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

被引:71
|
作者
Hufkens, Koen [1 ,2 ]
Melaas, Eli K. [3 ]
Mann, Michael L. [4 ]
Foster, Timothy [5 ]
Ceballos, Francisco [6 ]
Robles, Miguel [6 ]
Kramer, Berber [6 ]
机构
[1] Univ Ghent, Dept Appl Ecol & Environm Biol, Ghent, Belgium
[2] INRA, UMR ISPA, Villenave Dornon, France
[3] Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA
[4] George Washington Univ, Dept Geog, Washington, DC USA
[5] Univ Manchester, Sch Mech Aerosp & Civil Engn, Manchester, Lancs, England
[6] Int Food Policy Res Inst, Markets Trade & Inst Div, Washington, DC 20036 USA
基金
美国国家科学基金会;
关键词
Crowdsourcing; Remote sensing; Winter wheat; Insurance; India; DIGITAL REPEAT PHOTOGRAPHY; CLIMATE-CHANGE; SPRING PHENOLOGY; YIELD; SMALLHOLDER; WHEAT; ADAPTATION; PRODUCTIVITY; PERFORMANCE; MONSOON;
D O I
10.1016/j.agrformet.2018.11.002
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页码:327 / 337
页数:11
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