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

Abstract

Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies. 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]. These studies mainly focused on changing trends of winter wheat phenology and its responses to climatic factors from the perspective of regarding the wheat fields as important agricultural ecosystems to the global...

Climate change effects on bread wheat phenology and grain quality: A case study in the north of Italy

“…This case study aimed to verify whether CC has produced considerable effects on bread wheat phenological development. The topic is of particular interest, given the efforts made to analyze and predict phenology in bread wheat by remote sensing ( Wang et al, 2008 ; He et al, 2015 ; Wu et al, 2019 ). This approach, however, has been recently called for paying more attention by authors who compared its reliability with that of phenological studies performed by field observation, highlighting significant discrepancies ( Chen et al, 2020 ).…”

“…The topic is of particular interest, given the efforts made TABLE 2 Amount of crude protein (mg/g flour), protein distribution among fractions (%), and ratio between gliadins/glutenins and HMW-GS/LMW-GS in wheat flours derived from kernels harvested in different years. to analyze and predict phenology in bread wheat by remote sensing (Wang et al, 2008;He et al, 2015;Wu et al, 2019). This approach, however, has been recently called for paying more attention by authors who compared its reliability with that of phenological studies performed by field observation, highlighting significant discrepancies (Chen et al, 2020).…”

Identifying the Spatio-Temporal Change in Winter Wheat–Summer Maize Planting Structure in the North China Plain between 2001 and 2020

“…The winter wheat and summer maize rotation system is the predominant farming pattern in this area [8]. This dominant cultivation system requires 700-1000 mm yr −1 water, greatly exceeding the annual average precipitation of 500-600 mm [9], 70% to 80% of which falls during the summer maize growing season, from June to September [10,11]. Precipitation throughout the growing phase of winter wheat is significantly lower than the actual water demand [12], and approximately 70% of the water demand depends on groundwater irrigation [13].…”

Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery

“…Since the Moderate Resolution Imaging Spectrometer (MODIS) was launched, it provided time-series vegetation indices (VIs) with a high temporal (daily) and medium spatial resolution (250-500 m) [7]. Many previous studies have exploited the MODIS data to detect the phenological variation of different crops including maize [7], wheat [10,11] and rice [12][13][14], as well as other crops [15][16][17]. In addition, some studies retrieved plant phenology from fine resolution satellite imagery based on new optical sensors [18].…”