Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China

Yang, Haizhen; Zhang, Xiaokang; Xu, Mengyuan; Shao, Shuai; Xiang, Wei; Liu, Wenqi; Wu, Danqian; Ma, Yuyang; Bao, Yilin; Zhang, Xinle; Liu, Huanjun

doi:10.1016/j.catena.2019.104259

Cited by 45 publications

(13 citation statements)

References 47 publications

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“…Remote sensing variables, which had been widely used in DSM studies, were proven to be efficient in SOC prediction (Grinand et al., 2017; Poppiel et al., 2019; H. Yang et al., 2020; Zeraatpisheh et al., 2019). Many studies reported that the vegetation indices calculated by optical images could reflect the variations in soil properties due to the strong relationship between vegetation cover and soil properties (D. Chen et al., 2019; Mahmoudabadi et al., 2017).…”

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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“…At the time of study, there is no vegetation and snow cover on the soil surface (Figure 1d,e). There is neither a large area of vegetation nor a large area of snow cover, as April and May occur in the "bare soil period" [38].…”

Section: Study Areamentioning

confidence: 99%

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Meng

Bao

et al. 2021

Remote Sensing

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In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.

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“…Secondly, vegetation commonly covers the topsoil in many regions and the interference of vegetation can increase the uncertainty of soil moisture prediction, especially for satellite soil moisture products that are captured from above the vegetation, to reduce the uncertainty caused by the cover of vegetation. Yang et al [56] acquired satellite sensor images in periods when the soil was bare to reduce the effect of vegetation. However, this method is unsuitable for studies at the global scale.…”

Section: Uncertainty In the Soilmentioning

confidence: 99%

Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion

et al. 2022

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Soil moisture, a crucial property for Earth surface research, has been focused widely in various studies. The Soil Moisture Active Passive (SMAP) global products at 36 km and 9 km (called P36 and AP9 in this research) have been published from April 2015. However, the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015. In this research, the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme (VIPSTF-SW) to simulate the 9 km AP9 data after failure of the active radar. The method makes full use of all the historical AP9 and P36 data available between April and July 2015. As a result, 8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020, by downscaling the corresponding 8-day composited P36 data. The available AP9 data and in situ reference data were used to validate the predicted 9 km data. Generally, the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product. The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology, climatology, ecology, and many other fields at the global scale.

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Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China

Cited by 45 publications

References 47 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

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion

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