To understand the relative importance of biogenic emission, biomass burning emission on volatile organic compounds in Amazon, the spatial and temporal correlations between atmospheric column‐integrated formaldehyde (HCHO) and fire count, vegetation hydrological states index, surface solar radiation flux, near‐surface air temperature are studied using synergized satellite products and reanalysis data. A recently developed microwave‐based vegetation index (emissivity difference vegetation index, EDVI) with high temporal resolution is used for the linkages between vegetation and HCHO at daily scale with and without fire contaminations. At large regional scale, EDVI shows highest spatial correlation with HCHO indicating the important controlling effect of biogenic emission. In given subregions with frequent fires, the temporal variations of monthly HCHO show much stronger correlations with fire count. The temporal correlations between monthly HCHO and EDVI are vague and even negative in some subregions. Radiation and temperature show stable positive temporal correlations with HCHO, particularly in areas with few fires. After excluding the samples contaminated by fires, the daily temporal correlation between vegetation (EDVI) and HCHO becomes significant and positive in most areas except the northern rainforest with weak temporal variations of EDVI. We proposed a bilinear model of biogenic‐emission‐induced HCHO (B‐HCHO) using radiation and EDVI as inputs. The bias between modeled long‐term mean B‐HCHO and satellite observation is less than 20%. And the daily time series of modeled B‐HCHO matches observations as well. It is the first time to provide satellite observational evidences of the relative importance of biogenic emission and biomass burning emission on HCHO, the proxy of atmospheric volatile organic compounds concentration.

Understanding the effect of land use and land cover (LULC) type change on watershed hydrological response is essential for adopting applicable measures to control floods. In China, the Grain to Green Program (GTGP) and the Natural Forest Conservation Program (NFCP) have had a substantial impact on LULC. We investigate the effect of these conservation efforts on flood peak discharge in two mountainous catchments. We used a series of Landsat images ranging from 1990 to 2016/2017 to evaluate the LULC changes. Further to this, the hydrological responses at the basin and sub-basin scale were generated by the Hydrologic Modeling System (HEC-HMS) under four LULC scenarios. Between 1990 and 2016/2017, both catchments experienced an increase in forest and urban land by 18% and 2% in Yanhe and by 16% and 8% in Guangyuan, respectively. In contrast, the agricultural land decreased by approximately 30% in Yanhe and 24% in Guangyuan, respectively. The changes in land cover resulted in decrease in flood peak discharge ranging from 14% in Yanhe to 6% in Guangyuan. These findings provide a better understanding on the impact of reforestation induced LULC change on spatial patterns of typical hydrological responses of mountainous catchment and could help to mitigate flash flood hazards in other mountainous regions.

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m² error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

Spatiotemporal changes in the surface area of inland water bodies have important implications in regional water resources, flood control, and drought hazard prediction. Although inland water bodies have been investigated intensively, few studies have looked at the effect of human activities and climate variability on surface area of inland waters at a larger scale over time and space. In this study, we used MODIS (MOD13Q1) images to determine water surface area extent at 250 m spatial resolution. We then applied this algorithm with MOD13Q1 images taken at 16-day intervals from 2000 to 2018 to a large river basin in China’s northeast high latitude region with dense stream network and abundant wetlands to investigate spatiotemporal distribution and dynamics of inland water bodies. The study identified 209 ponds, lakes, and reservoirs with an average total surface area of 2080 km² in the past 19 years. The total water surface area fluctuated largely from 942 km² to 5169 km², corresponding to rainfall intensity and flood. We found that the total water surface area in this high latitude river basin showed an increasing trend during the study period, while the annual precipitation amount in the river basin also had an increasing trend concurrently. Precipitation and irrigation significantly contributed to the monthly change of water surface area, which reached the highest during June and August. The increase of water surface area was significant in the lower basin floodplain region, where agricultural irrigation using groundwater for rice production has progressed. Four nationally important wetland preserves (Zhalong, Xianghai, Momoge, and Chagan Lake) in the river basin made up nearly 50% of the basin’s total water surface area, of which Zhalong, Xianghai, and Momoge are designated by The Ramsar Convention as wetland sites of international importance. Seasonally, these water bodies reached their maximal surface area in August, when both the monsoon weather and agricultural discharge prevailed. This study demonstrates that water surface area in a high latitude river basin is affected by both human activities and climate variation, implying that high latitude regions will likely experience more changes in surface water distribution as global climate change continues and agriculture becomes intensified.

In this study, we used data from multiple sensors onboard NASA Aqua satellite to conduct a 10‐year (2002–2011) remote sensing of microwave emissivity difference vegetation index (EDVI) over China. We investigated the spatial and temporal variations of EDVI in tropical and subtropical evergreen forest, deciduous forest, rice and wheat farmlands, grassland, and montane vegetation regions. The average of China's EDVI is positive in dense vegetation regions and negative in sparse vegetation regions, depending on the proportion of bare soil and open water. In all selected studying regions, the seasonal variation of EDVI follows the trend of vegetation phenology, even in regions with large proportion of open water. EDVI is positively correlated to the greenness of vegetation (normalized difference vegetation index [NDVI]) with certain phase difference in their seasonal cycle. In autumn, EDVI begins to decline earlier and faster than NDVI. In tropical rainforest, EDVI also starts to increase earlier than NDVI in spring. The large‐scale spatial distribution of EDVI under clear sky and cloudy sky is similar. In montane vegetation regions, EDVI under heavy clouds (90% fraction) conditions is significantly greater than that under clear sky (10% fraction), indicating a possible cloud induced enhancement of vegetation water content. In forests and croplands in the plains, such effect is not remarkable.