Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

Bibliographic Data

ID22032103
AuthorsYan Guo (0000-0001-8666-6169, Henan University), Haoming Xia (0000-0003-0106-6709, Henan University, corresponding author), Li Pan (0009-0006-6596-1277, Henan University), Pan Li (0000-0002-0316-0355, Henan University), Xiaoyang Zhao (0000-0001-7882-9554, Henan University), Rumeng Li (0009-0003-3004-7221, Henan University), Xiqing Bian (Henan University), Ruimeng WANG (0000-0003-2995-8645, Henan University), Chong Yu (0000-0003-4171-2568, Henan University)
Year2021
Volume10
Issue9
Pages587
Publication date2021-09-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10090587
OpenAlexW3197688331
LanguageEN
Citations received2
References cited71

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas

Agricultural engineering · Agriculture · Agronomy · Climate change · Cropping · Enhanced vegetation index · Geography · Multiple cropping · Normalized Difference Vegetation Index · Phenology · Remote sensing · Sowing · Vegetation Index · Engineering · Environmental Science · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Urban Heat Island Mitigation · Geology

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