Monitoring crop phenology using a smartphone based near-surface remote sensing approach

Koen Hufkens, Eli K. Melaas, Michael L. Mann, Timothy Foster, Francisco Ceballos, Miguel Robles, Berber Kramer

Resultado de la investigación: Contribución a una revistaArtículo de revista revisión exhaustiva

27 Citas (Scopus)

Resumen

Smallholder farmers play a critical role in supporting food security in developing countries. Monitoring crop phenology and disturbances to crop growth is critical in strengthening farmers’ ability to manage production risks. This study assesses the feasibility of using crowdsourced near-surface remote sensing imagery to monitor winter wheat phenology and identify damage events in northwest India. In particular, we demonstrate how streams of pictures of individual smallholder fields, taken using inexpensive smartphones, can be used to quantify important phenological stages in agricultural crops, specifically the wheat heading phase and how it can be used to detect lodging events, a major cause of crop damage globally. Near-surface remote sensing offers granular visual field data, providing detailed information on the timing of key developmental phases of winter wheat and crop growth disturbances that are not registered by common satellite remote sensing vegetation indices or national crop cut surveys. This illustrates the potential of near-surface remote sensing as a scalable platform for collecting high-resolution plot-specific data that can be used in supporting crop modeling, extension and insurance schemes to increase resilience to production risk and enhance food security in smallholder agricultural systems.

Idioma originalInglés
Páginas (desde-hasta)327-337
Número de páginas11
PublicaciónAgricultural and Forest Meteorology
Volumen265
DOI
EstadoPublicada - 15 feb. 2019
Publicado de forma externa

Nota bibliográfica

Funding Information:
This work was undertaken as part of the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI). Funding support for this study was provided by the CGIAR Research Programs on Climate Change and Agricultural Food Security (CCAFS) and Policies, Institutions, and Markets (PIM); the CGIAR Platform for Big Data in Agriculture; 3ie Award No. TW13-1038; and NERC-ESRC-DFID Award No. NE/R014094/1. KH acknowledges support from the National Science Foundation’s Macro-system Biology Program (award EF-1065029 ) and the Belgian Science Policy Office (contract BR/175/A3/COBECORE ). EKM acknowledges support from NASA grant number NNH14ZDA001N-LCUC . This paper has not gone through IFPRI’s standard peer-review procedure. The opinions expressed here belong to the authors, and do not necessarily reflect those of CCAFS, PIM, 3ie, NERC, IFPRI, or CGIAR.

Publisher Copyright:
© 2018 The Authors

Palabras clave

  • Crowdsourcing
  • India
  • Insurance
  • Remote sensing
  • Winter wheat

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