovernmental Panel on Climate Change has reported a change in global mean temperature of 1.5 • C above pre-industrial levels, along with changes in precipitation and an increased frequency of extreme climate events (IPCC, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…We also quantified the temporal trends of crop phenology and its responses to climatic factors (i.e., temperature and precipitation) and the correlations with crop yields. The objectives of the study are (1) to identify phenological dates of corn and soybean using MODIS NDVI time series in Kentucky from 2000 to 2018; (2) to evaluate the accuracy of estimated crop phenological stages using ground data at the state and county levels; (3) to characterize the spatialtemporal trends of crop phenological stages for corn and soybean in Kentucky during the study period; (4) to examine the correlations between crop planting/harvesting dates and temperature/precipitation variations; and (5) to analyze the effects of crop phenological change on crop yields.…”

Section: Introductionmentioning

confidence: 99%

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

et al. 2021

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Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.

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Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data

Cited by 60 publications

References 92 publications

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

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