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
  • [31] Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring
    Gavahi, Keyhan
    Abbaszadeh, Peyman
    Moradkhani, Hamid
    Zhan, Xiwu
    Hain, Christopher
    [J]. JOURNAL OF HYDROMETEOROLOGY, 2020, 21 (10) : 2293 - 2308
  • [32] Drought reconstruction for north central China from tree rings: the value of the Palmer drought severity index
    Li, Jinbao
    Chen, Fahu
    Cook, Edward R.
    Goua, Xiaohua
    Zhang, Yongxiang
    [J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2007, 27 (07) : 903 - 909
  • [33] Assessing meteorological and agricultural drought characteristics and drought propagation in Guangdong, China
    Zhang, Ruqing
    Wei, Shangguan
    Liu, Jiajin
    Dong, Wenzong
    Wu, Daoyuan
    [J]. JOURNAL OF HYDROLOGY-REGIONAL STUDIES, 2024, 51
  • [34] Assessing the soil moisture drought index for agricultural drought monitoring based on green vegetation fraction retrieval methods
    Wu, Rongjun
    Li, Qi
    [J]. NATURAL HAZARDS, 2021, 108 (01) : 499 - 518
  • [35] Assessing the soil moisture drought index for agricultural drought monitoring based on green vegetation fraction retrieval methods
    Rongjun Wu
    Qi Li
    [J]. Natural Hazards, 2021, 108 : 499 - 518
  • [36] IMPROVING SOIL MOISTURE ESTIMATION BY ASSIMILATING REMOTELY SENSED DATA INTO CROP GROWTH MODEL FOR AGRICULTURAL DROUGHT MONITORING
    Zhou, Hongkui
    Wu, Jianjun
    Li, Xiaohan
    Geng, Guangpo
    Liu, Leizhen
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 4229 - 4232
  • [37] Agricultural drought dynamics in China during 1982-2020: a depiction with satellite remotely sensed soil moisture
    Sun, Hao
    Xu, Qian
    Wang, Yunjia
    Zhao, Zhiyu
    Zhang, Xiaohan
    Liu, Hao
    Gao, Jinhua
    [J]. GISCIENCE & REMOTE SENSING, 2023, 60 (01)
  • [38] Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China
    Zhou, Yuliang
    Zhou, Ping
    Jin, Juliang
    Wu, Chengguo
    Cui, Yi
    Zhang, Yuliang
    Tong, Fang
    [J]. ENVIRONMENTAL RESEARCH, 2022, 212
  • [39] Quantitative Classification of Desertification Severity for Degraded Aquifer Based on Remotely Sensed Drought Assessment
    Sidiropoulos, Pantelis
    Dalezios, Nicolas R.
    Loukas, Athanasios
    Mylopoulos, Nikitas
    Spiliotopoulos, Marios
    Faraslis, Ioannis N.
    Alpanakis, Nikos
    Sakellariou, Stavros
    [J]. HYDROLOGY, 2021, 8 (01)
  • [40] Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring
    Mohammad Ghabaei Sough
    Hamid Zare Abyaneh
    Abolfazl Mosaedi
    [J]. Water Resources Management, 2018, 32 : 3423 - 3440