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The coastal zone of China has experienced large increases in population, economy and urbanization since the early 1980s. Many studies have reported the loss, degradation and fragmentation of coastal wetlands in China at local to regional scales. To date, at the national scale, our knowledge of the spatial distribution, inter-annual variation and multi-decadal trends of coastal wetlands in China remains very limited. Here we analysed ~62,000 Landsat-5, -7 and -8 images over the period 1984–2018 and generated maps of coastal wetlands for individual years in China at 30-m spatial resolution. We found that coastal wetland area significantly decreased between 1984 and 2011. We also found a substantial increase in saltmarsh area and a stable trend of tidal flat area since 2012, driven by reduced anthropogenic activities and increased conservation and restoration efforts. These coastal wetland maps for the period 1984–2018 are invaluable for improvement of coastal wetland management and sustainability in China. China’s coasts have become more populous and urbanized. This study finds a rebound in the area of coastal wetlands, reflecting recent conservation and restoration, with large losses between 1984 and 2011 followed by increases in saltmarsh area and stabilization of tidal flats.

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989–2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989–2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002–2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China., The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands are unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2,798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7,474.6 km(2) of coastal wetlands in China in 2018, which included 5,379.8 km(2) of tidal flats, 1,856.4 km(2) of deciduous wetlands, and 238.3 km(2) of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

SUMMARY How ubiquitous transcription factors (TFs) coordinate temporal inputs from broadly expressed epigenetic factors to control cell fate remains poorly understood. Here, we uncover a molecular relationship between p53, an abundant embryonic TF, and WDR5, an essential member of the MLL chromatin modifying complex, that regulates mouse embryonic stem cell fate. Wild-type Wdr5 or transient Wdr5 knockout promotes a distinct pattern of global chromatin accessibility and spurs neuroectodermal differentiation through an RbBP5-dependent process in which WDR5 binds to, and activates transcription of, neural genes. Wdr5 rescue after its prolonged inhibition targets WDR5 to mesoderm lineage-specifying genes, stimulating differentiation toward mesoderm fates in a p53-dependent fashion. Finally, we identify a direct interaction between WDR5 and p53 that enables their co-recruitment to, and regulation of, genes known to control cell proliferation and fate. Our results unmask p53-dependent mechanisms that temporally integrate epigenetic WDR5 inputs to drive neuroectoderm and mesoderm differentiation from pluripotent cells., In Brief How ubiquitous chromatin-associated proteins and transcription factors (TFs) regulate cell fate determination is poorly understood. Li et al. show that regulation of the broadly expressed TF p53 by the chromatin-associated protein WDR5 is required for neuroectoderm versus mesoderm lineage determination in mouse embryonic stem cells (ESCs)., Graphical Abstract

Lakes are important water resources on the Mongolian Plateau (MP) for human's livelihood and production as well as maintaining ecosystem services. Previous studies, based on the Landsat-based analyses at epoch scale and visual interpretation approach, have reported a significant loss in the lake areas and numbers, especially from the late 1990s to 2010. Given the remarkable inter- and intra-annual variations of lakes in the arid and semi-arid region, a comprehensive picture of annual lake dynamics is needed. Here we took advantages of the power of all the available Landsat images and the cloud computing platform Google Earth Engine (GEE) to map water body for each scene, and then extracted lakes by post-processing including raster-to-vector conversion and separation of lakes and rivers. Continuous dynamics of the lakes over 1 km2 was monitored annually on the MP from 1991 to 2017. We found a significant shrinkage in the lake areas and numbers of the MP from 1991 to 2009, then the decreasing lakes on the MP have recovered since circa 2009. Specifically, Inner Mongolia of China experienced more dramatic lake variations than Mongolia. A few administrative regions with huge lakes, including Hulunbuir and Xilin Gol in Inner Mongolia and Ubsa in Mongolia, dominated the lake area variations in the study area, suggesting that the prior treatments on these major lakes would be critical for water management on the MP. The varied drivers of lake variations in different regions showed the complexity of factors impacting lakes. While both natural and anthropogenic factors significantly affected lake dynamics before 2009, precipitation played increasingly important role for the recovery of lakes on the MP after 2009.

Accurate phenology characterization is of great importance for measuring ecosystem dynamics, especially for carbon and water exchange between land and the atmosphere. Vegetation indices (VIs), calculated from land surface reflectance, are widely used to estimate phenology from the leaf and canopy structure perspective. Gross Primary Production (GPP) and solar-induced chlorophyll fluorescence (SIF) are used to estimate phenology from the canopy functional (physiological) perspective. To what degree are the spring phenology estimated from these different perspectives consistent with each other? In this study, we evaluated the consistency of the start of the growing season (SOS) in spring for snow-covered evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) using three vegetation indices, in-situ GPP data from the eddy covariance flux towers (GPPEC), GPP data from the Vegetation Photosynthesis Model (GPPVPM), and SIF data from the GOME-2. Results showed that SOSNDVI dates were much earlier than SOS dates from EVI (SOSEVI), land surface water index (LSWI) (SOSLSWI), GPP (SOSGPP; SOSGPP-EC, SOSGPP_VPM) and SIF (SOSSIF) for both snow-covered evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF). SOSLSWI dates were more linearly correlated with SOSGPP and SOSSIF than SOS dates from NDVI and EVI. At ENF sites, SOSLSWI dates were 17 (± 27) days later and SOSEVI were 25 (± 34) days later than SOSGPP_EC dates. At DBF sites, SOSLSWI and SOSEVI dates were 1-week (± 13 days) later than SOSGPP_EC dates. In the snow-covered regions at mid- to high-latitude in the Northern Hemisphere, SOSLSWI dates were 2˜3 weeks (± 5 days) later than those of SOSGPP_VPM and SOSSIF for both ENF and DBF. Our results clearly highlight the need for further investigation of NDVI-based SOS dates, which were likely affected by snowmelt in snow-covered forests, and the potential of LSWI for tracking the effects of snow on SOS dates. Estimations of SOS dates in snow-covered forests should consider the effects of both snow cover and temperature on leaf emergence (green-up) and gross primary production.

Drones are becoming increasingly used in a wide variety of industries and services and are delivering profound socioeconomic benefits. Technology needs to be in place to ensure safe operation and management of the growing fleet of drones. Mobile networks have connected tens of billions of devices on the ground in the past decades and are now ready to connect the drones flying in the sky. In this article, we share some of our findings in cellular connectivity for low altitude drones. We first present and analyze field measurement data collected during drone flights in a commercial Long-Term Evolution (LTE) network. We then present simulation results to shed light on the performance of a network when it is serving many drones simultaneously over a wide area. The results, analysis, and design insights presented in this article help enhance the understanding of the applicability and performance of providing mobile connectivity to low altitude drones., Comment: 10 pages; 8 figures

The data, information and knowledge on the tropical forest area and its dynamics in the Brazilian Amazon remain contentious. We use time-series satellite images to quantify annual forest area, loss and gain in the Brazilian Amazon during 2000–2017. We find that forest area was ~15% higher than the estimate by the official Brazilian forest dataset (PRODES), but annual forest-loss rates were twice the PRODES estimates (~0.027 × 106 km2 yr–1 during 2001–2016). Forest-loss rates increased again after 2013. The El Nino and drought year (2015/2016) drove large forest area loss. The cumulative forest-loss area within the protected areas (which include ~50% of forests in the region) was ~11% of the total forest-loss area, which highlights the roles of protected areas in forest conservation. A new estimation of forest cover and loss finds more area but also double the loss rates in recent years, relative to previous estimates. Deforestation increased after a low peak in 2013.

Author(s): Niu, Q; Xiao, X; Zhang, Y; Qin, Y; Dang, X; Wang, J; Zou, Z; Doughty, RB; Brandt, M; Tong, X; Horion, S; Fensholt, R; Chen, C; Myneni, RB; Xu, W; Di, G; Zhou, X | Abstract: Multiple ecological engineering projects have been implemented in semiarid and subhumid Northern China since 1978 with the purpose to combat desertification, control dust storms, and improve vegetation cover. Although a plethora of local studies exist, the effectiveness of these projects has not been studied in a systematic and comprehensive way. Here, we used multiple satellite-based time-series data as well as breakpoint analysis to assess shifts in leaf area index (a proxy for green vegetation cover), gross primary production, and aboveground biomass in Northern China. We documented increased vegetation growth in northwest and southeastern parts of the region, despite drought anomalies as documented by the standardized precipitation-evapotranspiration index during 1982–2016. Significant breakpoints in leaf area index were observed for over 72.5% of the southeastern and northwestern regions, and 70.6% of these breakpoints were detected after 1999, which correspond well to the areas with the highest ecological engineering efforts. Areas with negative trends were mainly located in the Inner Mongolian Plateau, Hulun Biur, Horqin Sand Land, and urban areas. The Loess Plateau had the largest increase in vegetation growth, followed by the north parts of Northern China where biomass increased more in the provinces of Shanxi, Liaoning, Shannxi, Hebei, and Beijing than Xinjiang, Inner Mongolia, Tianjin, and Qinghai. Our results show that multiple ecological engineering projects in the region have increased vegetation cover, production, and aboveground biomass that have led to improved environmental conditions in the study area.

High demand for tea has driven the expansion of tea plantations in the tropical and subtropical regions over the past few decades. Tea plant cultivation promotes economic development and creates job opportunities, but tea plantation expansion has significant impacts on biodiversity, carbon and water cycles, and ecosystem services. Mapping the spatial distribution and extent of tea plantations in a timely fashion is crucial for land use management and policy making. In this study, we mapped tea plantation expansion in Menghai County, Yunnan Province, China. We analyzed the structure and features of major land cover types in this tropical and subtropical region using (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and (2) time series Landsat TM/ETM+/OLI imagery. Tea plantation maps for 2010 and 2015 were generated using the pixel-based support vector machine (SVM) approach at 30 m resolution, which had high user/producer accuracies of 83.58%/91.67% and 87.50%/90.83%, respectively. The resultant maps show that tea plantation area increased by 33.56% (∼9335 ha), from ∼27,817 ha in 2010 to ∼37,152 ha in 2015. The additional tea plantation area was mainly converted from forest (32.50%) and cropland (67.50%). The results showed that the combination of PALSAR and optical data performed better in tea plantation mapping than using optical data only. This study provides a promising new approach to identify and map tea plantations in complex tropical landscapes at high spatial resolution.

Sustainable crop grain production and food security is a grand societal challenge. Substantial investments in China's agriculture have been made in the past decades, but our knowledge on cropland gross primary production in China remains limited. Here we analyzed gross primary production (GPP), solar-induced chlorophyll fluorescence (SIF), terrestrial water storage, crop grain production, and agricultural investment and policy during 2000–2018. We found that based on croplands in 2000, approximately 52 × 106 ha (~37%) had continuous increasing trends in GPP during 2000–2018, which were mainly located in northern China. GPP for 63% of croplands was stagnant, declined, or had no significant change. At the national scale, annual cropland GPP increased during 2000–2008 but became stagnant in 2009–2018, which was inconsistent with the interannual trend in the crop grain production data for 2009–2018. The spatial mismatch between crop production and water availability became worse. The major grain exporting provinces, mostly located in water-stressed regions, experienced increased water resource constraints, which posed a challenge for sustainable grain production. The stagnant cropland GPP and increasing water resource constraints highlight the urgent need for sustainable management for crop production and food security in China.

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986–1992, an increasing period during 1993–2001 and a decreasing period during 2002–2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets.

To accurately estimate the terrestrial carbon cycle and food production, it is essential to understand how gross primary production (GPP) of irrigated and non-irrigated grasslands and croplands respond to drought and pluvial events. This study analyzed annual GPP of irrigation-permitted and non-permitted grasslands, winter wheat (Triticum aestivum L.), other C3 croplands, and C4 croplands in Caddo County of western Oklahoma from 2010 through 2016, a period which consisted of extreme drought (2011) and pluvial events (2015). First, we compared GPP from the Vegetation Photosynthesis Model (GPPVPM) and GPP data from the Moderate Resolution Imaging Spectroradiometer (GPPMOD17) with GPP estimates from three eddy covariance towers (GPPEC) in Oklahoma. GPPVPM more accurately estimated mean daily GPPEC at each of the three sites than GPPMOD17. Second, we analyzed the seasonal and interannual dynamics of GPPVPM for eight pixels, one each for the four irrigation-permitted and non-permitted land types. The interannual variation of GPPVPM was due to the complexity of decision making and practice for irrigation, cropping intensity, and crop types. Finally, at the county scale, annual GPPVPM from the 2011 drought and pluvial 2015 were compared with mean annual GPPVPM from the other 5 years of the study period. The results show that for the 2011 drought: 1) non-permitted C4 croplands had the largest percentage decrease in GPP, but permitted C4 croplands had the smallest decrease; 2) regardless of water rights, GPP was significantly lower than the 5-year reference mean for grasslands, winter wheat, and other C3 crops; and 3) non-permitted lands were more affected by drought than irrigation-permitted lands, except for grasslands, which had similar percentage reductions in GPP. Results for the pluvial year 2015 show that: 1) GPP was significantly higher for grasslands, winter wheat, and non-permitted C3 croplands than the 5-year reference mean, but there was no significant difference in GPP for irrigation-permitted C3 croplands or non-permitted C4 croplands; and 2) GPP for C4 irrigation-permitted croplands was lower than the 5-year reference mean. Crop-specific responses to drought and pluvial events largely depend on a landowner’s ability to irrigate, and caution should be used when assessing or generalizing how crops respond to climate variability, drought, and pluvial conditions in the absence of irrigation-related data.

This paper studies online shortest path routing over multi-hop networks. Link costs or delays are time-varying and modeled by independent and identically distributed random processes, whose parameters are initially unknown. The parameters, and hence the optimal path, can only be estimated by routing packets through the network and observing the realized delays. Our aim is to find a routing policy that minimizes the regret (the cumulative difference of expected delay) between the path chosen by the policy and the unknown optimal path. We formulate the problem as a combinatorial bandit optimization problem and consider several scenarios that differ in where routing decisions are made and in the information available when making the decisions. For each scenario, we derive a tight asymptotic lower bound on the regret that has to be satisfied by any online routing policy. These bounds help us to understand the performance improvements we can expect when (i) taking routing decisions at each hop rather than at the source only, and (ii) observing per-link delays rather than end-to-end path delays. In particular, we show that (i) is of no use while (ii) can have a spectacular impact. Three algorithms, with a trade-off between computational complexity and performance, are proposed. The regret upper bounds of these algorithms improve over those of the existing algorithms, and they significantly outperform state-of-the-art algorithms in numerical experiments., 18 pages

Over the past few decades, wide encroachment of eastern redcedar (Juniperus virginiana) and Ashe juniper (Juniperus ashei) into the prairies of the U.S. Great Plains has affected wildlife habitats, forage and livestock production, and biogeochemical cycles. This study investigates the spatio-temporal dynamics of juniper forest encroachment into tallgrass prairies by generating juniper forest encroachment maps from 1984 to 2010 at 30 m spatial resolution. A pixel and phenology-based mapping algorithm was used to produce the time series maps of juniper forest encroachment using a combination of Phased Array type L-band Synthetic Aperture Radar (PALSAR) mosaic data from 2010 and Landsat 5 and 7 data (10,871 images from 1984 to 2010). We analyzed the resultant maps to understand the dynamics of juniper forest encroachment at state and county spatial scales and examined juniper occurrence by geographic region and soil type. The juniper forest maps were generated over five multi-year periods: the late 1980s (1984–1989), early 1990s (1990–1994), late 1990s (1995–1999), early 2000s (2000–2004), and late 2000s (2005–2010). We also produced a map of time since stand detection of juniper forests in 2010. Our major findings include: (1) juniper forests have expanded linearly in time at an annual rate of ~ 40 km2/year since 1984; (2) juniper forests had notable spatial clusters in its expansion process; (3) ~ 65% of juniper forests in 2010 were

Intersection management of automated vehicles relies on wireless communication, whereby communication resources should be allocated to vehicles while maintaining safety. We present a collision-aware resource allocation (CARA) strategy for coordination of automated and connected vehicles by a centralized intersection manager. The proposed strategy is based on a self-triggered approach and proactively reduces the risk of channel congestion by only assigning communication resources to vehicles that are in critical configurations, i.e., when there is a risk for a future collision. Compared with collision-agnostic communication strategies, typically considered for automated intersection management, the CARA strategy aims to bridge the gap between control, sensing, and communication. It is shown to significantly reduce the required amount of communication (albeit with a slight increase in the control cost), without compromising safety. Furthermore, control cost can be reduced by allowing more frequent communication, which we demonstrate through a trade-off analysis between control performance and communication load. Hence, CARA can operate in communication-limited scenarios, but also be modified for scenarios where the control cost is of primary interest.

Accurate prediction of soil organic carbon (SOC) at fine scales is frequently limited by data availability but fine-scale maps of soil properties are often of great importance for local agricultural land management and watershed processes assessment. Terrain attributes derived from elevation have been commonly used predictor variables in SOC mapping. The aim of this study is to map SOC density (0–30 cm) using hybrid methods that combine regional topographic models and a residual kriging procedure using local samples from an area of interest and evaluate the effects of different sampling designs for residual kriging on final map quality. The regional topographic model was developed based on light detection and ranging (lidar)-derived topographic covariates using random forest to map SOC density on a cropland site intensively sampled for SOC (n = 224). To test ability to generate a local calibration of this regional model, a second intensively sampled cropland site (n = 225) was used to generate a reference SOC density map where residual kriging was implemented involving different numbers of local samples (n = 10–255) and different sample selection schemes: stratified random sampling (SRS) and spatial coverage sampling with close-pairs inclusions (SCS+). To generate a population of local sample sets, 500 repeats were conducted for each scheme from the reference map. By analyzing the distribution of relative mean error (RME) of the overall map for the second site with repeated selection of local samples, we found a smaller variation in RME for SCS+ than that for SRS for a given sample size. However, the E(RME) estimates for SCS+ slightly varied with number of local samples used while the E(RME) for SRS was stabilized at zero over that range in local sampling intensity. There was negligible difference between the distributions of RME for the two sampling schemes at individual points, except a finding of somewhat lower RME for SCS+ based point estimates. These results indicate that SCS+ can generate smaller variation in overall map quality while SRS can provide unbiased mean estimate for an area of interest. This study demonstrates the utility of extrapolating a regional topographic model by use of a limited number of new samples for local calibration and provides insight for practitioners who are interested in mapping soil carbon distribution in sparsely sampled areas of interest.

South America has the largest tropical rainforests and the richest biodiversity in the world. It is challenging to map tropical forests and their spatio-temporal changes because forests are facing fragmentation from human activities (e.g., logging, deforestation), drought, and fire, as well as persistent clouds. Here we present a robust approach to map forests in South America during 2007–2010 and analyze the consistency and uncertainty among eight major forest maps in South America. Greenness-relevant MOD13Q1 NDVI and structure/biomass-relevant ALOS PALSAR time series data recorded 2007 through 2010 were coupled to identify and map forests at 50-m spatial resolution. Both area and spatial comparison were conducted to analyze the consistency and uncertainty of these eight forest maps. Annual 50-m PALSAR/MODIS forest maps were generated during 2007–2010 and the total forest area in South America was about 8.63 × 10 6 km 2 in 2010. Large differences in total forest area (8.2 × 10 6 km 2 –12.7 × 10 6 km 2 ) existed among these forest products, especially in the forest edges, semi-humid tropical, and subtropical regions. Forest products generated under a similar forest definition had similar or even larger variation than those generated with contrasting forest definitions. We also find out that one needs to consider leaf area index as an adjusting factor and use much higher threshold values in the Vegetation Continuous Field (VCF) datasets to estimate forest cover areas. Analyses of PALSAR/MODIS forest maps in 2008/2009 showed a relatively small rate of loss (3.2 × 10 4 km 2 year − 1 ) in net forest cover, similar to that of FAO FRA (3.3 × 10 4 km 2 year − 1 ), but much higher annual rates of forest loss and gain. The rate of forest loss (0.195 × 10 6 km 2 year − 1 ) was higher than that of Global Forest Watch (0.081 × 10 6 km 2 year − 1 ). PALSAR/MODIS forest maps showed that more deforestation occurred in the unfragmented forest areas. Caution should be used when using the different forest maps to analyze forest loss and make policies regarding forest ecosystem services and biodiversity conservation. The integration of PALSAR and MODIS images during 2007–2010 provides annual maps of forests in South America with improved accuracy and reduced uncertainty.

China is the largest rice producer and consumer in the world. Accurate estimations of paddy rice planting area and rice grain production is important for feeding the increasing population in China. However, Southern China had substantial losses in paddy rice area over the last three decades in those regions where paddy rice has traditionally been produced. Several studies have shown increased paddy rice area in Northeast China. Here we document the annual dynamics of paddy rice area, gross primary production (GPP), and grain production in Northeast China (Heilongjiang, Jilin and Liaoning provinces) during 2000–2017 using agricultural statistical data, satellite images, and model simulations. Annual maps derived from satellite images show that paddy rice area in Northeast China has increased by 3.68 million ha from 2000 to 2017, which is more than the total combined paddy rice area of North Korea, South Korea, and Japan. Approximately 82% of paddy rice pixels had an increase in annual GPP during 2000–2017. The expansion of paddy rice area slowed down substantially since 2015. Annual GPP from those paddy rice fields cultivated continuously over the 18 years were moderately higher than that from other paddy rice fields, which suggested that improved management practices could increase grain production in the region. There was a strong linear relationship between annual GPP and annual rice grain production in Northeast China by province and year, which illustrates the potential of using satellite-based data-driven model to track and assess grain production of paddy rice in the region. Northeast China is clearly an emerging rice production base and plays an increasing role in crop production and food security in China. However, many challenges for the further expansion and sustainable cultivation of paddy rice in Northeast China remain.

Beyond visual line-of-sight connectivity is key for use cases of unmanned aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and rescue missions. Cellular networks stand ready to support flying UAVs by providing wide-area, quality, and secure connectivity for UAV operations. Ensuring reliable connections in the presence of UAV movements is important for safety control and operations of UAVs. With increasing height above the ground, the radio environment changes. Using terrestrial cellular networks to provide connectivity to the UAVs moving in the sky may face new challenges. In this article, we share some of our findings in mobility support for cellular connected UAVs. We first identify how the radio environment changes with altitude and analyze the corresponding implications on mobility performance. We then present evaluation results to shed light on the mobility performance of cellular connected UAVs. We also discuss potential enhancements for improving mobility performance in the sky., Comment: 6 pages, 5 figures

Cellular connectivity to low altitude unmanned aerial vehicles (UAVs) has received significant interest recently which has led to a 3GPP study on enhanced LTE support for connected UAVs in Release 15. The objective of the study is to investigate the capability of long-term evolution (LTE) networks for providing connectivity to UAVs. In this article, we provide an overview of the key findings of the 3GPP Release-15 study-item phase. We first introduce UAV connectivity requirements and performance evaluation scenarios defined in the study. We then discuss radio channel models and the key identified challenges of using LTE networks to provide connectivity to UAVs. We summarize potential solutions to address the challenges including interference detection and mitigation techniques, mobility enhancements, and UAV identification. Finally, we also shed light on the key features standardized during the Release-15 work-item phase., 7 pages, 6 figures, submitted for publication

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.

Cell-type specific transcription factors cooperate with chromatin-modifying proteins to orchestrate proper differentiation, but little is known how ubiquitous embryonic proteins coordinate inputs from broadly expressed epigenetic factors to control cell fate. Here, we uncover a previously unappreciated molecular relationship between p53, an abundant embryonic protein, and Wdr5, an essential member of the COMPASS chromatin modifying complex, that regulates mouse embryonic stem cell (ESC) fate. Intact, or brief disruption of, Wdr5 activity promotes a distinct pattern of global chromatin accessibility and spurs neuroectodermal specification through Wdr5 binding to, and transcription of, neural genes. This leads to RbBP5-dependent differentiation of 3D Rx+ neuroectodermal organoids. In contrast, prolonged Wdr5 inhibition transcriptionally impairs ribosome biogenesis, which upregulates p53 activity, triggering increased expression of pro-mesoderm p53 target genes and emergence of a chromatin accessibility state permissive for mesoderm specification. Rescue of Wdr5 function after its extended inhibition targets WDR5 to mesoderm lineage-specifying genes, stimulating differentiation toward contractile cardiogenic and hematopoietic mesoderm fates. This cell fate transition is p53-dependent, as p53 inactivation partially rescues neuroectoderm differentiation and inhibits mesoderm formation. Our results unmask a p53-dependent mechanism that temporally integrates an epigenetic WDR5 input to drive neuroectoderm and mesoderm differentiation from pluripotent cells.