Reconciling the discrepancy in ground‐ and satellite‐observed trends in the spring phenology of winter wheat in China from 1993 to 2008

Abstract

Monitoring crop phenology has become a growing concern for food security. Crop phenology can be traditionally observed at plot scale in the field or recently at a much larger scale by satellites. In this study, we compared the spring phenology of winter wheat (Triticum sp.), quantified as the timing of start-of-spring-season (SOS), using 8 km resolution satellite data and ground observations at 112 agrometeorological stations across China from 1993 to 2008. We found that ground and satellite observations displayed opposing trends in winter wheat SOS. Ground observation exhibited a delayed onset of SOS at 86% of ground stations, whereas satellite data suggested an earlier arrival of SOS at 78% of stations. The meteorological SOS calculated from daily air temperature supported the earlier occurrence of SOS indicated by satellite data. Moreover, satellite data showed more agreement with meteorological data with respect to interannual SOS variations than did field phenology records. Given the dominant control of air temperature on winter wheat's spring phenology, satellite observation provides a reliable measure of the long-term trends and dynamics of SOS. Ground-observed SOS trends were impaired by data heterogeneity and limited spatial coverage. However, compared with ground observations, satellite-derived phenological timings are often lack of biological meanings. Therefore, integrating ground and satellite observations could enhance the monitoring of winter wheat SOS, which would increase the knowledge of vegetation's response to the changing climate and help to optimize timely crop management.

The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method

“…In their study, they used EVI time series data from 1981 to 2014 and studied their responses to climate change at a global scale. Guo et al [38] used NDVI time series from 1993 to 2008 to retrieve the SOS of winter wheat in China with the 20% threshold. The study also compared spatio-temporal trends based on ground observations and remote sensing data.…”

Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

“…Among the tools used to acquire information, unmanned aerial vehicles (UAVs) equipped with visible and near-infrared cameras, provide, in a fast and easy way, field data for precision agriculture applications [2,3]. The resolution of information from satellite data typically ranges from 5 to 30 m pixels and is unsuitable in agronomy trials given the limitations of real-time monitoring and accuracy [4]. In contrast to satellite imagery and aircraft-based remote sensing, UAVs can be used frequently during the entire growth period [5].…”

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

“…Since the previous studies showed remarkable discrepancies between the satellite estimation and field measurements of winter wheat phenology in the NCP [1,2], we applied an empirical relative threshold method (RTM) to improve the MODIS-based phenology of winter wheat in the NCP, which has been widely utilized owing to its simplicity and effectiveness [37]. Compared with other methods (i.e., maximum change rate [2,15] or the fixed threshold [1]), RTM is more flexible because various dates can be obtained by setting the different thresholds, making it possible to improve the agreement between the satellite estimation and field measurements through RTM.…”

“…The phenological events of winter wheat have been traditionally obtained through field observations in the NCP. That is, the metrics of GUD, HD, and MD have been continuously recorded at the meteorological stations located in the NCP from 2000 to now [15][16][17]. These long-term ground observations have been successfully applied to several studies analyzing the relationships between winter wheat phenology and climatic factors [16][17][18].…”

“…on NDVI, a novel snow-free type of VIs, including the empirical normalized difference phenology index (NDPI,[27]) and the semi-analytical normalized difference greenness index (NDGI,[26]), were recently proposed. Based on the time-series of the computed VIs, vegetation phenology can be estimated by extracting the day of year (DOY) corresponding to pre-determined absolute or relative thresholds of the VI values [1,15] or the maximum change rate of the trajectories of VIs [11,28]. For winter wheat, the GUDs were previously estimated based on either a relative threshold of 20% or a maximum change rate of the fitted curve of the time series VIs [1,2,10,11].…”