卫星遥感数据在土壤生产力评估中的应用

Q3 Engineering

Russian Engineering Research Pub Date : 2024-07-29 DOI:10.3103/s1068798x24701119

S. S. Ogorodnikov, I. I. Lebedev

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引用次数: 0

摘要

摘要建立了植物覆盖状况和土壤腐殖质含量的各种指数之间的关系。通过使用线性回归和随机森林算法分析哨兵-2 号卫星不同通道的图像，确定了植被的光谱反射特征。结果发现，与 RVI 和 NDVI 指数相比，植被指数 NDI45 和 PSSRa 对土壤中的腐殖质含量更为敏感。判定系数（R2 = 0.59）和均方误差（MSE = 0.00 014）表明拟议模型具有良好的预测能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Remote Sensing Data from Satellites in Assessing Soil Productivity

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Remote Sensing Data from Satellites in Assessing Soil Productivity

Abstract

Relations are established between various indices for the condition of the plant cover and humus content of the soil. Spectral reflective characteristics of vegetation are determined by analysis of images in different channels from the Sentinel-2 satellite using linear regression and random forest algorithms. It is found that the vegetation indices NDI45 and PSSRa are more sensitive to the humus content in the soil than the indices RVI and NDVI. The determination coefficient (R² = 0.59) and mean square error (MSE = 0.00 014) indicate that the predictive power of the proposed model is good.

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来源期刊

Russian Engineering Research Engineering-Industrial and Manufacturing Engineering

CiteScore

1.20

自引率

0.00%

发文量

226

期刊介绍： Russian Engineering Research is a journal that publishes articles on mechanical and production engineering. The journal covers the development of different branches of mechanical engineering, new technologies, and tools for machine and materials design. Emphasis is on operations research and production-line layout, industrial robots and manipulators, quality control and process engineering, kinematic analysis of machine assemblies, and computerized integrated manufacturing systems.