The rapid development of photovoltaic (PV) powerplants in the world has drawn attention on their climate and environmental impacts. In this study, we assessed the effects of PV powerplants on surface temperature using 23 largest PV powerplants in the world with thermal infrared remote sensing technique. Our result showed that the installation of the PV powerplants had significantly reduced the daily mean surface temperature by 0.53 °C in the PV powerplant areas. The cooling effect with the installation of the PV powerplants was much stronger during the daytime than the nighttime with the surface temperature dropped by 0.81 °C and 0.24 °C respectively. This cooling effect was also depended on the capacity of the powerplants with a cooling rate of −0.32, −0.48, and −0.14 °C/TWh, respectively, for daily mean, daytime, and nighttime temperature. We also found that the construction of the powerplants significantly decreased the surface albedo from 0.22 to 0.184, but significantly increased the effective albedo (surface albedo plus electricity conversion) from 0.22 to 0.244, suggesting conversion of solar energy to electrical energy is a major contributor to the observed surface cooling. Our further analyses showed that the nighttime cooling in the powerplants was significantly correlated with the latitude and elevation of the powerplants as well as the annual mean temperature, precipitation, solar radiation, and normalized difference vegetation index (NDVI). This means the temperature effect of the PV powerplants depended on regional geography, climate and vegetation conditions. This finding can be used to guide the selection of the sites of PV powerplants in the future.

Exploring the surface energy exchange between atmosphere and water bodies is essential to gain a quantitative understanding of regional climate change, especially for the lakes in the desert. In this study, measurements of energy flux and water vapor were performed over a lake in the Badain Jaran Desert, China from March 2012 to March 2013. The studied lake had about a 2-month frozen period (December and January) and a 10-month open-water period (February-November). Latent heat flux (LE) and sensible heat flux (Hs) acquired using the eddy covariance technique were argued by measurements of longwave and shortwave radiation. Both fluxes of longwave and shortwave radiation showed seasonal dynamics and daily fluctuations during the study period. The reflected solar radiation was much higher in winter than in other seasons. LE exhibited diurnal and seasonal variations. On a daily scale, LE was low in the morning and peaked in the afternoon. From spring (April) to winter (January), the diurnal amplitude of LE decreased slowly. LE was the dominant heat flux throughout the year and consumed most of the energy from the lake. Generally speaking, LE was mostly affected by changes in the ambient wind speed, while Hs was primarily affected by the product of water-air temperature difference and wind speed. The diurnal LE and Hs were negatively correlated in the open-water period. The variations in Hs and LE over the lake were differed from those on the nearby land surface. The mean evaporation rate on the lake was about 4.0 mm/d over the entire year, and the cumulative annual evaporation rate was 1445 mm/a. The cumulative annual evaporation was 10 times larger than the cumulative annual precipitation. Furthermore, the average evaporation rates over the frozen period and open-water period were approximately 0.6 and 5.0 mm/d, respectively. These results can be used to analyze the water balance and quantify the source of lake water in the Badain Jaran Desert.

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Abstract. Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at https://doi.org/10.5281/zenodo.6849477 (Zhang et al., 2022).

The Badain Jaran Desert is the second-largest area of shifting sands in China. Our first measurements of the energy components and water vapor fluxes on a megadune using eddy covariance technology were taken from April 2012 to April 2013. The results indicate that the longwave and shortwave radiative fluxes exhibited large fluctuations and seasonal dynamics. The total radiative energy loss by longwave and shortwave radiation was greater on the megadune than from other underlying surfaces. The radiation partitioning was different in different seasons. The land-atmosphere interaction was primarily represented by the sensible heat flux. The average sensible heat flux (40.1 W/m 2 ) was much larger than the average latent heat flux (14.5 W/m 2 ). Soil heat flux played an important role in the energy balance. The mean actual evaporation was 0.41 mm/d, and the cumulative actual evaporation was approximately 150 mm/a. The water vapor would transport downwardly and appear as dew condensation water. The amount of precipitation determined the actual evaporation. The actual evaporation was supposed to be equal to the precipitation on the megadune and the precipitation was difficult to recharge the groundwater. Our study can provide a foundation for further research on land-atmosphere interactions in this area.