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Wang Zhaofei

Bio: Wang Zhaofei is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Image fusion & Soil organic matter. The author has an hindex of 1, co-authored 2 publications receiving 11 citations.

Papers
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Journal ArticleDOI
TL;DR: The spatiotemporal dynamics of SOM highlighted the necessity of modeling with fused remote sensing images, and more effective modeling could be expected with the continued increase in SOM in future.

31 citations

Patent
23 Jul 2019
TL;DR: In this paper, a space-time refined soil organic matter content remote sensing dynamic inversion method is proposed to capture the change trend of the farmland productivity so as to accurately guide the agricultural production, and an inversion model framework containing the fused image multiband reflectivity and the multiple vegetation indexes is constructed, an optimal index combination mode is selected from an index selection pool through methods such as relation fitting, selection comparison and autocorrelation verification.
Abstract: The invention relates to a space-time refined soil organic matter content remote sensing dynamic inversion method. The method comprises the steps that the Sentine-2 and Sentine-3 are selected as remote sensing image sources, and a Gram-Schmidt spectrum sharpening image fusion method is used for image fusion, so that the image data used by the method has high space, high time and high spectral resolution. An inversion model framework containing the fused image multiband reflectivity and the multiple vegetation indexes is constructed, an optimal index combination mode is selected from an index selection pool through methods such as relation fitting, selection comparison and autocorrelation verification, and an image inversion method which is based on image fusion and can capture space-time change characteristics of soil organic matter most effectively is constructed and formed. According to the soil organic matter content remote sensing inversion method, the high spectral resolution andthe high space-time precision are both considered, a space-time refined soil organic matter content change diagram in the regional scale can be obtained, and the important basic data and the decisionsupport are provided for exploring the change trend of the farmland productivity so as to accurately guide the agricultural production.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This study analyzed and compared the potential of satellite sensors with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland and revealed that remote sensing variables were the best predictors for soil prediction models.

64 citations

Journal ArticleDOI
01 Jan 2022-Catena
TL;DR: In this article, the authors used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR), to predict soil organic carbon (SOC) in an arid agroecosystem in Iran.
Abstract: In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.

54 citations

Journal ArticleDOI
TL;DR: In this paper, the relationship between soil organic carbon (SOC) and remotely sensed and easily accessible variables have been rarely reported, and the main objective of the present study is to estimate SOC using the remote sensing of satellite images as well as some field variables for the Shazand Watershed, Iran.

38 citations

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

17 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used the Ebinur Lake Basin in arid and semi-arid regions as the study area, and used the sentinel data as the main data source, and combined with 16 kinds of DEM derivatives and climate data as analysis.
Abstract: 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.

15 citations