Monitoring crop phenology is essential for evaluating crop productivity and crop management. Remote sensing provides an efficient way to monitor crop phenological metrics at a large-scale. However, the widely used AVHRR and MODIS images are less reliable at a small-scale and in areas with heterogeneous land covers, such as the patchy cropland in South Central China. Therefore, this study analyzed the spatial and temporal variations of winter wheat phenology in South Central China, using enhanced vegetation index (EVI) time series predicted by a spatio-temporal fusion method that combines information from Landsat and MODIS images. The 13-year predicted EVI showed a close correspondence with the EVI derived from original Landsat images. Start of season (SOS), peak greenness, and end of season (EOS) were derived from the predicted EVI time series. The comparison with ground observations showed that the differences between the predicted phenological metrics and observations were usually within seven days. The length of the growing season demonstrated high spatial heterogeneity over the study area and the spatial patterns varied from year to year. The phenological dates did not show obvious increasing or decreasing trends through 13 years. The length of the growing season in the study area was positively correlated with precipitation, but the duration from SOS to peak greenness and the duration from peak greenness to EOS were strongly and negatively correlated with hours of sunshine.
This study aims to find the optimal vegetation indices (VIs) to remotely estimate plant nitrogen concentration (PNC) in winter oilseed rape across different growth stages. Since remote sensing cannot “sense” N in live leaves, remote estimation of PNC should be based on understanding the relationships between PNC and chlorophyll (Chl), carotenoid concentration (Car), Car/Chl, dry mass (DM), and leaf area index (LAI). The experiments with eight nitrogen fertilization treatments were conducted in 2014 to 2015 and 2015 to 2016, and measurements were acquired at six-leaf, eight-leaf, and ten-leaf stages. We found that at each stage, Chl, Car, DM, and LAI were all strongly related to PNC. However, across different growth stages, semipartial correlation and linear regression analysis showed that Chl and Car had consistently significant relationships with PNC, whereas LAI and DM were either weakly or barely correlated with PNC. Therefore, the most suitable VIs should be sensitive to the change in Chl and Car while insensitive to the change in DM. We found that anthocyanin reflectance index and the simple ratio of the red band to blue band fit the requirements. The validation with the 2015 to 2016 dataset showed that the selected VIs could provide accurate estimates of PNC in winter oilseed rape.
This study analyzed grassland gross primary production (GPP) estimated by the Temperature and Greenness (TG) model and the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm along the mean precipitation gradient and as a function of interannual variability in site-level precipitation. The calibrated TG model and MODIS algorithm appeared to provide accurate GPP estimations at three study sites with varying precipitation. However, the evaluation for each site/year demonstrated the variations of the accuracy of GPP estimates among different sites and years. GPP were overestimated at the driest site among three study sites, and during the dry years of the semiarid site. Both models provided more accurate GPP estimates for the wet site and during the wet and normal years of the semiarid sites. Calibrating both models for each site/year showed that the parameters of both models varied among sites and years, especially for the TG model. The relationship between flux-tower GPP observations and (scaled EVI *scaled LST) for the TG model and the relationship between GPP observations and (fPAR*PAR*Tmin scalar*VPD scalar) for the MODIS algorithm were different during green-up and dry-down period of grassland during the dry years at semiarid sites. This result implied that different relationships at different growing stages might be one of the major reasons for the overestimation of GPP by the TG model and the MODIS algorithm for semiarid grassland where water is a limiting resource. Thus, both TG model and MODIS algorithm should be used with caution in the arid and semiarid grassland regions
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