本文主要研究内容
作者（2019）在《Estimating soil moisture content using laboratory spectral data》一文中研究指出：Monitoring soil moisture is important for agriculture and forestry and plays an essential role in land surface processes as well as providing feedback among the earth’s surface ecosystems. Large-scale regional soil moisture spatial data can be obtained with a reliable and operational approach using remote sensing. In this paper,we provide an operational framework for retrieving soil moisture using laboratory spectral data. The inverted Gaussian function was used to fit soil spectral data, and its feature parameters, including absorption depth(AD) and absorption area(AA), were selected as variables for a soil moisture estimate model. There was a significant correlative relationship between soil moisture and AD, as well as AA near 1400 and 1900 nm. A one-variable linear regression model was established to estimate soil moisture.The model was evaluated using the determination coefficients(R~2), root mean square error and average precision.Four models were established and evaluated in this study.The determination coefficients of the four models ranged from 0.794 to 0.845. The average accuracy for soil moisture estimates ranged from 90 to 92%. The results prove that it is feasible to estimate soil moisture using remote sensing technology.
Abstract
Monitoring soil moisture is important for agriculture and forestry and plays an essential role in land surface processes as well as providing feedback among the earth’s surface ecosystems. Large-scale regional soil moisture spatial data can be obtained with a reliable and operational approach using remote sensing. In this paper,we provide an operational framework for retrieving soil moisture using laboratory spectral data. The inverted Gaussian function was used to fit soil spectral data, and its feature parameters, including absorption depth(AD) and absorption area(AA), were selected as variables for a soil moisture estimate model. There was a significant correlative relationship between soil moisture and AD, as well as AA near 1400 and 1900 nm. A one-variable linear regression model was established to estimate soil moisture.The model was evaluated using the determination coefficients(R~2), root mean square error and average precision.Four models were established and evaluated in this study.The determination coefficients of the four models ranged from 0.794 to 0.845. The average accuracy for soil moisture estimates ranged from 90 to 92%. The results prove that it is feasible to estimate soil moisture using remote sensing technology.
论文参考文献
论文详细介绍
论文作者分别是来自Journal of Forestry Research的，发表于刊物Journal of Forestry Research2019年03期论文，是一篇关于,Journal of Forestry Research2019年03期论文的文章。本文可供学术参考使用，各位学者可以免费参考阅读下载，文章观点不代表本站观点，资料来自Journal of Forestry Research2019年03期论文网站，若本站收录的文献无意侵犯了您的著作版权，请联系我们删除。