Automated soybean mapping based on canopy water content and chlorophyll content using Sentinel-2 images
نویسندگان
چکیده
Accurate and timely spatiotemporal distribution information of soybean is vital for sustainable agriculture development. However, it challenging to establish a phenology-based automated crops mapping algorithm at large spatial domains by simply applying vegetation index temporal profile. This study developed Phenology-based automatic Soybean through combined Canopy water Chlorophyll variations (PSCC). Three indices were designed: the ratio change magnitudes stress during late growth stage (T1), mean concentration chlorophyll whole period (T2), accumulated before after heading date (T3). was distinguished lower T1 T3 higher due senescence loss content. The PSCC method validated in Northeast China from 2017 2021 four states (Missouri, Illinois, Indiana, Ohio) United States (US) 2020 using Sentinel-2 datasets. planting areas obtained consistent with corresponding agricultural statistical area (R2 > 0.83). maps evaluated 5702 reference data, overall accuracy kappa coefficient 91.99% 0.8338, respectively. improved 16.07% compared only canopy variation. result showed that our could be applied multi-years without retraining. expanded substantially 25,867 km2 (by 89.10%) 2015–2020 decreased slightly 7,535 13.73%) 2021. expansion occurred mainly ever-planted regions. contributed about 60% national revitalization goal 2020. provided on changes China, which significant policymakers formulate production plans achieve revitalization.
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ژورنال
عنوان ژورنال: International journal of applied earth observation and geoinformation
سال: 2022
ISSN: ['1872-826X', '1569-8432']
DOI: https://doi.org/10.1016/j.jag.2022.102801