Abstract
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).
Highlights
Phenology is a key indicator of vegetation growth and development and plays an important role in vegetation monitoring (Qiu et al, 2015; Tao et al, 2017; Zhong et al, 2016)
In this study, using a remotely sensed Global Land Surface Satellite (GLASS) leaf area index (LAI) product (2000–2015; Xiao et al, 2014), we aim to (1) choose the most suitable smoothing method to reduce the noise of the LAI time series for different crops and regions, (2) detect the phenological information of three staple crops at 1 km spatial resolution across China and evaluate its accuracy by comparing with the observed data at agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), and (3) explore the spatial patterns of different phenological stages
Previous studies have proposed different smoothing methods to reduce the noise of GLASS LAI time series and found that the optimal filter-based phenology detection (OFP) method varied by studied times, areas and objectives (Zhao et al, 2016; Wang et al, 2018)
Summary
Phenology is a key indicator of vegetation growth and development and plays an important role in vegetation monitoring (Qiu et al, 2015; Tao et al, 2017; Zhong et al, 2016). Accurate information on the timing of key crop phenological stages is critical for determining the optimal timing of agronomic management options, reliable simulations of crop growth and yield, and analyzing the plant response to climate change (Bolton and Friedl, 2013; Brown et al, 2012; Chen et al, 2018a; Sakamoto et al, 2010, 2013; Wang et al, 2015; Zhang and Tao, 2013). The field phenological observations cannot meet the requirements of many purposes such as vegetation monitoring for remote areas with sparse observations and the grid-based earth system simulations. The satellite-based observations with a wide spatial coverage and short revisit times have become a powerful method
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