Challenges and opportunities in remote sensing-based crop monitoring: a review.

Ground data Remote Sensing crop condition crop monitoring crop production

Journal

National science review
ISSN: 2053-714X
Titre abrégé: Natl Sci Rev
Pays: China
ID NLM: 101633095

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 02 08 2022
revised: 12 12 2022
accepted: 15 12 2022
entrez: 24 3 2023
pubmed: 25 3 2023
medline: 25 3 2023
Statut: epublish

Résumé

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

Identifiants

pubmed: 36960224
doi: 10.1093/nsr/nwac290
pii: nwac290
pmc: PMC10029851
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

nwac290

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.

Déclaration de conflit d'intérêts

None declared.

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Auteurs

Bingfang Wu (B)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Executive Committee of Group on Earth Observations Global Agricultural Monitoring (GEOGLAM), Geneva 2300, Switzerland.

Miao Zhang (M)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Executive Committee of Group on Earth Observations Global Agricultural Monitoring (GEOGLAM), Geneva 2300, Switzerland.

Hongwei Zeng (H)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Executive Committee of Group on Earth Observations Global Agricultural Monitoring (GEOGLAM), Geneva 2300, Switzerland.

Fuyou Tian (F)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Andries B Potgieter (AB)

Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane 4343, Australia.

Xingli Qin (X)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Nana Yan (N)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Sheng Chang (S)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Yan Zhao (Y)

Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane 4343, Australia.

Qinghan Dong (Q)

Department of Remote Sensing, Flemish Institute of Technological Research, Mol 2400, Belgium.

Vijendra Boken (V)

Department of Geography and Earth Science, University of Nebraska-Kearney, NE 68849, USA.

Dmitry Plotnikov (D)

Department of Satellite Monitoring Technologies, Space Research Institute of Russian Academy of Sciences, Moscow 117997, Russia.

Huadong Guo (H)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

Fangming Wu (F)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Hang Zhao (H)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

Bart Deronde (B)

Department of Remote Sensing, Flemish Institute of Technological Research, Mol 2400, Belgium.

Laurent Tits (L)

Department of Remote Sensing, Flemish Institute of Technological Research, Mol 2400, Belgium.

Evgeny Loupian (E)

Department of Satellite Monitoring Technologies, Space Research Institute of Russian Academy of Sciences, Moscow 117997, Russia.

Classifications MeSH