Satellite-Observed Energy Budget Change of Deforestation in Northeastern China and its Climate Implications

Biophysical Effects of Temperate Forests in Regulating Regional Temperature and Precipitation Pattern across Northeast China

The temperate forests in Northeast China are an important ecological barrier. However, the way in which temperate forests regulate the regional temperature and water cycling remains unclear. In this study, we quantitatively evaluated the role that temperate forests play in the regulation of the regional temperature and precipitation by combining remote sensing observations with a state-of-the-art regional climate model. Our results indicated that the forest ecosystem could slightly warm the annual air temperature by 0.04 ± 0.02 °C and bring more rainfall (17.49 ± 3.88 mm) over Northeast China. The temperature and precipitation modification function of forests varies across the seasons. If the trees were not there, our model suggests that the temperature across Northeast China would become much colder in the winter and spring, and much hotter in the summer than the observed climate. Interestingly, the temperature regulation from the forest ecosystem was detected in both forested regions and the adjacent agricultural areas, suggesting that the temperate forests in Northeast China cushion the air temperature by increasing the temperature in the winter and spring, and decreasing the temperature in the summer over the whole region. Our study also highlights the capacity of temperate forests to regulate regional water cycling in Northeast China. With high evapotranspiration, the forests could transfer sufficient moisture to the atmosphere. Combined with the associated moisture convergence, the temperate forests in Northeast China brought more rainfall in both forest and agricultural ecosystems. The increased rainfall was mainly concentrated in the spring and summer; these seasons accounted for 93.82% of the total increase in rainfall. These results imply that temperate forests make outstanding contributions to the maintainance of the sustainable development of agriculture in Northeast China.

Reduction of surface radiative forcing observed from remote sensing data during global COVID-19 lockdown

The calamity of the COVID-19 pandemic during the early half of 2020 not only caused a huge physical and economic loss but altered the social behavior of the whole world. The social and economic stagnation imposed in many countries and served as a major cause of perturbation in atmospheric composition. This paper utilized the relation between atmospheric composition and surface radiation and analyzed the impact of global COVID-19 lockdown on land surface solar and thermal radiation. Top of atmosphere (TOA) and surface radiation are obtained from the Clouds and Earth's Radiant Energy System (CERES) and European Reanalysis product (ERA5) reanalysis product. Aerosol Optical Depth (AOD) is obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) while Nitrogen dioxide (NO₂)_, and sulfur dioxide (SO₂) are obtained from Ozone Monitoring Instrument (OMI). Observations of all mentioned parameters are studied for the global lockdown period of 2020 (from January to July) and compared with the corresponding months of the previous four years (2016-19) observations. Regarding surface radiation, April 2020 is the most affected month during the pandemic in which 0.2% increased net solar radiation (NSR), while 3.45% and 4.8% decreased net thermal radiation (NTR) and net radiation (NR) respectively was observed. Average radiative forcing during March-May 2020 was observed as 1.09 Wm^-2, -2.19 Wm^-2 and -1.09 Wm^-2 for NSR, NTR and NR, respectively. AOD was reduced by 0.2% in May 2020 while NO₂ and SO₂ were reduced by 5.4% and 8.8%, respectively, in April 2020. It was observed that NO₂ kept on reducing since January 2020 while SO₂ kept on reducing since February 2020 which were the pre-lockdown months. These results suggest that a more sophisticated analysis is needed to explain the atmosphere-radiation relation.

Influence of Soil Moisture vs. Climatic Factors in Pinus Halepensis Growth Variability in Spain: A Study with Remote Sensing and Modeled Data

The influence of soil water content on Aleppo pine growth variability is analyzed against climatic variables, using satellite and modeled soil moisture databases. The study was made with a dendrochronological series of 22 forest sites in Spain with different environmental conditions. From the results of the correlation analysis, at both daily and monthly scales, it was observed that soil moisture was the variable that correlated the most with tree growth and the one that better identified the critical periods for this growth. The maximum correlation coefficients obtained with the rest of the variables were less than half of that obtained for soil moisture. Multiple linear regression analysis with all combinations of variables indicated that soil moisture was the most important variable, showing the lowest p-values in all cases. While identifying the role of soil moisture, it was noted that there was appreciable variability between the sites, and that this variability is mainly modulated by water availability, rather than thermal conditions. These results can contribute to new insights into the ecohydrological dynamics of Aleppo pine and a methodological approach to the study of many other species.

Spatio-Temporal Trends of Surface Energy Budget in Tibet from Satellite Remote Sensing Observations and Reanalysis Data

Being the highest and largest land mass of the earth, the Tibetan Plateau has a strong impact on the Asian climate especially on the Asian monsoon. With high downward solar radiation, the Tibetan Plateau is a climate sensitive region and the main water source for many rivers in South and East Asia. Although many studies have analyzed energy fluxes in the Tibetan Plateau, a long-term detailed spatio-temporal variability of all energy budget parameters is not clear for understanding the dynamics of the regional climate change. In this paper, satellite remote sensing and reanalysis data are used to quantify spatio-temporal trends of energy budget parameters, net radiation, latent heat flux, and sensible heat flux over the Tibetan Plateau from 2001 to 2019. The validity of both data sources is analyzed from in situ ground measurements of the FluxNet micrometeorological tower network, which verifies that both datasets are valid and reliable. It is found that the trend of net radiation shows a slight increase. The latent heat flux increases continuously, while the sensible heat flux decreases continuously throughout the study period over the Tibetan Plateau. Varying energy fluxes in the Tibetan plateau will affect the regional hydrological cycle. Satellite LE product observation is limited to certain land covers. Thus, for larger spatial areas, reanalysis data is a more appropriate choice. Normalized difference vegetation index proves a useful indicator to explain the latent heat flux trend. Despite the reduction of sensible heat, the atmospheric temperature increases continuously resulting in the warming of the Tibetan Plateau. The opposite trend of sensible heat flux and air temperature is an interesting and explainable phenomenon. It is also concluded that the surface evaporative cooling is not the indicator of atmospheric cooling/warming. In the future, more work shall be done to explain the mechanism which involves the complete heat cycle in the Tibetan Plateau.

Development of Land Cover Classification Model Using AI Based FusionNet Network

Prompt updates of land cover maps are important, as spatial information of land cover is widely used in many areas. However, current manual digitizing methods are time consuming and labor intensive, hindering rapid updates of land cover maps. The objective of this study was to develop an artificial intelligence (AI) based land cover classification model that allows for rapid land cover classification from high-resolution remote sensing (HRRS) images. The model comprises of three modules: pre-processing, land cover classification, and post-processing modules. The pre-processing module separates the HRRS image into multiple aspects by overlapping 75% using the sliding window algorithm. The land cover classification module was developed using the convolutional neural network (CNN) concept, based the FusionNet network and used to assign a land cover type to the separated HRRS images. Post-processing module determines ultimate land cover types by summing up the separated land cover result from the land cover classification module. Model training and validation were conducted to evaluate the performance of the developed model. The land cover maps and orthographic images of 547.29 km2 in area from the Jeonnam province in Korea were used to train the model. For model validation, two spatial and temporal different sites, one from Subuk-myeon of Jeonnam province in 2018 and the other from Daseo-myeon of Chungbuk province in 2016, were randomly chosen. The model performed reasonably well, demonstrating overall accuracies of 0.81 and 0.71, and kappa coefficients of 0.75 and 0.64, for the respective validation sites. The model performance was better when only considering the agricultural area by showing overall accuracy of 0.83 and kappa coefficients of 0.73. It was concluded that the developed model may assist rapid land cover update especially for agricultural areas and incorporation field boundary lineation is suggested as future study to further improve the model accuracy.

Comparison of surface energy budgets and feedbacks to microclimate among different land use types in an agro-pastoral ecotone of northern China

The biophysical effect of land use conversion plays a significant role in regulating climate change. Owing to albedo and evapotranspiration (ET) change, the effect of energy budget difference on land surface temperature (LST) is important but unclear among contrasting land use types, especially in temperate semi-arid regions. Based on moderate-resolution imaging spectroradiometer (MODIS) data, we compared the differences in albedo, ET, and LST between cropland and grassland (CR-GR), and between planted forest and grassland (PF-GR) in the Horqin Sandy Land of Inner Mongolia, an agro-pastoral ecotone of northern China. Our main objective was to explore the magnitude and direction of albedo and ET change during the growing season and, subsequently, to estimate the biophysical effects on LST as a result of land use and land cover change. Our results indicate no significant difference in mean monthly albedo for CR-GR and PF-GR. Cropland lost more water through ET and significantly decreased daytime LST compared with grassland from July to September, but no significant differences in ET and LST were observed for PF-GR in any month. The biophysical climate effects were more pronounced for CR-GR compared with PF-GR. The response of LST to the changes in energy budget confirmed that ET was the critical driving factor relative to albedo. Compared with grassland, cropland and planted forest tended to cool the land surface by 5.15°C and 1.51°C during the growing season, respectively, because of the biophysical effects. Our findings suggest the significance of local-scale biophysical effect on climate variation after land use conversion in semi-arid regions.