A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
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, satellit...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ad3142 |
| _version_ | 1827287149133692928 |
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| author | Oz Kira Jiaming Wen Jimei Han Andrew J McDonald Christopher B Barrett Ariel Ortiz-Bobea Yanyan Liu Liangzhi You Nathaniel D Mueller Ying Sun |
| author_facet | Oz Kira Jiaming Wen Jimei Han Andrew J McDonald Christopher B Barrett Ariel Ortiz-Bobea Yanyan Liu Liangzhi You Nathaniel D Mueller Ying Sun |
| author_sort | Oz Kira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:52:31Z |
| format | Article |
| id | doaj.art-c9196ebcf90a467d9609ef482b4322b9 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-04-24T10:52:31Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-c9196ebcf90a467d9609ef482b4322b92024-04-12T08:28:43ZengIOP PublishingEnvironmental Research Letters1748-93262024-01-0119404407110.1088/1748-9326/ad3142A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)Oz Kira0https://orcid.org/0000-0002-1620-2323Jiaming Wen1Jimei Han2Andrew J McDonald3Christopher B Barrett4https://orcid.org/0000-0001-9139-2721Ariel Ortiz-Bobea5https://orcid.org/0000-0003-4482-6843Yanyan Liu6Liangzhi You7Nathaniel D Mueller8Ying Sun9https://orcid.org/0000-0002-9819-1241Department of Civil and Environmental Engineering, Ben-Gurion University of the Negev , Beer-Sheva 8410501, Israel; School of Sustainability and Climate Change, Ben-Gurion University of the Negev , Beer-Sheva 8410501, Israel; School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University , Ithaca, NY 14853-7801, United States of AmericaSchool of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University , Ithaca, NY 14853-7801, United States of AmericaSchool of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University , Ithaca, NY 14853-7801, United States of AmericaSchool of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University , Ithaca, NY 14853-7801, United States of America; School of Integrative Plant Sciences and Department of Global Development, Cornell University , Ithaca, NY 14853-7801, United States of AmericaCharles H. Dyson School of Applied Economics and Management and Jeb E. Brooks School of Public Policy, Cornell University , Ithaca, NY 14853-7801, United States of AmericaCharles H. Dyson School of Applied Economics and Management and Jeb E. Brooks School of Public Policy, Cornell University , Ithaca, NY 14853-7801, United States of AmericaDepartment of Transformation Strategies, International Food Policy Research Institute (IFPRI) , 1201 I Street, NW, Washington, DC 20005, United States of AmericaDepartment of Transformation Strategies, International Food Policy Research Institute (IFPRI) , 1201 I Street, NW, Washington, DC 20005, United States of AmericaDepartment of Ecosystem Science and Sustainability at Colorado State University , Fort Collins, CO 80523, United States of America; Department of Soil and Crop Sciences at Colorado State University , Fort Collins, CO 80523, United States of AmericaSchool of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University , Ithaca, NY 14853-7801, United States of AmericaProjected 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.https://doi.org/10.1088/1748-9326/ad3142solar-induced chlorophyll fluorescence (SIF)crop yieldmechanistic light reactionsagricultural monitoringsatellite remote sensingmachine learning |
| spellingShingle | Oz Kira Jiaming Wen Jimei Han Andrew J McDonald Christopher B Barrett Ariel Ortiz-Bobea Yanyan Liu Liangzhi You Nathaniel D Mueller Ying Sun A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) Environmental Research Letters solar-induced chlorophyll fluorescence (SIF) crop yield mechanistic light reactions agricultural monitoring satellite remote sensing machine learning |
| title | A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) |
| title_full | A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) |
| title_fullStr | A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) |
| title_full_unstemmed | A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) |
| title_short | A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) |
| title_sort | scalable crop yield estimation framework based on remote sensing of solar induced chlorophyll fluorescence sif |
| topic | solar-induced chlorophyll fluorescence (SIF) crop yield mechanistic light reactions agricultural monitoring satellite remote sensing machine learning |
| url | https://doi.org/10.1088/1748-9326/ad3142 |
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