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Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.

Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

Agricultural land use change substantially affects climate, water, ecosystems, biodiversity, and human welfare. In recent decades, due to increasing population and food demand and the backdrop of global warming, croplands have been expanding into higher latitude regions. One such hotspot is paddy rice expansion in northeast China. However, there are no maps available for documenting the spatial and temporal patterns of continuous paddy rice expansion. In this study, we developed an automated, Landsat-based paddy rice mapping (Landsat-RICE) system that uses time series Landsat images and a phenology-based algorithm based on the unique spectral characteristics of paddy rice during the flooding/transplanting phase. As a pilot study, we analyzed all the available Landsat images from 1986 to 2010 (498 scenes) in one tile (path/row 113/27) of northeast China, which tracked paddy rice expansion in epochs with five-year increments (1986-1990, 1991-1995, 1996-2000, 2001-2005, and 2006-2010). Several maps of land cover types (barren land and built-up land; evergreen, deciduous and sparse vegetation types; and water-related land cover types such as permanent water body, mixed pixels of water and vegetation, spring flooded wetlands and summer flooded land) were generated as masks. Air temperature was used to define phenology timing and crop calendar, which were then used to select Landsat images in the phenology-based algorithms for paddy rice and masks. The resultant maps of paddy rice in the five epochs were evaluated using validation samples from multiple sources, and the overall accuracies and Kappa coefficients ranged from 84 to 95% and 0.6-0.9, respectively. The paddy rice area in the study area substantially increased from 1986 to 2010, particularly after the 1990s. This study demonstrates the potential of the Landsat-RICE system and time series Landsat images for tracking agricultural land use changes at 30-m resolution in the temperate zone with single crop cultivation.

The contiguous United States (CONUS), especially the West, faces challenges of increasing water stress and uncertain impacts of climate change. The historical information of surface water body distribution, variation, and multidecadal trends documented in remote-sensing images can aid in water-resource planning and management, yet is not well explored. Here, we detected open-surface water bodies in all Landsat 5, 7, and 8 images (∼370,000 images, >200 TB) of the CONUS and generated 30-meter annual water body frequency maps for 1984-2016. We analyzed the interannual variations and trends of year-long water body area, examined the impacts of climatic and anthropogenic drivers on water body area dynamics, and explored the relationships between water body area and land water storage (LWS). Generally, the western half of the United States is prone to water stress, with small water body area and large interannual variability. During 1984-2016, water-poor regions of the Southwest and Northwest had decreasing trends in water body area, while water-rich regions of the Southeast and far north Great Plains had increasing trends. These divergent trends, mainly driven by climate, enlarged water-resource gaps and are likely to continue according to climate projections. Water body area change is a good indicator of LWS dynamics in 58% of the CONUS. Following the 2012 prolonged drought, LWS in California and the southern Great Plains had a larger decrease than surface water body area, likely caused by massive groundwater withdrawals. Our findings provide valuable information for surface water-resource planning and management across the CONUS.

21Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is 22 vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In 23 this study, we estimate GPP in North America (NA) using the satellite-based Vegetation 24Photosynthesis Model (VPM), MODIS images at 8-day temporal and 500 m spatial resolutions, 25 , which accounts for ~11.0% of the global terrestrial GPP and 35 is within the range of annual GPP estimates from six other process-based and data-driven 36 models ). Among the seven models, some models did not capture the 37 spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. 38The results from this study demonstrate the reliable performance of VPM at the continental 39 scale, and the potential of SIF data being used as a benchmark to compare with GPP models. 40 41 3

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