Modeling Scattering Power of Soil Particle Based on K-M Theory

IF 4.7 2区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-29 DOI:10.1109/JSTARS.2024.3487645
Yiting Fan;Mingchang Wang;Liheng Liang;Ziwei Liu;Xue Ji;Zhiguo Meng;Yilin Bao
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Abstract

Soil particle size is an important indicator in soil systems, it can provide important assistance for the agricultural work. In order to address the weakness of traditional soil particle size measuring work, which are time-consuming, labor-intensive, and have limited applicability. This study utilizes the Mie theory and the Kubelka–Munk theory as the precondition, establish an empirical formula between the scattering power and the soil particle size. The study collected surface soil samples from Nong'an, Changchun City, Jilin Province, including black soil, brown soil, sandy soil, and each saline sample, based on visible and near-infrared spectroscopy. Prepare soil samples with a particle size range of 2.5–0.15 mm through drying, grinding, and sieving operations, combining scattering power parameters in the K-M theory to construct an empirical formula for it and soil particle. After verified by comparing different empirical formulas are suitable for the measured data, assume the inverse proportion formula added correction term is the most appropriate. The conclusion is there is a strong linear relationship between the scattering power and the reciprocal of particle size. The average fitting accuracy of the 400–2400 nm wavelength band reaches 94.45%, root mean square error ( $\text{RMSE}$ ) reaches 0.0354 mm. After removing outliers, the fitting accuracy can reach up to 95.77%, $\text{RMSE}$ up to 0.0337 mm. Proved there is a very high analytical relationship between soil particle size and scattering power parameters in K-M theory. The empirical formula also can find supported by Mie theory and S-shape R ( D ) function, and has a high transferability from the laboratory to Landsat8 satellite board, the accuracy can reach to about 90% on SWIR band, showed good generalization ability.
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基于 K-M 理论的土壤颗粒散射力建模
土壤粒径是土壤系统中的一项重要指标,可为农业工作提供重要帮助。针对传统土壤粒度测量工作耗时、耗力、适用性有限的弱点。本研究以 Mie 理论和 Kubelka-Munk 理论为前提,建立了散射功率与土壤粒度之间的经验公式。本研究采集了吉林省长春市农安县的地表土壤样品,包括黑土、棕壤、沙土和各盐碱地样品,基于可见光和近红外光谱分析。通过干燥、研磨、筛分等操作制备粒径范围为 2.5-0.15 毫米的土壤样品,结合 K-M 理论中的散射功率参数,构建其与土壤颗粒的经验公式。通过比较不同的经验公式是否适用于实测数据进行验证后,认为反比例公式中加入修正项是最合适的。结论是散射功率与粒径的倒数之间存在很强的线性关系。400-2400 nm 波段的平均拟合精度达到 94.45%,均方根误差($text{RMSE}$)达到 0.0354 mm。剔除异常值后,拟合精度可达 95.77%,均方根误差($text{RMSE}$)为 0.0337 mm。证明在 K-M 理论中,土壤粒度与散射功率参数之间存在很高的解析关系。该经验公式还可以得到米氏理论和S形R(D)函数的支持,并且从实验室到Landsat8卫星板具有很高的可移植性,在SWIR波段上精度可达90%左右,显示了良好的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.30
自引率
10.90%
发文量
563
审稿时长
4.7 months
期刊介绍: The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.
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