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基于高光谱特征的土壤含水量遥感反演方法综述

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晏红波, 韦晚秋, 卢献健, 杨志高, 黎振宝. 2022. 基于高光谱特征的土壤含水量遥感反演方法综述. 自然资源遥感, 34(2): 1-9. doi: 10.6046/zrzyyg.2021126
引用本文: 晏红波, 韦晚秋, 卢献健, 杨志高, 黎振宝. 2022. 基于高光谱特征的土壤含水量遥感反演方法综述. 自然资源遥感, 34(2): 1-9. doi: 10.6046/zrzyyg.2021126
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YAN Hongbo, WEI Wanqiu, LU Xianjian, YANG Zhigao, LI Zhenbao. 2022. A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics. Remote Sensing for Natural Resources, 34(2): 1-9. doi: 10.6046/zrzyyg.2021126
Citation: YAN Hongbo, WEI Wanqiu, LU Xianjian, YANG Zhigao, LI Zhenbao. 2022. A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics. Remote Sensing for Natural Resources, 34(2): 1-9. doi: 10.6046/zrzyyg.2021126
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基于高光谱特征的土壤含水量遥感反演方法综述

  • 1.桂林理工大学测绘地理信息学院,桂林 541004；2.广西空间信息与测绘重点实验室,桂林 541004

  • 基金项目:

    广西空间信息与测绘重点实验室开放基金项目”广西地区农业干旱遥感监测及预警方法研究”(桂科能19-050-11-23)

    广西自然科学基金项目”基于高分影像的喀斯特地区土壤水分反演关键问题研究”(2022GXNSFBA035639)

    国家自然科学基金项目”地基和星载GNSS-R融合的花岗岩滑坡高时空分辨率土壤湿度反演研究”(42064003)

详细信息
    作者简介: 晏红波(1983-),女,博士,副教授,主要从事遥感数据处理及其应用的研究。Email: 56403075@qq.com。

  • 中图分类号: TP79

A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics

  • 1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China；2. Guangxi Laboratory of Spatial Information and Mapping, Guilin 541004,China

    摘要
  • 在不同时空尺度上快速、准确地估算土壤含水量是水文、环境、地质、农业和气候变化等领域研究的重点内容。目前,如何准确获取土壤含水量仍然是一项具有挑战性的任务,过去传统的基于”点”的土壤取样和分析方法费时费力,利用遥感影像反演土壤含水量具有范围广、时效快、成本低、动态对比性强等优势。其中,在高光谱遥感中土壤含水量与土壤反射率波长范围有关,至今已有多种方法被用来描述土壤含水量与高光谱遥感的关系,综述了现有的基于高光谱反射率估计土壤含水量的方法,并将其分为4大类: 光谱反射率法、函数法、模型法和机器学习法。通过比较分析了不同方法在精度、复杂性、辅助数据要求、不同模式下的可操作性以及对土壤类型的依赖性等方面的潜力和局限性,并对未来土壤含水量-高光谱反射率方面的研究提出了相应建议。
    The rapid and accurate estimation of soil water content at different spatial and temporal scales is key research content in the fields of hydrology, environment, geology, agriculture, and climate change. However, it is still a challenge to obtain accurate soil water content presently. In the past, the traditional point-based soil sampling and analysis methods were time-consuming and laborious. By contrast, retrieving soil water content using remote sensing images has the advantages of a wide range, high timeliness, low cost, and strong dynamic contrast. In hyperspectral remote sensing, soil water content is related to the wavelength range of soil reflectance. So far, many methods have been used to describe the relationships between soil water content and hyperspectral remote sensing. This paper summarized existing methods for estimating soil water content based on hyperspectral reflectance and divided them into four categories: spectral reflectance methods, function methods, model methods, and machine learning methods. Moreover, this paper compared and analyzed the potential and limitations of different methods in terms of accuracy, complexity, auxiliary data requirements, operability under different modes, and the dependence on soil types. Finally, this study put forward corresponding suggestions for future research on the relationships between soil water content and hyperspectral reflectance.
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收稿日期:  2021-04-23
刊出日期:  2022-06-20

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