气候变化领域集成服务门户(CCPortal): A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

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DOI	10.1088/1748-9326/ad3142
	A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
	Kira, Oz; Wen, Jiaming; Han, Jimei; McDonald, Andrew J.; Barrett, Christopher B.; Ortiz-Bobea, Ariel; Liu, Yanyan; You, Liangzhi; Mueller, Nathaniel D.; Sun, Ying
发表日期	2024
ISSN	1748-9326
起始页码	19
结束页码	4
卷号	19 期号:4
英文摘要	Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India's Indo-Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.
英文关键词	solar-induced chlorophyll fluorescence (SIF); crop yield; mechanistic light reactions; agricultural monitoring; satellite remote sensing; machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001201274600001
来源期刊	ENVIRONMENTAL RESEARCH LETTERS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304948
作者单位	Ben Gurion University; Ben Gurion University; Cornell University; Cornell University; Cornell University; Cornell University; Cornell University; CGIAR; International Food Policy Research Institute (IFPRI); Colorado State University; Colorado State University
推荐引用方式 GB/T 7714	Kira, Oz,Wen, Jiaming,Han, Jimei,et al. A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)[J],2024,19(4).
APA	Kira, Oz.,Wen, Jiaming.,Han, Jimei.,McDonald, Andrew J..,Barrett, Christopher B..,...&Sun, Ying.(2024).A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF).ENVIRONMENTAL RESEARCH LETTERS,19(4).
MLA	Kira, Oz,et al."A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)".ENVIRONMENTAL RESEARCH LETTERS 19.4(2024).