TY  - JOUR
AU  - Luo, Yuchuan
AU  - Zhang, Zhao
AU  - Chen, Yi
AU  - Li, Ziyue
AU  - Tao, Fulu
PY  - 2020
DA  - 2020//
TI  - ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products
JO  - Earth System Science Data
SP  - 197
EP  - 214
VL  - 12
IS  - 1
AB  - Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).
UR  - https://essd.copernicus.org/articles/12/197/2020/essd-12-197-2020.pdf
UR  - https://app.dimensions.ai/details/publication/pub.1124480581
UR  - https://doi.org/10.5194/essd-12-197-2020
DO  - 10.5194/essd-12-197-2020
ID  - pub.1124480581
ER  -