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• NDVI represents photosynthetic phenology only in deciduous trees. • Gcc tracks photosynthetic phenology more accurately than GRVI at the canopy scale. • ɸ NPQ and ɸ f,D have great influences on the spectral signal changes of NIRv and PRI. • Good consistency was found between NIRv and PRI for predicting A max. Vegetation photosynthetic phenology is an important indicator to characterize the biological responses of terrestrial carbon cycle to climate change. Remote sensing-derived spectral indices have been used to estimate photosynthetic phenology of terrestrial ecosystems, yet there are large uncertainties due to differences in leaf structure and canopy function between evergreen and deciduous trees. In this study, we used in-situ measured meteorology, the maximum rate of leaf-level CO 2 assimilation (A max), chlorophyll fluorescence and canopy color changes in evergreen and deciduous species to evaluate and compare the abilities of spectral indices for photosynthetic phenology modeling. Seasonal variation in A max is used as a proxy for photosynthetic phenology. Several indices were used, including the normalized difference vegetation index (NDVI), near-infrared reflectance vegetation index (NIRv), photochemical reflectance index (PRI), chlorophyll/carotenoid index (CCI), green-red vegetation index (GRVI), and green chromaticity coordinates (Gcc). We found that NIRv and PRI estimated phenological periods for Cedrus deodara and three deciduous trees all differed from A max by less than 5 days, and all were linearly related to A max (R2 > 0.6). CCI had less potential for A max modeling, while NDVI had the largest error in predicting A max. Gcc was a more accurate proxy for photosynthetic phenology than GRVI at the canopy scale. In addition, we found that the variability of different spectral indices in tracking photosynthetic phenology of evergreen and deciduous species was related to non-photochemical quenching processes. Especially for the fraction of absorbed light quenched by dynamic NPQ (ɸ NPQ) and the fraction of absorbed light quenched by sustained NPQ (ɸ f,D), they contribute more than 40 % to the variations of the NIRv and PRI spectral signals. Therefore, NIRv and PRI provide powerful tools for monitoring photosynthetic phenology, and future exploration of NIR v and PRI at larger spatial scales will favor the global plant phenology and carbon uptake modeling. [ABSTRACT FROM AUTHOR]

[Display omitted] • A selection strategy for base-period images of spatio-temporal fusion was proposed. • The spatio-temporal inconsistency between field samples and images was reduced. • The estimation accuracy of grassland aboveground biomass was improved. The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]