Abstract
Crop phenology information provides essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets agree with in-situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding their spatial patterns and temporal dynamics. We observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the change of temperature and precipitation, which further alter the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).
Highlights
Accurate and timely crop phenology information, which contains multi-phase growth information from sowing to harvest, is 5 highly required by precision agriculture management (Gao and Zhang, 2021; Zeng et al, 2020), such as irrigation schedules and pest control
The harmonic model can well delineate the seasonal dynamics of enhanced vegetation index (EVI) for spring and summer maize
Due to the lack of accurate locations of observed crops in agricultural meteorological stations (AMS), we measured the uncertainties of phenological indicators of maize within the range of 5km to the station
Summary
Accurate and timely crop phenology information, which contains multi-phase growth information from sowing to harvest, is 5 highly required by precision agriculture management (Gao and Zhang, 2021; Zeng et al, 2020), such as irrigation schedules and pest control. The traditional in-situ based crop phenology recording is timeconsuming and focused on limited sites (Gao and Zhang, 2021) These limitations have been considerably mitigated by satellite images, which provide revisit observations of crop growth at regional and global scales (Shanmugapriya et al, 2019; Zhang et al, 2003; Cao et al, 2015). Different phenological indicators (such as the start of season and the end of season) are retrieved for crop growth monitoring using satellite observations, including the moderate resolution imaging spectroradiometer (MODIS) 20 data (Sakamoto et al, 2010), the advanced very high resolution radiometer (AVHRR) data (Zhang et al, 2014; Gim et al, 2020). Fine resolution Landsat satellite data show great potential in providing crop phenological indicators with a fine resolution and a long-term span Despite that those coarse satellite data (such as MODIS and AVHRR) have a fine temporal resolution, which
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