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Surveillance continue du NDVI du riz à l’échelle parcellaire par combinaison synergique d’images satellitaires et de drones

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：zihanren2001@163.com
  • Introduction：任紫晗，研究方向为农业遥感。E-mail：zihanren2001@163.com
REN Zihan1,  
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

XU Jiaqi1,  
  • Affiliation：

    Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore 119076, Singapore

WEI Shanshan2,  
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

WU Wenbin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：liwenjuan01@caas.cn
  • Introduction：李文娟， 研究方向为基于空天地多源遥感技术的农田智能监测技术及应用。E-mail：liwenjuan01@caas.cn
LI Wenjuan1*

résumé

La surveillance en temps réel de la croissance des cultures à l'échelle parcellaire est essentielle pour l'agriculture de précision. Cependant, la télédétection satellitaire couvre de larges zones mais est limitée par les conditions météorologiques et la résolution spatiale, tandis que les drones (UAV) offrent une résolution spatiale élevée mais sont contraints par l'autonomie. Une seule source de données ne peut satisfaire les besoins de surveillance continue à grande échelle. Pour répondre à ce problème, cette étude utilise des données multispectrales de UAV, Sentinel-2 et PlanetScope SuperDove, et propose une méthode de fusion spatio-temporelle inter-plateformes basée sur un algorithme CACAO amélioré. Cette méthode construit deux stratégies de combinaison de données, « UAV+Sentinel-2 » et « SuperDove+Sentinel-2+UAV », et génère quasi en temps réel des séries temporelles quotidiennes de l'indice de végétation normalisé (NDVI) à une résolution de 1 mètre via deux modes, prévision avant et mise à jour arrière. Une validation croisée Leave-One-Out (LOOCV) a été utilisée, comparée à l'algorithme existant GLM-STF (fusion spatio-temporelle basée sur le modèle linéaire généralisé), afin d'évaluer la précision de la fusion. Les résultats montrent : (1) les données NDVI des différentes plateformes présentent une bonne cohérence, avec une corrélation Sentinel-2 et SuperDove de 0,97, et une corrélation UAV-satellite supérieure à 0,75, répondant ainsi aux conditions de fusion ; (2) l'algorithme CACAO peut efficacement reconstruire la dynamique phénologique du riz, avec des séries temporelles NDVI plus lisses en mode mise à jour arrière, et les deux stratégies de combinaison basées sur CACAO atteignent une haute précision (R > 0,94). L'introduction des données SuperDove à haute résolution temporelle pendant les périodes phénologiques clés améliore la précision de 0,51 à 0,67 ; (3) l'algorithme CACAO montre une performance plus stable et légèrement supérieure à GLM-STF pendant toute la saison de croissance. En somme, le cadre de fusion inter-plateformes proposé permet de générer efficacement des séries temporelles NDVI continues et précises à l'échelle parcellaire, fournissant un support technique solide pour la surveillance précise de la croissance des cultures et la gestion agricole de précision.

mots-clés

PlanetScope;Sentinel-2;fusion de données spatio-temporelle;surveillance de la croissance;agriculture de précision;échelle parcellaire;courbes phénologiques;surveillance quasi temps réel

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