Md Moniruzzaman

The surface subsidence in the Krishna Godavari (KG) basin in India has increased with the discovery of crude oil and natural gas reserves since 1983. With private players coming up to bag the exploration and refining contracts, there must be timely monitoring of the surface subsidence of the region so that remedial measures for the resettlement of the populations can be taken promptly. Regular monitoring is necessary since the region is fertile and any seawater ingress results in the loss of valuable cultivable land. Multi-temporal SAR Interferometry (MTInSAR) technique has been applied successfully all over the world for the study and regular monitoring of land surface subsidence scenarios. This study utilizes data from Sentinel-1 C-band SAR sensor for MTInSAR-based surface subsidence and RADAR Vegetation Index (RVI)-based vegetation loss for the same season estimation between 2017 and 2022 for the KG basin region. It is inferred from the study that the region has shown surface subsidence of 80 mm/year between April 2020 and June 2022. This study uses support vector regressor (SVR) to predict the loss in forest cover in terms of RVI using MTInSAR-based surface subsidence, VH, and VV backscatter as parameters. It is observed that the SVR gave R²-statistics of 0.89 and 0.873 in the training and testing phases with a mean absolute error (MAE) and root mean squared error (RMSE) of 0.08 and 0.02, respectively. It is also observed that the region showed a loss of 3.21 km² of cultivable land between 2020 and 2022.

Mango is widely known as a popular fruit in South Asia, including Bangladesh. The country is a significant producer of different local and exotic varieties of mangoes in different geographic locations. Therefore, a study of fruit maturity at diverse locations and climatic conditions becomes critical for a sustainable mango production. In responding to this need, this study evaluates the variability of a few selected commercial mango (Mangifera indica L.) varieties and their maturity timeline with respect to spatial extent (longitudinal-latitudinal variations), elevation profile, and time. Remote sensing technology has been widely used for horticultural applications to study fruit phenology, maturity, harvesting time, and for mapping locational differences. In doing so, we have employed remotely sensed data, such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) of 30 m spatial resolution, GPM IMERGM precipitation datasets (0.1 × 0.1 degree), NASA GLDAS (Global Land Data Assimilation System) surface skin temperature (0.25 × 0.25 degree), and Noah Land Surface Model L4 3-hourly soil moisture content datasets (0.25 × 0.25 degree). Alongside these, an intensive field data collection campaign has been carried out for 2019 and 2020. It was found that 1° locational variations may result in approximately 2–5 days delay of mango harvesting. The outcome of this study may enhance the appropriate planning of harvesting, production, and the commercialization of mango selection in specific geographic setting for a sustainable harvest and production system in the country.

The Sentinel-1 is an active SAR satellite with a C-band SAR sensor operating at a center frequency of 5.405 GHz and 5.55 cm wavelength. With the availability of freely available Synthetic Aperture RADAR datasets from Sentinel-1, the Persistent Scatterer SAR Interferometry (PSInSAR) based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the GRACE satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface Line of Sight (LOS)displacement from Sentinel-1, with the average groundwater fluctuations from GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the R ² values to be 0.63 and 0.65 for VV and VH polarizations respectively with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of gravimetric anomaly due to decrease in groundwater level, was carried out for Varanasi city. The R ² , Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were observed to be 0.84, 1.23 and 2.4 respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.

Amid the COVID-19 crisis, governments all over the world, and not excluding India, took to lockdown measures to deaccelerate the spread of the COVID-19 virus. This led to reduction of atmospheric pollution by declining the harmful Nitrogen and Sulphur Oxide (NO_X and SO_X) concentrations. However, one hand while the stratospheric Ozone (O₃) showed repair, the lower atmospheric O₃ concentrations demonstrated a remarkable increase during lockdown phase over India. This study aims to estimate the O₃ concentration during the Covid-19 lockdown over Pune city in India using freely available Sentinel-5P satellite datasets. The study makes use of the Ordinary Least Squares (OLS) and Random Forest (RF) regressions and compares the findings of the two algorithms based on estimation results. This study utilizes lower atmospheric O₃ concentration data from Sentinel-5P satellite of the European Space Agency (ESA) over the Indian mainland for a month of lockdown scenario (March 22^nd, 2020, to April 25^th, 2020) and shows the remarkable increase in concentration of O₃ gas as a pollutant. Despite the complete lockdown over India during this given time frame, there has been enough emission of O₃ precursors from other sources such as stubble burning. The estimates of tropospheric O₃ concentration for May 2020 for Pune city, using OLS and RF Regressions, have been validated with May 2020 data. The results have provided a RMSE of 1.05 and 1.23 with R²-statistics of 0.90 and 0.857 in training and testing phases for OLS and RMSE of 0.98 and MAE of 1.07 with R²-statistics of 0.968 and 0.895 in training and testing phases of the RF. The outcome of this study has proven that O₃ gas concentrations in the atmosphere depends upon various other causative factors apart from the precursor gases. The study also shows that the remotely sensed Sentinel-5P datasets, supplemented with ground-based sensor data can help in time and cost saving estimation of O₃ concentrations in the troposphere with considerable accuracy.

The coronavirus pandemic (COVID-19) has impacted the usual global movement patterns, atmospheric pollutants, and climatic parameters. The current study sought to assess the impact of the COVID-19 lockdown on urban mobility, atmospheric pollutants, and Pakistan’s climate. For the air pollution assessment, total column ozone (O₃), sulphur dioxide (SO₂), and tropospheric column nitrogen dioxide (NO₂) data from the Ozone Monitoring Instrument (OMI), aerosol optical depth (AOD) data from the Multi-angle Imaging Spectroradiometer (MISR), and dust column mass density (PM_2.5) data from the MERRA-2 satellite were used. Furthermore, these datasets are linked to climatic parameters (temperature, precipitation, wind speed). The Kruskal–Wallis H test (KWt) is used to compare medians among k groups (k > 2), and the Wilcoxon signed-rank sum test (WRST) is for analyzing the differences between the medians of two datasets. To make the analysis more effective, and to justify that the variations in air quality parameters are due to the COVID-19 pandemic, a Generalized Linear Model (GLM) was used. The findings revealed that the limitations on human mobility have lowered emissions, which has improved the air quality in Pakistan. The results of the study showed that the climatic parameters (precipitation, T_max, T_min, and T_mean) have a positive correlation and wind speed has a negative correlation with NO₂ and AOD. This study found a significant decrease in air pollutants (NO₂, SO₂, O₃, AOD) of 30–40% in Pakistan during the strict lockdown period. In this duration, the highest drop of about 28% in NO₂ concentrations has been found in Karachi. Total column O₃ did not show any reduction during the strict lockdown, but a minor decline was depicted as 0.38% in Lahore and 0.55% in Islamabad during the loosening lockdown. During strict lockdown, AOD was reduced up to 23% in Islamabad and 14.46% in Lahore. The results of KWt and WRST evident that all the mobility indices are significant (p < 0.05) in nature. The GLM justified that restraining human activities during the lockdown has decreased anthropogenic emissions and, as a result, improved air quality, particularly in metropolitan areas.