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Smart Agriculture ?? 2021, Vol. 3 ?? Issue (2): 1-14.doi: 10.12133/j.smartag.2021.3.2.202104-SA002

? 專題--空間信息技術農業應用 ? 上一篇    下一篇

農業干旱衛星遙感監測與預測研究進展

韓東(), 王鵬新(), 張悅, 田惠仁, 周西嘉   

  1. 中國農業大學 信息與電氣工程學院,北京 100083
  • 收稿日期:2021-04-15 修回日期:2021-05-25 出版日期:2021-06-30 發布日期:2021-08-25
  • 基金資助:
    國家自然科學基金面上項目(41871336)
  • 作者簡介:韓 東(1994-),男,博士研究生,研究方向為農業定量遙感。E-mail:hd5877@cau.edu.cn。
  • 通訊作者: 王鵬新 E-mail:hd5877@cau.edu.cn;wangpx@cau.edu.cn

Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing

HAN Dong(), WANG Pengxin(), ZHANG Yue, TIAN Huiren, ZHOU Xijia   

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Received:2021-04-15 Revised:2021-05-25 Online:2021-06-30 Published:2021-08-25
  • corresponding?author: Pengxin WANG E-mail:hd5877@cau.edu.cn;wangpx@cau.edu.cn

摘要:

干旱是影響農業生產的主要氣候因素。傳統的農業干旱監測主要是基于氣象和水文數據,雖然能提供監測點上較為精確的干旱監測結果,但是在監測面上的農業干旱時,仍存在一定的局限。遙感技術的快速發展,尤其是目前在軌的衛星傳感器感測的電磁波段涵蓋了可見光、近紅外、熱紅外和微波等波段,為區域尺度農業干旱監測提供了新的手段。充分利用衛星遙感數據獲得的豐富地表信息進行農業干旱監測和預測具有重要的研究意義。本文從遙感指數方法、土壤含水量方法和作物需水量方法三個方面闡述了基于衛星遙感的農業干旱監測研究進展。農業干旱預測是在干旱監測的基礎上進行時間軸的預測,本文在總結干旱監測進展的基礎上,進一步簡述了以干旱指數方法和作物生長模型方法為主的農業干旱預測研究進展。

關鍵詞: 衛星, 遙感, 農業干旱, 作物生長模型, 監測, 預測

Abstract:

Agricultural drought is a major factor that affects agricultural production. Traditional agricultural drought monitoring is mainly based on meteorological and hydrological data, and although it can provide more accurate drought monitoring results at the point level, there are still limitations in monitoring agricultural drought at the regional scale. The rapid development of remote sensing technology has provided a new mean of monitoring agricultural droughts at the regional scale, especially since the electromagnetic wavelengths sensed by satellite sensors in orbit now cover visible, near-infrared, thermal infrared and microwave wavelengths. It is important to make full use of the rich surface information obtained from satellite remote sensing data for agricultural drought monitoring and forecasting. This paper described the research progress of agricultural drought monitoring based on satellite remote sensing from three aspects: remote sensing index-based method, soil water content method and crop water demand method. The research progress of agricultural drought monitoring based on remote sensing index-based method was elaborated from five aspects: vegetation drought index, temperature drought index, integrated vegetation and temperature drought index, water drought index and microwave drought index; the research progress of agricultural drought monitoring based on soil water content method was elaborated from two aspects: soil water content retrieval based on visible to thermal infrared data and soil water content retrieval based on microwave data; the research progress of agricultural drought monitoring based on crop water demand method was elaborated from two aspects: agricultural drought monitoring based on crop canopy water content retrieval method and crop growth model method. Agricultural drought forecasting is a timeline prediction based on drought monitoring. Based on the summary of the progress of drought monitoring, the research progress of agricultural drought forecasting by the drought index method and the crop growth model method was further briefly described. The existing agricultural drought monitoring methods based on satellite remote sensing were summarized, and its shortcomings were sorted out, and some prospects were put forward. In the future, different remote sensing data sources can be used to combine deep learning methods with crop growth models and based on data assimilation methods to further explore the potential of satellite remote sensing data in the monitoring of agricultural drought dynamics, which can further promote the development of smart agriculture.

Key words: satellite, remote sensing, agricultural drought, crop growth model, monitor, forecast

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