A Study on Inversion for Remote Sensing of Desert Soil Moisture Based on Landsat 8 Data

Gao Qi; Peng Jie; Feng Chunhui; Song Qi; Ma Ziqiang; Wang Yuzhen

doi:10.13961/j.cnki.stbctb.2021.01.018

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A Study on Inversion for Remote Sensing of Desert Soil Moisture Based on Landsat 8 Data

- A Study on Inversion for Remote Sensing of Desert Soil Moisture Based on Landsat 8 Data
- Bulletin of Soiland Water Conservation Vol. 41, Issue 1, Pages: 125-131(2021)
- 作者机构：
  
  1. 塔里木大学植物科学学院,新疆,阿拉尔,843300
  2. 北京大学地球与空间科学学院, 遥感与地理信息系统研究所,北京,100000
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2021.01.018
  CLC： S152.7
- Published：2021
- 稿件说明：
移动端阅览
Gao Qi, Peng Jie, Feng Chunhui, et al. A Study on Inversion for Remote Sensing of Desert Soil Moisture Based on Landsat 8 Data[J]. Bulletin of Soiland Water Conservation, 2021, 41(1): 125-131.
DOI：

Gao Qi, Peng Jie, Feng Chunhui, et al. A Study on Inversion for Remote Sensing of Desert Soil Moisture Based on Landsat 8 Data[J]. Bulletin of Soiland Water Conservation, 2021, 41(1): 125-131. DOI： 10.13961/j.cnki.stbctb.2021.01.018.

摘要

[目的

]

分析荒漠土壤水分变化特征，为南疆干旱区荒漠土壤水分遥感监测提供理论依据和方法支持。[方法

]

以Landsat 8数据构建干旱地区荒漠土壤水分建模指示因子，通过优选的26个光谱指数、地表温度（T

）和地形数据（DEM）为建模因子，分别以偏最小二乘（PLSR）、支持向量机（SVM）和随机森林（RF）3种方法构建土壤水分反演模型，对模型进行验证和对比，选取最优模型反演空台里克土壤水分空间分布。[结果

]

①TVDI，NR，GLI等26个优选的光谱指数中，T

和DEM与土壤水分均达极显著相关，可作为南疆干旱区荒漠土壤水分遥感建模的指示因子；②对比3种模型，RF模型建模集和预测集的R

分别为0.93，0.91，预测集RPD为3.90，各评价指标均为最高，PLSR模型精度次之，SVM模型精度最低；③以RF模型反演研究区表层土壤水分，在不同土地利用分类中土壤水分分布特征存在明显差异，特别在盐结皮区域的差异尤为突出。[结论

]

利用光谱指数、环境因子和地形数据构建的多因子、多指数综合的模型能较高精度地反演干旱区荒漠表层土壤水分，对研究该地区土地荒漠化和生态环境治理具有一定参考价值。

Abstract

[Objective] The variation characteristics of desert soil moisture were analyzed to provide a theoretical basis and methodology for remote-sensing monitoring of soil water content in the arid desert of Southern Xinjiang Uygur Autonomous Region.[Methods] The desert soil moisture modeling indicators were constructed based on Landsat 8 data. An optimal 26 spectral index

land surface temperature (Ts)

and digital elevation model data (DEM) were selected as modeling factors

and the soil water inversion model was constructed using the partial least squares regression (PLSR)

support vector machine (SVM)

and random forest (RF) algorithms. After model validation and comparison

the spatial distribution of soil moisture in Kongtailike was retrieved using the optimal model.[Results] ① The temperature vegetation dryness index

GLI

and other 26 preferred spectral indices

as well as TS and DEM

were significantly correlated with soil moisture. They could be used as indicators for remote-sensing modeling of desert soil moisture in the arid area of Southern Xinjiang. ② Among the three models

the R2 of calibration and validation based on the RF model were 0.93 and 0.91

respectively

and the RPD of validation was 3.90

which was the highest. The PLSR model accuracy was the second best

and the SVM model accuracy was the lowest. ③ The surface soil moisture in the study area was retrieved by the RF model

and the characteristics of soil moisture distribution in different ground classifications were noticeably different

especially in the salt crust region.[Conclusion] The comprehensive use of spectral index

environmental factors

and terrain factors could result in the inversion of the soil water content in arid areas with a higher accuracy

providing scientific value for the desertification and ecological environment control in this area.

关键词

Keywords

references

包青岭,丁建丽,王敬哲,等.基于VIC模型模拟的干旱区土壤水分及其时空变化特征[J].生态学报,2020,40(9):3048-3059.

Anagnostopoulos V, Petropoulos G P, Ireland G, et al. A modernized version of a 1 D soil vegetation atmosphere transfer model for improving its future use in land surface interactions studies[J]. Environmental Modelling & Software, 2017,90:147-156.

郭交,刘健,宁纪锋,等.基于Sentinel多源数据的农田地表土壤水分反演模型构建与验证[J].农业工程学报,2019,35(14):71-78.

Leng Pei, Song Xiaoning, Duan Sibo, et al. A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,52:338-348.

蔡亮红,丁建丽.基于改进植被指数土壤水分遥感反演[J].干旱区地理,2017,40(6):1248-1255.

李华朋,张树清,高自强,等. MODIS植被指数监测农业干旱的适宜性评价[J].光谱学与光谱分析,2013,33(3):756-761.

Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval from AMSR-E[J]. Ieee Transactions on Geoscience and Remote Sensing, 2003,41(2):215-229.

李柏贞,周广胜.干旱指标研究进展[J].生态学报,2014,34(5):1043-1052.

De Jeu R A M, Holmes T R H, Parinussa R M, et al. A spatially coherent global soil moisture product with improved temporal resolution[J]. Journal of Hydrology, 2014,516:284-296.

Pan Ming, Sahoo A K, Wood E F. Improving soil moisture retrievals from a physically-based radiative transfer model[J]. Remote Sensing of Environment, 2014,140:130-140.

Price J C. On the analysis of thermal infrared imagery:The limited utility of apparent thermal inertia[J]. Remote Sensing of Environment, 1985,18(1):59-73.

Ghulam A, Qin Qiming, Zhan Zhiming. Designing of the perpendicular drought index[J]. Environmental Geology, 2007,52(6):1045-1052.

张学艺,李剑萍,秦其明,等.几种干旱监测模型在宁夏的对比应用[J].农业工程学报,2009,25(8):18-23.

姚云军,秦其明,赵少华,等.基于MODIS短波红外光谱特征的土壤含水量反演[J].红外与毫米波学报,2011,30(1):9-14.

孙灏,陈云浩,孙洪泉.典型农业干旱遥感监测指数的比较及分类体系[J].农业工程学报,2012,28(14):147-154.

葛少青,张剑,孙文,等.3种干旱指数在干旱区沼泽湿地土壤水分遥感反演中的应用[J].生态学报,2018,38(7):2299-2307.

Du Lingtong, Tian Qingjiu, Yu Tao, et al. A comprehensive drought monitoring method integrating MODIS and TRMM data[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,23(1):245-253.

彭杰.荒漠土壤盐渍化遥感监测与开垦方案分析[D].浙江杭州:浙江大学,2019.

刘新路,彭杰,冯春晖,等.基于电磁感应仪数据的南疆棉田土壤电导率反演模型研究[J].土壤学报,2020,57(3):646-655.

蔡亮红,丁建丽,魏阳.基于多源数据的土壤水分反演及空间分异格局研究[J].土壤学报,2017,54(5):1057-1067.

刘宁.不同土地利用方式下黄河三角洲土壤特性空间变异研究[D].山东泰安:山东农业大学,2007.

董雪,田静,吴骅,等.考虑土壤水分影响的比辐射率方法在地表温度反演中的应用[J].资源科学,2017,39(8):1592-1604.

蔡国印.基于MODIS数据的地表温度、热惯量反演研究及其在土壤水分、地气间热交换方面的应用[D].北京:中国科学院研究生院(遥感应用研究所),2006.

姜红,玉素甫江·如素力,拜合提尼沙·阿不都克日木,等.基于支持向量机回归算法的土壤水分光学与微波遥感协同反演[J].地理与地理信息科学,2017,33(6):30-36.

Peng Jie, Biswas A, Jiang Qingsong, et al. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China[J]. Geoderma, 2019,337:1309-1319.

王思楠,李瑞平,李夏子.基于综合干旱指数的毛乌素沙地腹部土壤水分反演及分布[J].农业工程学报,2019,35(13):113-121.

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