The Sentinel-5 Precursor (Sentinel-5P) is an Earth Observation satellite developed by the European Space Agency (ESA). The aim is primarily to fill the gap between the end of the ENVISAT mission and the proposed launch of Sentinel-5. The satellite has a single payload and carries aboard the TROPOMI sensor, which is a nadir viewing imaging spectrometer. The sensor measures electromagnetic spectral wavelengths between ultraviolet (UV) (270–320 nm) and short-wave infrared (SWIR) (2305–2385 nm) to carry out air pollution monitoring. The sensor measures both the top of the atmosphere (TOA) reflected solar radiation and radiation from Earth. The sensor has a push-broom (nonscanning) configuration with a swath of 2600 km on the ground. The TROPOMI sensor has a near pixel dimension of $7 \times 3.5$ km2 for all the spectral bands. The UV-1 and SWIR bands are, however, an exception to this, with pixel dimensions of $7 \times 28$ and $7 \times 7$ km2, respectively. The data products from the sensor are available at Level-1B and Level-2. Since its launch, the TROPOMI sensor has been used worldwide in several research works related to atmospheric pollution mapping, monitoring, and modeling. The TROPOMI sensor is being used to study atmospheric pollutant measurements, the impact of forest fires, and carbon sequestration. This article aims to present a concise review of the current status of TROPOMI sensor in terms of applications in the study of the atmosphere at country, state, and city levels across the globe. It is aimed that documentation on such applications would help researchers the world over to think and use its data further, for better study of global climate change and its impacts.

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.

This study utilizes surface displacement data from Persistent Scatterer SAR Interferometry (PSInSAR) of Sentinel-1 satellite and groundwater storage change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to understand land subsidence in the Chandigarh tri-city region. The satellite datasets are used along with the groundwater level data obtained from wells over the study area. Since the GRACE data are available at a much coarser spatial resolution of 1o by 1o, challenges remain in correlating the dataset with PSInSAR displacement that has been multi-looked at 14 m by 14 m resolution. Therefore, multiple sources of data (i.e., the monthly average of GRACE data, groundwater storage change and monthly average PSInSAR displacement per pixel, and interpolated groundwater level data from wells for 2017 to 2022) have been deployed into a deep learning multi-layer perceptron (DLMLP) model to estimate the groundwater storage change at the urban level. This has an indirect downscaling method that is carried out successfully using the DLMLP model for the estimation of groundwater storage changes at the urban level, which is usually complicated by applying direct downscaling methods on the GRACE data. Thus, the DLMLP model developed here is a distinctive approach considered for estimating the changes in groundwater storage using PSInSAR displacement, groundwater data from wells, and GRACE data. The DLMLP model gives an R²-statistics value of 0.91 and 0.89 in the training and testing phases, respectively, and has a mean absolute error (MAE) of 1.23 and root mean square error (RMSE) of 0.87.

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.