Remote Sensing Monitoring of Drought Based on Landsat8 and NDVI-Ts Characteristic Space Method

被引：2
作者：
Liang, Shouzhen [1 ,2 ]
Liu, Tao [1 ,2 ]
Chen, Zhen [3 ]
Sui, Xueyan [1 ,2 ]
Hou, Xuehui [1 ,2 ]
Wang, Meng [1 ,2 ]
Yao, Huimin [1 ,2 ]
机构：
[1] Shandong Inst Agr Sustainable Dev, Jinan, Peoples R China
[2] Minist Agr, Key Lab East China Urban Agr, Jinan, Peoples R China
[3] Weifang Bur Land Resources, Weifang, Peoples R China
来源：
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, PT I | 2019年 / 545卷
关键词：
Remote sensing; Drought; NDVI; Temperature; SURFACE; INDEX; EMISSIVITY;
D O I：
10.1007/978-3-030-06137-1_12
中图分类号：
S2 [农业工程];
学科分类号：
0828 ;
摘要：
Drought has an important impact to agriculture, and its monitoring is very necessary for many regions subjected to drought in Shandong province. Gaomi city of Shandong was chosen as a study area to probe remote sensing monitoring method of drought. Landsat8 satellite data and soil volumetric moisture content data from filed investigation were used. Temperature- vegetation method was adopted to monitor drought in the study area. The results showed that land surface temperature was negatively related to NDVI. Temperature vegetation dryness index (TVDI) had a significant correlation with soil water content. TVDI can reflect the drought in the study. It suggests that TVDI can be used as a effective index to monitor drought in the study area.
引用
收藏
页码：116 / 125
页数：10
相关论文
共 50 条
[31]   Remote sensing monitoring of drought response of spring maize based on vegetation indexes [J].
Liu D. ;
Feng R. ;
Yu C. ;
Tang Q. ;
Guo C. .
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2019, 35 (20) :152-161
[32]   Comparative evaluation of drought indices for monitoring drought based on remote sensing data [J].
Wei, Wei ;
Zhang, Jing ;
Zhou, Liang ;
Xie, Binbin ;
Zhou, Junju ;
Li, Chuanhua .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (16) :20408-20425
[33]   Remote Sensing-Based Drought Monitoring in Iran's Sistan and Balouchestan Province [J].
Omidvar, Kamal ;
Nabavizadeh, Masoume ;
Rousta, Iman ;
Olafsson, Haraldur .
ATMOSPHERE, 2024, 15 (10)
[34]   Construction of a drought monitoring model using deep learning based on multi-source remote sensing data [J].
Shen, Runping ;
Huang, Anqi ;
Li, Bolun ;
Guo, Jia .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 79 :48-57
[35]   A component-based system for agricultural drought monitoring by remote sensing [J].
Dong, Heng ;
Li, Jun ;
Yuan, Yanbin ;
You, Lin ;
Chen, Chao .
PLOS ONE, 2017, 12 (12)
[36]   Effect of Spectral/Spatial Transformation on Remote Sensing Image for NDVI-Based Drought Detection Analysis [J].
Akbar, Fikri ;
Suryana, Nanna ;
Hussin, Burairah .
INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2011), 2011, 8285
[37]   Atmospheric correction method of GF-1 data based on Landsat8 product algorithm flow [J].
Zhang X. ;
Li L. ;
Wang Y. ;
Zhang Q. ;
Li G. .
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2020, 36 (01) :182-192
[38]   A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale [J].
Alahacoon, Niranga ;
Edirisinghe, Mahesh .
GEOMATICS NATURAL HAZARDS & RISK, 2022, 13 (01) :762-799
[39]   DYNAMIC MONITORING OF DROUGHT CONDITONS IN HENAN PROVINCE BASED ON LAI-TS SPACE [J].
Liu, Ying ;
Yue, Hui .
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, :5097-5100
[40]   Landsat 8 Remote Sensing Image Based on Deep Residual Fully Convolutional Network [J].
Zhang Jiaqiang ;
Li Xiaoyan ;
Li Liyuan ;
Sun Pengcheng ;
Su Xiaofeng ;
Hu Tingliang ;
Chen Fansheng .
LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (10)
12345