The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Drought is an intricate atmospheric phenomenon with the greatest impacts on food security and agriculture in South Asia. Timely and appropriate forecasting of drought is vital in reducing its negative impacts. This study intended to explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002–2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh. The spatial maps were generated against each index, which indicated a severe agricultural drought during the year 2002, compared to the other years. The results showed that the southeast region of Pakistan, and the north, northwest, and southwest regions of India and Afghanistan were significantly affected by drought. However, Bangladesh faced substantial drought in the northeast and northwest regions during the drought year (2002). The longest drought period of seven months was observed in India followed by Pakistan and Afghanistan with six months, while, only three months were perceived in Bangladesh. The correlation between drought indices and climate variables such as soil moisture has remained a significant drought-initiating variable. Furthermore, this study confirmed that the evaporative stress index (ESI) is a good agricultural drought indicator, being quick and with greater sensitivity, and thus advantageous compared to the VHI, EVI, and SAI vegetation indices.

Developments in mid-infrared Differential Absorption Lidar (DIAL), for gas remote sensing, have received a significant amount of research in recent years. In this paper, a high repetition rate tunable mid-infrared DIAL, mounted on a mobile platform, has been built for long range remote detection of gas plumes. The lidar uses a solid-state tunable optical parametric oscillator laser, which can emit laser pulse with repetition rate of 500 Hz and between the band from 2.5 μm to 4 μm. A monitoring channel has been used to record the laser energy in real-time and correct signals. Convolution correction technology has also been incorporated to choose the laser wavelengths. Taking NO₂ and SO₂ as examples, lidar system calibration experiment and open field observation experiment have been carried out. The observation results show that the minimum detection sensitivity of NO₂ and SO₂ can reach 0.07 mg/m³, and 0.31 mg/m³, respectively. The effective temporal resolution can reach second level for the high repetition rate of the laser, which demonstrates that the system can be used for the real-time remote sensing of atmospheric pollution gas.

Atmospheric carbon dioxide (CO₂) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO₂ is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO₂ emissions. Satellites provide the most effective way to monitor the concentration of CO₂ in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO₂, i.e., XCO₂ retrieved from a CO₂ monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO₂ emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO₂, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO₂ emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO₂ anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO₂ concentrations and seasonal variability. The XCO₂ anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO₂ emissions were estimated for 2019 based on the NPP and XCO₂ anomalies derived for the same year. The estimated and the ODIAC CO₂ emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO₂ emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO₂ anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO₂ retrievals can be used to estimate the regional-scale anthropogenic CO₂ emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO₂ emission and concentration datasets.

Carbon dioxide (CO₂) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO₂ at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO₂ were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO₂ retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO₂. The profiles captured a decrease in CO₂ concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO₂ when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO₂ mole fractions (XCO₂) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO₂ derived from in situ measurements.

Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO₂) emissions. For a better understanding of CO₂ variation trends over Asia, in this study, the column-averaged CO₂ dry air mole fraction (XCO₂) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO₂ from GOSAT is higher than the XCO₂ simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO₂ is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO₂ can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales.