Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard

Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002-2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of - 0.80, - 0.58, - 0.43, and - 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of - 0.68, - 0.23, and - 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA-Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.

Visible, near-infrared dual-polarization lidar based on polarization cameras: system design, evaluation and atmospheric measurements

A visible, near-infrared (VIS-NIR) dual-polarization lidar technique employing laser diodes and polarization cameras has been designed and implemented for all-day unattended field measurements of atmospheric aerosols. The linear volume depolarization ratios (LVDR) and the offset angles can be retrieved from four-directional polarized backscattering signals at wavelengths of 458 nm and 808 nm without additional optical components and sophisticated system adjustments. Evaluations on the polarization crosstalk of the polarization camera and the offset angle have been performed in detail. A rotating linear polarizer (RLP) method based on the Stokes-Mueller formalism has been proposed and demonstrated for measuring extinction ratios of the polarization camera, which can be used to eliminate the polarization crosstalk between different polarization signals. The offset angles can be online measured with a precision of 0.1°, leading to negligible measurement errors on the LVDR. One-month statistical analysis revealed a small temporal variation of the offset angles, namely -0.13°±0.07° at 458 nm and 0.33°±0.09° at 808 nm, indicating good system stability for long-term measurement. Atmospheric measurements have been carried out to verify the system performance and investigate aerosol optical properties. The spectral characteristics of the aerosol extinction coefficient, the color ratio, the linear particle polarization ratio (LPDR) and the ratio of LPDR were retrieved and evaluated based on one-month continuous atmospheric measurements, from which different types of aerosols can be classified. The promising results showed great potential of employing the VIS-NIR dual-polarization lidar in characterizing aerosol optical properties, discriminating aerosol types and analyzing long-range aerosol transportation.