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.

Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years and food security has become a major issue. For a country like India, with a huge population to cater, the problem becomes more serious. Since the inception of remote sensing in scientific agriculture management, optical remote sensing along with field data has been used for soil health monitoring and mapping. SAR or microwave remote sensing has an all-weather and high temporal data availability which has found applications for various domains. For soil health studies of multiple soil classes and sub-classes of same type, both aerial and spaceborne SAR remote sensing is currently in use for a multitude of monitoring and parameter modelling approaches. This study utilizes Sentinel 1A, C-band SAR remote sensing data with VV and VH polarization channels for surface soil moisture estimation for alluvial soil and its sub-types in Rupnagar of Punjab state in India. While Index based OLS Regression method for soil moisture estimation was done using backscatter from Sentinel-1A SAR data, it was validated using Normalized Differential Moisture Index (NDMI) generated from Sentinel 2 optical datasets. This approach though did not consider the actual soil moisture data from field yet gave a low Root Mean Squared Error (RMSE) of 0.5 and R2-statistics of 0.72 (72%) in training and testing phases. The Index based OLS (Ordinary Least Squares) Regression method for soil moisture estimation aims to establish a technique for cases when field data is either not available or the study area is not easily accessible. In the statistical approach with field data, the same OLS model, when replaced by on-field surface soil moisture data gave a RMSE of 1.9 and R2-statistics of 0.968 and 0.948 in training and testing phases respectively at 97.5% confidence level. The study is significant for using freely available optical and SAR remote sensing data parameters synergistically, in a simplified manner for surface soil moisture estimation. The results have comparable accuracies given by studies using commercial data and complex modelling approaches.