Abstract. Data and knowledge of surface water bodies (SWB), including large lakes and reservoirs (surface water areas > 1 km2), are critical for the management and sustainability of water resources. However, the existing global or national dam datasets have large georeferenced coordinate offsets for many reservoirs, and some datasets have not reported reservoirs and lakes separately. In this study, we generated China's surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19 338 images) in Google Earth Engine and very-high spatial resolution imagery in Google Earth Pro. There were ∼ 3.52 × 106 yearlong SWB polygons in China for 2019, only 0.01 × 106 of them (0.43 %) were of large size (> 1 km2). The areas of these large SWB polygons accounted for 83.54 % of the total 214.92 × 103 km2 yearlong surface water area (SWA) in China. We identified 2418 large dams, including 624 off-stream dams and 1794 on-stream dams, 2194 large reservoirs (16.35 × 103 km2), and 3051 large lakes (73.38 × 103 km2). In general, most of the dams and reservoirs in China were distributed in South China, East China, and Northeast China, whereas most of lakes were located in West China, the lower Yangtze River basin, and Northeast China. The provision of the reliable, accurate China-LDRL dataset on large reservoirs/dams and lakes will enhance our understanding of water resources management and water security in China. The China-LDRL dataset is publicly available at https://doi.org/10.6084/m9.figshare.16964656.v3 (Wang et al., 2021b).

Space-based vegetation indices have been used to model global photosynthesis for more than two decades. However, vegetation indices are fundamentally linked to leaf optical properties rather than leaf physiology, which limits their utility in regions where changes in photosynthesis are driven by leaf development and demography. Contrary to vegetation indices, solar-induced chlorophyll fluorescence contains information on leaf physiology and has been shown to be synchronous with photosynthetic activity in the tropics. Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation (PAR) absorbed by chlorophyll (APAR chl ). ChloFluo is unique in that instead of estimating APAR chl as a function of a vegetation index and an ancillary PAR product, we model APAR chl using its empirical relationship with SIF and the proportion of APAR chl that is reemitted as SIF, or ΦF. This empirical, fluorescence-based approach to estimating the amount of sunlight absorbed by chlorophyll accounts for non-linearities between SIF and photosynthesis emerging from seasonality diverging efficiencies in light use. We compare and validate our model using FluxCom, FluxSat, and eddy covariance tower data and find that ChloFluo best matches the seasonality of tower photosynthesis in the Amazon. Thus, ChloFluo has potential for advancing our ability to accurately model photosynthesis in tropical evergreen broadleaf forests, which is responsible for one-third of terrestrial photosynthesis. Potential uses of our model are to investigate and advance our understanding of the timing and magnitude of the uptake of atmospheric carbon dioxide by vegetation, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.

Abstract. Many forest cover maps have been generated by using optical and/or microwave images and various forest definitions, but these forest cover maps have large discrepancies. Both forest definition and validation data used for accuracy assessment of forest cover maps are often considered as two of the major factors for the discrepancy among these forest cover maps. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: (1) tree canopy height and (2) canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and Moderate Resolution Imaging Spectroradiometer, and the forest definition of the Food and Agriculture Organization (FAO) of the United Nations (>5-m tree height and >10 % canopy coverage). The canopy height and coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 were used to assess annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest and evergreen forest cover maps in the Brazilian Amazon.

Abstract. Data and knowledge of surface water bodies (SWB), including large lakes and reservoirs (surface water areas > 1 km2) are critical for the management and sustainability of water resources. However, the existing global or national dam datasets have large georeferenced coordinate offsets for many reservoirs, and some datasets have not reported reservoirs and lakes separately. In this study, we generated China’s surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19,338 images) in Google Earth Engine and very-high spatial resolution imagery in Google Earth. There were ~3.52 × 106 yearlong SWB polygons in China for 2019, only 0.01 × 106 of them (0.43 %) were of large size (> 1 km2). The areas of these large SWB polygons accounted for 83.54 % of the total 214.92 × 103 km2 yearlong surface water area (SWA) in China. We identified 2,140 large dams, including 1,494 reservoir dams and 646 river dams, 1,976 large reservoirs (16.42 × 103 km2), and 3,508 large lakes (75.97 × 103 km2). In general, most of the dams and reservoirs in China were distributed in South China, East China, and Northeast China, whereas most of lakes were located in West China, the Lower Yangtze River Basin, and Northeast China. The provision of the reliable, accurate China-LDRL dataset on dams, large reservoirs and lakes will enhance our understanding of water resources management and water security in China. The China-LDRL dataset is publicly available at https://doi.org/10.6084/m9.figshare.16964656.v2 (Wang et al., 2022).