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Multivariate Prediction of Soil Aggregate-associated Organic Carbon by Simulating Satellite Sensor Bands by Gao, ChunRui (author);Yan, XiaoBin (author);Qiao, XingXing (author);Wei, KunXi (author);Zhang, XiaoMei (author);Yang, Sha (author);Wang, Chao (author);Yang, WuDe (author);Feng, MeiChen (author);Xiao, LuJie (author);Zhang, MeiJun (author);Zhang, Zhou (author);Shafiq, Fahad (author);Song, XiaoYan (author) is a Agricultural and Biological Sciences article available to read on EtoBox.

What is Multivariate Prediction of Soil Aggregate-associated Organic Carbon by Simulating Satellite Sensor Bands about?

Soil aggregate-associated organic carbon (SAOC) is critical for maintaining soil quality and environmental health. To realize non-destructive and potentially applicable assessments on SAOC by using the hyperspectral technology, the resampling method was adopted to simulate the different satellites bands and the three multivariate methods of support vector machine (SVM), back propagation neural network (BPNN) and random forest (RF) were also implemented to calibrate the predictive models on SAOC. The results showed that the computed Three-band index 2 (TBI2) was proved to be highly related to both soil micro- and macroaggregate-associated organic carbon (OC) among all spectral indexes, and the highest correlation coefficient (R = −0.443) was obtained between the microaggregate-associated OC and TBI2 derived from the simulated bands 7, 4, 10 of Sentinel-2 satellite. For the macroaggregate-associated OC, the good correlation (R = −0.422) was also acquired with TBI2 which was constructed by using the simulated bands 7, 6, and 1 from the Sentinel-2 satellite. The calibrated models show that accepted accuracy for microaggregate-associated OC was achieved with R2 = 0.773, RMSE = 0.853, RP

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Author

Gao, ChunRui (author);Yan, XiaoBin (author);Qiao, XingXing (author);Wei, KunXi (author);Zhang, XiaoMei (author);Yang, Sha (author);Wang, Chao (author);Yang, WuDe (author);Feng, MeiChen (author);Xiao, LuJie (author);Zhang, MeiJun (author);Zhang, Zhou (author);Shafiq, Fahad (author);Song, XiaoYan (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)