Spatial-temporal Variation of Soil Moisture in Karst Area based on LST-VI Feature Space

doi:10.11873/j.issn.1004-0323.2022.6.1460

Remote Sensing Technology and Application

2022, Vol. 37

Issue (6): 1460-1471 DOI: 10.11873/j.issn.1004-0323.2022.6.1460

Spatial-temporal Variation of Soil Moisture in Karst Area based on LST-VI Feature Space

Hongbo Yan^1,²(

),Hao Li¹,Xianjian Lu^1,²(

),Jiahua Wang¹

^1.College of Geomatics and Geoinformation，Guilin University of Technology，Guilin 541004，China
^2.Guangxi Key Laboratory of Spatial Information and Geomatics，Guilin 541004，China

Download:

HTML

PDF (6740KB)
Export: BibTeX | EndNote (RIS)

Abstract

Surface soil moisture is a key parameter of terrestrial systems in terms of ground-air energy exchange， and the surface soil moisture in karst areas is an important factor to promote karst action， affect the soil erosion， and cause karst rocky desertification. Therefore， it is of great significance to accurately determine the distribution of surface soil moisture and its dynamic change in karst areas to the safety of ecological and geological environment and regional climate change. Taking the typical karst area of Guangxi as the study area the LST-VI feature space was constructed using MODIS surface temperature data and vegetation index data. Firstly， the applicability of the four different vegetation indices （NDVI， EVI， SAVI， and FVC） in karst areas was compared， and FVC is selected as the optimal vegetation index for the LST-VI feature space in the study area. The soil moisture index M₀ based on the uniform normalized T^*-FVC feature space is derived. The track and regularity of M₀ with time under different underlying surface types such as forest， farmland and karst areas are studied in detail，as well as the spatial distribution changes of M₀ and the causes. The results show that the track and regularity of M₀ with time is similar under the same underlying surface type in the T^*-FVC feature space， indicating that the underlying surface type is an important factor affecting the change of soil moisture. In terms of spatial distribution， the distribution of M₀ in Guangxi has the characteristics of being smaller in summer than in winter， smaller in southwest than in northeast， smaller in karst than in non-karst areas， and the seasonal variation of M₀ in farmland is evident. Overall， the spatio-temporal dynamic change of soil moisture in karst areas has been achieved using the normalized T * -FVC feature space.

Key words: Karst Soil moisture Land surface temperature Vegetation index LST-VI feature space

Received: 19 April 2022 Published: 15 February 2023

ZTFLH:

TP79

Corresponding Authors: Xianjian Lu E-mail: 56403075@qq.com;2008056@glut.edu.cn

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Hongbo Yan
	Hao Li
	Xianjian Lu
	Jiahua Wang

Cite this article:

Hongbo Yan,Hao Li,Xianjian Lu,Jiahua Wang. Spatial-temporal Variation of Soil Moisture in Karst Area based on LST-VI Feature Space. Remote Sensing Technology and Application, 2022, 37(6): 1460-1471.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1460 OR http://www.rsta.ac.cn/EN/Y2022/V37/I6/1460

Fig.1 Guangxi karst landform concentration area

Fig.2 Guangxi weather station distribution map

Fig.3 Schematic diagram of LST-VI triangle feature space

Fig.4 Schematic diagram of LST-VI trapezoidal feature space

Fig.5 T^*-VI feature space diagram

Fig.6 Experimental points distribution for soil moisture change analysis

Fig.7 Flow chart of data processing

Fig.8 Characteristic space of surface temperature and four vegetation indices

Fig.9 Feature space of surface temperature and four vegetation indices after removing outliers

数据类型	指数	干湿边	拟合方程	$R 2$
原始特征空间	NDVI	干边	y=-11.106 5*x+28.677 8	0.94
	NDVI	湿边	y=-4.578 2*x+18.113 5	0.40
	EVI	干边	y=2.265 2*x+23.360 3	0.17
	EVI	湿边	y=3.258 9*x+14.314 9	0.39
	FVC	干边	y=-6.923 5*x+24.931 7	0.91
	FVC	湿边	y=-2.992 7*x+16.870 6	0.46
	SAVI	干边	y=-22.731 4*x+28.932 2	0.92
	SAVI	湿边	y=-4.946 0*x+15.761 3	0.22
剔除温度异常值的特征空间	NDVI	干边	y=-10.879 6*x+28.036 5	0.97
	NDVI	湿边	y=-5.671 6*x+19.309 4	0.73
	EVI	干边	y=1.824 5*x+22.834 6	0.21
	EVI	湿边	y=3.134 7*x+15.149 2	0.58
	FVC	干边	y=-7.568 5*x+24.587 0	0.96
	FVC	湿边	y=-3.126 7*x+17.636 7	0.69
	SAVI	干边	y=-19.954 9*x+27.508 4	0.89
	SAVI	湿边	y=-1.72 9*x+15.708 1	0.04

Table 1 Fitting equations for the characteristic spatial dry and wet edges of four vegetation indices and surface temperature

Fig.10 Trajectory of M₀ with time in forest， farmland， and karst mountains

Fig.11 Spatial distribution map of M₀ in Guangxi region in different periods

Fig.12 Statistical chart of M₀ value domain percentages for each period of 2009—2010 in Guangxi

Table 2 Meteorological station site information

Fig.13 Plot of soil moisture index M₀ versus measured values

1	Yan Hongbo， Zhou Guoqing. Research progress of optical remote sensing inversion method for surface soil moisture［J］. Journal of Subtropical Resources and Environment，2017，12（2）：82-89，95.
1	晏红波，周国清.地表土壤湿度光学遥感反演方法研究进展［J］.亚热带资源与环境学报，2017，12（2）：82-89，95.
2	Yao Chunsheng， Zhang Zengxiang， Wang Xiao.Inversion of soil moisture in Xinjiang using Temperature Vegetation Drought Index （TVDI） method［J］. Remote Sensing Technology and Applications， 2004，18（6）： 473-478.
2	姚春生，张增祥，汪潇. 使用温度植被干旱指数法（TVDI）反演新疆土壤湿度［J］. 遥感技术与应用， 2004，18（6）：473-478.
3	Sandholt I， Rasmussen K， Andersen J. A simple interpretation of the Surface temperature/ vegetation index space for assessment of surface moisture status［J］. Remote Sensing of Environment，2002，79（2）：213-224. DOI： . doi: 10.1016/S0034-4257（01）00274-7
4	Cao Yan， Wang Jie， Li Zufen， et al. Research on soil moisture inversion in Yunnan Province based on VSWI method［J］. China Rural Water Conservancy and Hydropower，2017（4）：22-27.
4	曹言，王杰，李竹芬，等.基于VSWI法的云南省土壤水分反演研究［J］. 中国农村水利水电， 2017（4）：22-27.
5	Wang Pengxin， Gong Jinya， Li Xiaowen. Conditional vegetation temperature index and its application in drought monitoring［J］. Journal of Wuhan University （Information Science Edition）， 2001， 26（5）：412-418.
5	王鹏新，龚健雅，李小文. 条件植被温度指数及其在干旱监测中的应用［J］. 武汉大学学报（信息科学版）， 2001， 26（5）：412-418.
6	Ghulam A， Qin Q， Teyip T， et al. Modified perpendicular drought index （MPDI）： a real-time drought monitoring method ［J］. Photogrammetry & Remote Sensing， 2007，62：150-164.DOI： . doi: 10.1016/j.isprsjprs.2007.03.002
7	Man Yuanwei， Li Jing， Xing Liting. Construction and application of Temperature-soil Moisture-Precipitation Drought Index （TMPDI） based on multi-source remote sensing data［J］.Arid Zone Research， 2021， 38（5）： 1442-1451.
7	满元伟，李净，邢立亭.基于多源遥感数据的温度-土壤湿度-降水干旱指数（TMPDI）的构建与应用［J］.干旱区研究， 2021，38（5）： 1442-1451.
8	Song Tingqiang， Lu Xueli， Lu Mengyao，et al. Construction of an agricultural drought monitoring model based on crop water deficit index［J］. Journal of Agricultural Engineering， 2021， 37（24）：65-72.
8	宋廷强，鲁雪丽，卢梦瑶，等. 基于作物缺水指数的农业干旱监测模型构建［J］. 农业工程学报， 2021， 37（24）：65-72.
9	Carlson T N， Petropoulos G P. A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements： The simplified triangle ［J］. Remote Sensing，2019，40（20）：7716-7729.DOI： . doi: 10. 1080/01431161.2019.1601288
10	Yang Na， Guo Xingguo. Remote sensing inversion of soil moisture in the national grassland ecosystem cover based on NDVI zoning［J］. Soil and Water Conservation Research，2022，29（3）：147-155.
10	杨娜，郭兴国.基于NDVI分区的全国草地生态系统覆盖区土壤水分遥感反演［J］.水土保持研究，2022，29（3）：147-155.
11	Hassanpour R， Zarehaghi D， Neyshabouri M R， et al. Modification on optical trapezoid model for accurate estimation of soil moisture content in a maize growing field ［J］. Journal of Applied Remote Sensing，2020，14（3）：034519.DOI： . doi: 10.1117/1.JRS.14.034519
12	Yuan L， Li L， Zhang T， et al. Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI ［J］. Remote Sensing， 2020， 12： 3040.DOI： . doi: 10.3390/rs12183040
13	Sun Jingxia， Zhang Dongyou， Hou Yuchu. Collaborative inversion of forest surface soil moisture based on multi-source remote sensing data［J］. Remote Sensing Technology and Applications， 2021， 36（3）：564-570.
13	孙景霞，张冬有，侯宇初. 基于多源遥感数据协同反演森林地表土壤水分研究［J］. 遥感技术与应用， 2021， 36（3）：564-570.
14	Peng Shuyan， Zhao Long， Li Tingting， et al. Soil moisture inversion study of typical watersheds in karst trough valley area based on cosmic ray observation［J］. Remote Sensing Technology and Applications， 2021， 36（5）：997-1008.
14	彭书艳，赵龙，李婷婷，等.基于宇宙射线观测的喀斯特槽谷区典型流域土壤水分反演研究［J］.遥感技术与应用，2021，36（5）： 997-1008.
15	Luo W， Xu X， Liu W， et al. UAV based soil moisture remote sensing in a karst mountainous catchment ［J］. Catena，2019，174：478-489.DOI： . doi: 10.1016/j.catena.2018.11.017
16	Yan H， Zhou G， Yang F， et al. DEM correction to the TVDI method on drought monitoring in karst areas ［J］. Remote Sensing， 2019， 40（5-6）： 2166-2189.
17	Wang Sheng， Bao Xiaohuai， Rong Ying，et al. Effects of rainfall intensity on soil moisture and flow production characteristics in southwest karst slopes［J］. Agricultural Modernization Research，2020，41（5）：889-898. doi: 10.13872/j.1000-0275.2020.0084
17	王升，包小怀，容莹，等.降雨强度对西南喀斯特坡地土壤水分及产流特征的影响［J］.农业现代化研究， 2020， 41（5）： 889-898.DOI： . doi: 10.13872/j.1000-0275.2020.0084
18	Sadeghi M， Babaeian E， Tuller S B， et al. The optical trapezoid model： A novel approach to remote sensing of soil moisture applied to Sentinal-2 and Landsat-8 observations ［J］. Remote Sensing of Environment 2017， 198：52-68.DOI： . doi: 10.1016/j.rse.2017.05.041
19	Babaeian E， Sadeghi M， Franz T E，et al.Mapping soil moisture with the Ooptical TRApezoid Model （OPTRAM） based on long-term MODIS observations［J］.Remote Sensing of Environ-ment，2018，211：425-440. DOI： . doi: 10.1016/j.rse. 2018. 04.029
20	Tang Ronglin， Wang Shengli， Yazhen J， et al. A review of remote sensing inversion of surface evapotranspiration based on surface temperature-vegetation index triangle/trapezoid feature space［J］. Journal of Remote Sensing， 2021， 25（1）： 65-82.
20	唐荣林，王晟力，姜亚珍，等.基于地表温度——植被指数三角/梯形特征空间的地表蒸散发遥感反演综述［J］.遥感学报，2021，25（1）：65-82.
21	Carlson T N. An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery ［J］. Sensors， 2007， 7：1612-1629.
22	Zhang D J， Tang R L， Tang B H， et al. A simple method for soil moisture determination from LST-VI feature space using non-linear interpolation based on thermal infrared remotely sensed data［J］. Applied Earth Observations and Remote Sensing， 2015， 8：638-648.
23	Zhao W， Li A， Jin H， et al. Performance evaluation of the triangle-based empirical soil moisture relationship models based on Landsat-5 TM data and in-situ measurements［J］. Remote Sensing，2017，5（55）：2632-2645.DOI： . doi: 10.1109/tgrs. 2017. 2649522
24	Tang R L， Li Z L， Tang B H. An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions： Implementation and validation［J］. Remote Sensing of Environment，2010，114（3）：540-551.
25	Li Qilin， Fan Guangzhou， Zhou Dingwen， et al. Correction of comprehensive meteorological drought index in southwest China［J］. Journal of Southwest Normal University （Natural Science Edition）， 2016， 41（1）：138-146.
25	李奇临，范广洲，周定文，等. 综合气象干旱指数在西南地区的修正［J］. 西南师范大学学报（自然科学版），2016，41（1）：138-146.

[1]	XU Xiao-jun,DU Hua-qiang,ZHOU Guo-mo,FAN Wen-yi. Review on Correlation Analysis of Independent Variables in Estimation Models of Vegetation Biomass Based on Remote Sensing[J]. Remote Sensing Technology and Application, 2008, 23(2): 239 -247 .
[2]	ZHANG Duo-kun,TIAN Zhao-shen,LONG Hui,WANG Hong-qi. A New Method for Automatic Geometric Rectification Based on Image Matching in Remote Sensing Image[J]. Remote Sensing Technology and Application, 2008, 23(5): 545 -550 .
[3]	PAN Jing-hu, LIU Chun-yu. Retrieving Evapotranspiation of Loess Hilly-gully Region Using TSEB Parallel Model Based on Remote Sensing Image[J]. Remote Sensing Technology and Application, 2010, 25(2): 183 -188 .
[4]	Yu Wenping,Ma Mingguo. Validation of the MODIS Land Surface Temperature Products—A Case Study of the Heihe River Basin[J]. Remote Sensing Technology and Application, 2011, 26(6): 705 -712 .
[5]	Li Weiwei，Husi Letu，Chen Hongbin，Shang Huazhe. Estimation of Surface Solar Radiation Using MODIS Satellite Data and RSTAR Model[J]. Remote Sensing Technology and Application, 2017, 32(4): 643 -650 .
[6]	Chunliang Zhao,Wenbo Xu,Jinlong Fan. Validation of Narrow-band Surface Albedo Retrieved from FY-3C MERSI Satellite Data[J]. Remote Sensing Technology and Application, 2020, 35(1): 153 -162 .
[7]	Zongsheng Zheng,Chenyu Hu,Dongmei Huang,Guoliang Zou,Zhaorong Liu,Wei Song. Research on Transfer Learning Methods for Classification of Typhoon Cloud Image[J]. Remote Sensing Technology and Application, 2020, 35(1): 202 -210 .
[8]	Lingmei Jiang,Huizhen Cui,Gongxue Wang,Jianwei Yang,Jian Wang,Fangbo Pan,Xu Su,Xiyao Fang. Progress on Remote Sensing of Snow, Surface Soil Frozen/Thaw State and Soil Moisture[J]. Remote Sensing Technology and Application, 2020, 35(6): 1237 -1262 .
[9]	Zhiyong Wang,LiHua Wang,Zihao Wang,Hao Li. Research on Error of Three-dimensional Imaging Radar Altimeter in Sea Ice Thickness Estimation[J]. Remote Sensing Technology and Application, 2022, 37(6): 1311 -1318 .
[10]	Duo Chu,Caiwang Dunzhu,Lawang Dunzhu,Suolang Tajie,Pingcuo Sangdan,Zhaxi Duoji,Mingma Ciren,Cuo Ping. Monitoring Glacier Avalanches in Tibet Using Sentinel-2 Imagery[J]. Remote Sensing Technology and Application, 2022, 37(6): 1289 -1301 .

Viewed

Full text

Abstract

Cited

Shared

Discussed