油料作物产量遥感监测研究进展与挑战

doi:10.12133/j.smartag.SA202303002

Smart Agriculture ›› 2023, Vol. 5 ›› Issue (3): 1-16.doi: 10.12133/j.smartag.SA202303002

• 专刊--作物信息监测技术 • 下一篇

油料作物产量遥感监测研究进展与挑战

马宇靖¹(), 吴尚蓉²(), 杨鹏², 曹红², 谭杰扬³, 赵荣坤²

^1. 中北大学信息与通信工程学院，山西太原 030051，中国
^2. 北方干旱半干旱耕地高效利用全国重点实验室（中国农业科学院农业资源与农业区划研究所），北京 100081，中国
^3. 湖南省农业科学院农业经济和农业区划研究所，湖南长沙 410125，中国

收稿日期:2023-03-03 出版日期:2023-09-30
基金资助:
湖南省自然科学基金项目(2021JJ40286); 国家自然科学基金项目(42271374); 中央级公益性科研院所基本科研业务费专项(1610132021009); 中国农业科学院青年创新专项(Y2023QC18)
作者简介:
马宇靖，研究方向为农业信息分析与应用。E-mail：s202105028@st.nuc.edu.cn
通信作者:
吴尚蓉，博士，副研究员，研究方向为农业遥感基础与应用。E-mail：wushangrong@caas.cn

Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing

MA Yujing¹(), WU Shangrong²(), YANG Peng², CAO Hong², TAN Jieyang³, ZHAO Rongkun²

^1. School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
^2. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China (the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China )
^3. Institute of Agricultural Economy and Agricultural Regionalization, Hunan Academy of Agricultural Sciences, Changsha 410125, China

Received:2023-03-03 Online:2023-09-30
Supported by:
National Natural Science Foundation of Hunan Province(2021JJ40286); National Natural Science Foundation of China(42271374); The Fundamental Research Funds for Central Nonprofit Scientific Institutions(1610132021009); The Youth innovation Program of Chinese Academy of Agricultural Sciences(Y2023QC18)

摘要/Abstract

摘要：

[目的/意义] 油料作物是粮食供应和非粮食供应的重要组成部分，也是食用植物油和植物蛋白的重要来源。实时、动态、大范围的油料作物生长监测对指导农业生产、维持粮油市场稳定、确保国民生命健康具有重大意义。遥感技术因其覆盖范围广、获取信息及时、快速等优势被广泛应用于区域作物产量监测研究和应用中。 [进展] 本文首先介绍了利用遥感技术对油料作物进行估产的相关背景；其次，从遥感参数反演、面积监测及估产研究三个方面综述了基于遥感技术的油料作物监测研究现状，指出数据同化技术在油料作物估产方面具有极大潜力，并从同化方法、网格选取两方面进行详细阐述。 [结论/展望] 指出了遥感技术在油料作物监测中的机遇，提出了基于遥感技术的油料作物估产在作物特征选取、空间尺度确定以及遥感数据选择等方面存在的一些问题和挑战，并对未来油料作物估产研究的发展趋势进行了展望。本文可为油料作物的区域估产及生长监测的深入研究提供借鉴和参考。

关键词: 遥感, 产量模拟, 数据同化, 油料作物, 产量监测, 参数反演

Abstract:

[Significance] Oil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology. [Progress] This paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future. [Conclusions and Prospects] The paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management.

Key words: remote sensing, yield simulation, data assimilation, oil crops, yield monitoring, parameter inversion

马宇靖, 吴尚蓉, 杨鹏, 曹红, 谭杰扬, 赵荣坤. 油料作物产量遥感监测研究进展与挑战[J]. 智慧农业(中英文), 2023, 5(3): 1-16.

MA Yujing, WU Shangrong, YANG Peng, CAO Hong, TAN Jieyang, ZHAO Rongkun. Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing[J]. Smart Agriculture, 2023, 5(3): 1-16.

图/表 2

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作物	估产物候期
大豆	种子初期、豆荚期、灌浆期^{［47，48］}
油菜	开花期、角果期^{［49，50］}
花生	种子初生期^［51］
向日葵	开花期前的花序出现阶段^［52］

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油料作物产量遥感监测研究进展与挑战

Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing

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