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The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP. [ABSTRACT FROM AUTHOR]

Improving understanding of changes in intra-annual variability (IAV) of evapotranspiration (ET) and the underlying drivers is an essential step for modeling hydrological processes in response to global change. Previous studies paid special attention to climatic regulations of IAV of ET. However, ignoring the role of landscape characteristics (e.g., vegetation coverage) can introduce great uncertainty in the explanation of ET variance. In this work, the Poyang Lake Basin, which is a typical humid basin in China, was taken as the study area. It has experienced an obvious climate change and revegetation since the 1980s. Here, trends of IAV of ET and their responses to four climatic variables (i.e., air temperature, precipitation, downward shortwave radiation and wind speed) and vegetation coverage were explored from 1983 to 2014. The results show that IAV of ET exhibited contrary trends during the past decades. It significantly (p < 0.05) declined with a significant linear slope of −0.52 mm/year before 2000, and then slightly increased (slope = 0.06 mm/year, p > 0.05) over the basin, which was generally consistent with the IAV of temperature and radiation. The proposed variables could well capture the change in IAV of ET, while their dominators were different during the two contrasting phases mentioned above. The IAV of radiation and temperature dominated the change of the IAV of ET over 77.82% and 35.14% of the basin, respectively, before and after the turning point. Meanwhile, the rapid increase in vegetation coverage, which was associated with afforestation, significantly (p < 0.05) reduced IAV of ET over about 35% of the study area. The achievements of this study should be beneficial for a sophisticated awareness of responses of intra-annual variability of ET to climate and land cover changes at the basin scale. [ABSTRACT FROM AUTHOR]