Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (4): 1716-1730.DOI: 10.1016/j.jia.2025.04.027

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基于多源遥感数据的黑龙江省农业干旱监测

  

  • 收稿日期:2025-02-18 修回日期:2025-04-18 接受日期:2025-03-20 出版日期:2026-04-20 发布日期:2026-03-16

Monitoring of agricultural drought based on multi-source remote sensing data in Heilongjiang Province, China

Chenfa Jiang1, 2*, Changhui Ma1*, Sibo Duan1# , Xiaoxiao Min1, Youzhi Zhang3, Dandan Li1, Xia Zhang2   

  1. 1 State Key Laboratory of Efficient Utilization of Arable Land in China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

    2 Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China

    3 Institute of Agricultural Remote Sensing and Information, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China

  • Received:2025-02-18 Revised:2025-04-18 Accepted:2025-03-20 Online:2026-04-20 Published:2026-03-16
  • About author:Chenfa Jiang, E-mail: 22120405011@hgu.edu.cn; Changhui Ma, E-mail: machanghui@caas.cn; #Correspondence Sibo Duan, E-mail: duansibo@caas.cn * These authors contributed equally to this study.
  • Supported by:

    This work was supported by the National Key Research and Development Program of China (2022YFD2001105).

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摘要： 农业是社会经济发展的基础，受到天气和气候条件的高度影响。干旱是农业发展和粮食安全面临的最严重威胁之一。目前，基于气象站的地面干旱监测和基于遥感数据的干旱监测均存在局限性，包括更新频率低、覆盖范围有限和精度不足。为了监测中国黑龙江省的农业干旱，本研究利用多源遥感数据，整合降水、地表温度、土壤湿度和植被指数，构建了不同时间尺度(3、6、9和12个月)的多源复合干旱指数(MCDIs)。本研究利用多种遥感数据计算了一系列单一干旱指数，包括降水条件指数(PCI)、土壤湿度条件指数(SMCI)、植被条件指数(VCI)和温度条件指数(TCI)，并采用多变量线性回归方法将这些指数整合为MCDIs。分析结果表明，与单一干旱指数相比，MCDIs与标准化降水蒸散指数(SPEI)具有更高的相关性。在不同MCDIs与受灾作物面积及主要粮食产量的相关性分析中，MCDI-9与受灾作物面积的相关性最高，而 MCDI-12与粮食产量的相关性最高。这表明，不同时间尺度下的 MCDI-9和 MCDI-12是农业干旱的更优指标。MCDI 的时空分析结果表明，黑龙江省的干旱主要发生在早春，并逐渐从大兴安岭地区向东南平原扩展。随后干旱在夏季逐渐缓解，并在秋收期结束。因此，本研究构建的MCDIs可作为黑龙江省及类似地区有效的干旱监测方法和指标。


Abstract:

Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.  Drought is one of the most significant threats to agricultural development and food security.  Currently, in-situ drought monitoring based on weather stations and based on remote sensing data has limitations, including infrequent updates, limited coverage, and low accuracy.  This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province, China.  We developed multi-source composite drought indices (MCDIs) at various timescales (3, 6, 9, and 12 months) by integrating precipitation, land surface temperature, soil moisture, and vegetation indices.  Utilizing remote sensing data from various sources, we calculated a series of single drought indices, which are the precipitation condition index, soil moisture condition index, vegetation condition index, and temperature condition index.  These are then integrated into MCDIs using a multivariable linear regression approach.  The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index (SPEI) than single drought indices.  When examining the correlation between different MCDIs and the affected area of crops and major grain production, MCDI-9 showed the highest correlation with the affected area of crops, while MCDI-12 showed the highest correlation with grain production.  This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.  The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring, gradually spreading from the Greater Khingan Mountains region to the southeastern plains.  The drought gradually alleviates during the summer, ending by the autumn harvest period.  Therefore, the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.

Key words: agricultural drought ,  spatio-temporal monitoring ,  multi-source remote sensing data ,  SPEI ,  Heilongjiang Province