Regional scale soil moisture content estimation based on multi-source remote sensing parameters

IF 2.6 3区地球科学 Q2 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

International Journal of Remote Sensing Pub Date : 2020-05-02 DOI:10.1080/01431161.2019.1701723

Mireguli Ainiwaer, Jianli Ding, Nijat Kasim, Jingzhe Wang, Jinjie Wang

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引用次数: 22

Abstract

ABSTRACT Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized diﬀerence index (NDI) ((B 769~797 – B 848~881/B 769~797 + B 848~881); (B 842 – B 740/B 842 + B 740)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R 2) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R 2 = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R 2 values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring.

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基于多源遥感参数的区域尺度土壤水分估算

土壤含水量(SMC)是作物生长发育的基本条件，也是作物产量预测和干旱监测的关键参数。利用遥感技术进行大尺度同步观测的一个优点是，它提供了在大范围内动态监测土壤含水量的可能性。本研究旨在探索将地面高光谱数据与多光谱遥感(Sentinel-2)数据相结合，在区域尺度上精确估算土壤含水量的可行性。结果表明，不同的数学变换均不同程度地提高了土壤光谱反射率与SMC之间的相关性。高光谱优化指数归一化差指数(NDI) ((b769 ~797 - b848 ~881/ b769 ~797 + b848 ~881);(B 842 - B 740/B 842 + B 740))变换反射率(往复对数一阶导数(Log (1/R))′，往复对数二阶导数(Log (1/R))′′)具有显著的相关性(相关系数(R) = 0.61;r = 0.47)，且相关系数高于差异指数(DI)和比值指数(RI)。从以优化指标为自变量的12个预测模型的性能来看，中心波长反射率模型(Log (1/R))”和平均波长反射率模型(Log (1/R))具有较高的验证系数(决定系数(r2) = 0.61，均方根误差(RMSE) = 4.09%，残差预测偏差(RPD) = 1.82;r2 = 0.69, RMSE = 3.48%， RPD = 1.91)。在验证精度时，模型的r2值为0.619和0.701。结果表明，双波段高光谱优化指数(NDI)是快速准确估算土壤含水量的最佳指标。结合地面高光谱数据和卫星遥感影像进行区域尺度土壤水分预测，为地空一体化土壤水分遥感监测提供了科学参考。

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

International Journal of Remote Sensing 工程技术-成像科学与照相技术

CiteScore

7.00

自引率

5.90%

发文量

219

审稿时长

4.8 months

期刊介绍： The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include: • Remotely sensed data collection, analysis, interpretation and display. • Surveying from space, air, water and ground platforms. • Imaging and related sensors. • Image processing. • Use of remotely sensed data. • Economic surveys and cost-benefit analyses. • Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).