Dynamic variability of the heading–flowering stages of single rice in China based on field observations and NDVI estimations

Abstract

Although many studies have indicated the consistent impact of warming on the natural ecosystem (e.g., an early flowering and prolonged growing period), our knowledge of the impacts on agricultural systems is still poorly understood. In this study, spatiotemporal variability of the heading-flowering stages of single rice was detected and compared at three different scales using field-based methods (FBMs) and satellite-based methods (SBMs). The headingflowering stages from 2000 to 2009 with a spatial resolution of 1 km were extracted from the SPOT/VGT NDVI time series data using the Savizky-Golay filtering method in the areas in China dominated by single rice of Northeast China (NE), the middle-lower Yangtze River Valley (YZ), the Sichuan Basin (SC), and the Yunnan-Guizhou Plateau (YG). We found that approximately 52.6 and 76.3 % of the estimated heading-flowering stages by a SBM were within ±5 and ±10 days estimation error (a root mean square error (RMSE) of 8.76 days) when compared with those determined by a FBM. Both the FBM data and the SBM data had indicated a similar spatial pattern, with the earliest annual average heading-flowering stages in SC, followed by YG, NE, and YZ, which were inconsistent with the patterns reported in natural ecosystems. Moreover, diverse temporal trends were also detected in the four regions due to different climate conditions and agronomic factors such as cultivar shifts. Nevertheless, there were no significant differences (p>0.05) between the FBM and the SBM in both the regional average value of the phenological stages and the trends, implying the consistency and rationality of the SBM at three scales.

ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products

“…Previous studies have proposed different smoothing methods to reduce the noise of GLASS LAI time series and found that the OFP method varied by studied times, areas and objectives (Zhao et al, 2016;Wang et al, 2018). Three commonly used methods were chosen in the study to smooth the LAI time-series curves, including the double logistic (DL) method, Savitzky-Golay (S-G) filter method and waveletbased filter (WF) method.…”

Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

“…In this study, MVC was used to aggregate the original biweekly NDVI series into monthly series. In MATLAB, the moving averaged filter analysis was used to eliminate the abnormal values in time-series NDVI data [60,61]. To reflect the vegetation more appropriately, the NDVI products were averaged over an entire growing season from April to October to obtain the growing-season NDVI, which is usually used to represent the annual NDVI [62].…”

Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS)

“…Several constraints were applied on the smoothed NDVI time series to reduce outliers of phenometrics. A similar strategy was used in other studies on phenology monitoring for different crops [20,24]. The senescence dates calculated with a local threshold of 0.41 had a very close link to the DWD ripeness stage (RMSE within 1-2 weeks).…”