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Research progress on radar remote sensing for rice growth monitoring

  • role： First author第一作者
  • Affiliation：

    University of Electronic Science and Technology of China, School of Resources and Environment, Chengdu 611731, China

  • Email：201911070402@std.uestc.edu.cn
  • Introduction：何泽，研究方向为水稻微波遥感。E-mail： 201911070402@std.uestc.edu.cn
HE Ze1,  
  • role： Corresponding author通信作者
  • Affiliation：

    University of Electronic Science and Technology of China, School of Resources and Environment, Chengdu 611731, China

    University of Electronic Science and Technology of China, Yangtze Delta Region Institute (Huzhou), Huzhou 313001, China

  • Email：lishihua@uestc.edu.cn
  • Introduction：李世华， 研究方向为主动遥感技术。E-mail： lishihua@uestc.edu.cn
LI Shihua12*

resumen

Rice is one of the most productive food crops of Asian countries. Timely and accurate access to rice cultivation information can provide professional support for farming management and agricultural policy-making. Space-borne Synthetic Aperture Radar (SAR) imaging is free from meteorological interference and can sensitively respond to rice plant development and soil moisture changes. Therefore, satellites equipped with various SAR sensors are important data sources for rice growth monitoring in cloudy and foggy areas. The research progress of microwave remote sensing in rice growth monitoring has been made on all fronts, but the technical evolution and relationship of different research topics are complex and relatively confusing.The development history, current focus, and innovation prospect of rice radar remote sensing should be reviewed and analyzed, considering the key scientific problems and main experimental approaches. Based on the collation and statistics of relevant literature in the recent 30 years, the key problems of rice radar remote sensing are summarized into three study focuses: planting area identification, biophysical parameter retrieval, and phenology and cropping intensity recognition. Then, the technical methods are summarized into three research strategies: mathematical and physical analysis, machine learning, and multisource data synergism.Specifically, rice planting area identification methods are divided into four schemes: time domain change analysis, machine learning, object-oriented classification, and multisource data synergism. Rice biophysical parameter retrieval methods are divided into five models: empirical model, physical model, semi-empirical model, data assimilation, and multi-source data synergism. Rice phenology and cropping intensity recognition methods are divided into two algorithms: time-series feature detection and multi-temporal machine learning. From the perspective of data attributes and model structure, the theoretical basis and applicable conditions of different methods are introduced, and their advantages and limitations are explained. Finally, in view of the rapid advancement of SAR imaging capability and computer science, the future research issues are discussed.Therefore, the three difficult points to be solved in rice radar remote sensing monitoring are as follows: (1) fragmented farmlands and fluctuant terrain; (2) diverse cultivation conditions, and (3) asynchronous phenology and complex interplant. The future study should focus on the following: (1) high temporal-spatial resolution of rice planting area identification relying on less prior information; (2) dynamic retrieval of rice biophysical parameters balancing model efficiency and accuracy; (3) automatic recognition of rice phenology and cropping intensity combining plant growth mechanism and time series observation. The improvement of these research topics profoundly promotes the practical application of rice radar remote sensing.

palabra clave

SAR;rice;Planting area;biophysical parameters;phenology;cropping intensity

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