Assessing the remotely sensed Drought Severity Index for agricultural drought monitoring and impact analysis in North China

被引:107
|
作者
Zhang, Jie [1 ]
Mu, Qiaozhen [2 ]
Huang, Jianxi [3 ]
机构
[1] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[2] Univ Montana, Dept Ecosyst & Conservat Sci, Numer Terradynam Simulat Grp, Missoula, MT 59812 USA
[3] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
关键词
Drought; Remote sensing; MODIS DSI; Agriculture; Winter wheat yield; Moisture; WHEAT YIELD ESTIMATION; SURFACE MOISTURE STATUS; LEAF-AREA INDEX; SOIL-MOISTURE; UNITED-STATES; EVAPOTRANSPIRATION ALGORITHM; VEGETATION INDEX; CROP PRODUCTION; SATELLITE DATA; WOFOST MODEL;
D O I
10.1016/j.ecolind.2015.11.062
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Remote sensing can provide real-time and dynamic information for terrestrial ecosystems, facilitating effective drought monitoring. A recently proposed remotely sensed Drought Severity Index (DSI), integrating both vegetation condition and evapotranspiration information, shows considerable potential for drought monitoring at the global scale. However, there has been little research on regional DSI applications, especially concerning agricultural drought. As the most important winter wheat producing region in China, North China has suffered from frequent droughts in recent years, demonstrating high demand for efficient agricultural drought monitoring and drought impact analyses. In this paper, the capability of the MODIS DSI for agricultural drought monitoring was evaluated and the drought impacts on winter wheat yield were assessed for 5 provinces in North China. First, the MODIS DSI was compared with precipitation and soil moisture at the province level to examine its capability for characterizing moisture status. Then specifically for agricultural drought monitoring, the MODIS DSI was evaluated against agricultural drought severity at the province level. The impacts of agricultural drought on winter wheat yield during the main growing season were also explored using 8-day MODIS DSI data. Overall, the MODIS DSI is generally effective for characterizing moisture conditions at the province level, with varying ability during the main winter wheat growing season and the best relationship observed in April during the jointing and booting stages. The MODIS DSI agrees well with agricultural drought severity at the province level, with better performance in rainfed-dominated than irrigation-dominated regions. Drought shows varying impacts on winter wheat yield at different stages of the main growing season, with the most significant impacts found during the heading and grain-filling stages, which could be used as the key alert period for effective agricultural drought monitoring. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:296 / 309
页数:14
相关论文
共 50 条
  • [1] Assessing the Remotely Sensed Evaporative Drought Index for Drought Monitoring over Northeast China
    Zhang, Lilin
    Yao, Yunjun
    Bei, Xiangyi
    Jia, Kun
    Zhang, Xiaotong
    Xie, Xianhong
    Jiang, Bo
    Shang, Ke
    Xu, Jia
    Chen, Xiaowei
    [J]. REMOTE SENSING, 2019, 11 (17)
  • [2] Assessing the Consistency of Remotely Sensed Multiple Drought Indices for Monitoring Drought Phenomena in Continental China
    Li, Ziying
    Han, Yang
    Hao, Tianyi
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (08): : 5490 - 5502
  • [3] A REMOTELY SENSED GLOBAL TERRESTRIAL DROUGHT SEVERITY INDEX
    Mu, Qiaozhen
    Zhao, Maosheng
    Kimball, John S.
    McDowell, Nathan G.
    Running, Steven W.
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2013, 94 (01) : 83 - 98
  • [4] Drought monitoring from the remotely sensed temperature and vegetation index in China
    Xin, JF
    Tian, GL
    Liu, QH
    Chen, LF
    Xin, XZ
    [J]. IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 389 - 391
  • [5] Correlation analysis between drought and winter wheat yields based on remotely sensed drought severity index
    College of Information and Electrical Engineering, China Agricultural University, Beijing
    100083, China
    不详
    20742, United States
    [J]. Nongye Jixie Xuebao, 3 (166-173):
  • [6] Remotely sensed drought index and its responses to meteorological drought in Southwest China
    Wang, Hongshuo
    Lin, Hui
    Liu, Desheng
    [J]. REMOTE SENSING LETTERS, 2014, 5 (05) : 413 - 422
  • [7] An agricultural drought severity index using quasi-climatological anomalies of remotely sensed data
    Lessel, Jerrod
    Sweeney, Alexandra
    Ceccato, Pietro
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2016, 37 (04) : 913 - 925
  • [8] Assessing Agricultural Drought in the Anthropocene: A Modified Palmer Drought Severity Index
    Yang, Mingzhi
    Xiao, Weihua
    Zhao, Yong
    Li, Xudong
    Lu, Fan
    Lu, Chuiyu
    Chen, Yan
    [J]. WATER, 2017, 9 (10)
  • [9] Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield
    Huang, Jianxi
    Zhuo, Wen
    Li, Ying
    Huang, Ran
    Sedano, Fernando
    Su, Wei
    Dong, Jinwei
    Tian, Liyan
    Huang, Yanbo
    Zhu, Dehai
    Zhang, Xiaodong
    [J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2020, 13 (04) : 504 - 526
  • [10] Evaluation of evapotranspiration deficit index for agricultural drought monitoring in North China
    Wu, Rongjun
    Liu, Yibo
    Xing, Xiaoyong
    [J]. JOURNAL OF HYDROLOGY, 2021, 596