Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

Wang, Shuai; Zhuang, Qianlai; Jin, Xinxin; Yang, Zijiao; Liu, Hongbin

doi:10.3390/rs12071115

Cited by 36 publications

(17 citation statements)

References 54 publications

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“…The spectral resolution of the sensors significantly influences the quality of soil attributes prediction [ 52 ]. It is, therefore, necessary to utilize remote-sensing data with the appropriate spectral resolution taken across VNIR and SWIR spectrum for accurate soil bulk density predictions [ 52 , 107 ]. Our study also investigated the importance of bands in machine-learning prediction models.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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“…Using the Daycent model, the regional SOC was simulated, using GIS digital soil maps, a 1 m soil depth, and no gravel larger than 2 mm. For the SOCD content calculation formula, you can try to measure SOC density by remote sensing spectrum for model verification [53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Simulated Soil Organic Carbon Density Changes from 1980 to 2016 in Shandong Province Dry Farmlands Using the CENTURY Model

et al. 2020

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The changes in cultivated soil organic carbon (SOC) have significant effects on soil fertility and atmospheric carbon dioxide (CO2) concentration. Shandong Province is an important agricultural and grain production area in China. Dry farmland accounts for 74.15% of the province’s area, so studies on dynamic SOC changes would be helpful to understand its contribution to the Chinese national carbon (C) inventory. Using the spatial overlay analysis of the soil layer (1:10,000,000) and the land use layer (1:10,000,000), 2329 dry farmland soil polygons were obtained to drive the CENTURY model to simulate SOC dynamics in Shandong Province from the period 1980 to 2016. The results showed that the CENTURY model can be used to simulate the dry farmland SOC in Shandong Province. From the period 1980 to 2016, the soil organic carbon storage (SOCS) and soil organic carbon density (SOCD) showed an initial increase and then decreased, especially after reaching a maximum in 2009. In 2016, the SOCS was 290.58 × 106 t, an increase of 26.99 × 106 t compared with 1980. SOCD in the dry farmland increased from 23.69 t C ha−1 in 1980 to 25.94 t C ha−1 in 2016. The dry farmland of Shandong Province was a C sink from 1980 to 2016. Among the four soil orders, inceptisols SOCD dominated, and accounted for 47.81% of the dry farmland, followed by >entisols > vertisols > alfisols. Entisols SOCD growth rate was the highest (0.23 t C ha−1year−1). Compared to 1980, SOCD in 2016 showed an increasing trend in the northeast, northwest and southeast regions, while it followed a downward trend in the southwest.

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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%

“…Some efforts for soil N content estimation by remote sensing data (Landsat 5 TM, Landsat 8 OLI, Sentinel 1A, Sentinel 2A, etc.) have been reported in recent studies [38][39][40][41][42]. Due to the limitation of the available channels of these sensors, these studies usually apply black-box models as the BRT, RF, SVM, etc.…”

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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Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

Cited by 36 publications

References 54 publications

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Simulated Soil Organic Carbon Density Changes from 1980 to 2016 in Shandong Province Dry Farmlands Using the CENTURY Model

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

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