多光谱图像
多光谱模式识别
遥感
对数
偏最小二乘回归
数学
均方误差
转化(遗传学)
线性回归
回归分析
反演(地质)
模式识别(心理学)
计算机科学
人工智能
统计
地质学
化学
数学分析
生物化学
古生物学
构造盆地
基因
作者
Siyu Tang,Chong Du,Tangzhe Nie
出处
期刊:Land
[MDPI AG]
日期:2022-04-21
卷期号:11 (5): 608-608
被引量:5
摘要
Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.
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