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In the context of global warming, snow cover changes have an extremely important impact on the hydrological cycle and the redistribution of water resources in arid and semi-arid regions. In this paper, based on the daily cloud-free snow area remote sensing product data in the source region of Yangtze River (SRYR) from 2000 to 2019, the snow phenology variables such as the snow cover day (SCD), snow onset date (SOD), snow end date (SED), and snow duration day (SDD) were extracted separately for each hydrological year, and the vertical distribution of snow cover area (SCA) in the SRYR was analyzed by combining with the digital elevation model (DEM). In addition, we also combined climate factors and land cover types to further explore the spatiotemporal variation characteristics of snow phenology in response to different influencing factors, in order to reveal the spatiotemporal variation patterns of snow cover in the SRYR. The results showed that: (1) The SCA in the SRYR has a more obvious vertical distribution, with the maximum SCA reaching 61.58% at high elevation, while at low elevation, the SCA is mostly below 20%. The distribution of SCD in the study area showed a significant exponential correlation with DEM (R2 = 0.87). (2) The area of SOD in the SRYR showed an advanced trend of about 63.37%, while the area of SED showed a delayed trend of about 69.59%, and the area which showed significant trends is 4.29% and 4.36%, respectively. Therefore, the trends of both SOD and SED showed insignificant changes. (3) Temperature change is the main factor affecting the change of snow cover in the SRYR. Among them, 90.9% of the regions showed a significant positive correlation between temperature and SCD, while precipitation showed a significant negative correlation with SCD in about 75.3% of the total area of SRYR. Under the stable snow area (SCD > 60), the land cover type is glacial or permanent snow about 1.5 × 103 km2, which covers almost the entire glacial or permanent snow of the SRYR. [ABSTRACT FROM AUTHOR]

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale. [ABSTRACT FROM AUTHOR]