The Ku-band (13.5 GHz) based scatterometer is the main sensor onboard Scatterometer Satellite (SCATSAT-1) launched on 26th September 2016 by Indian Space Research Organization (ISRO). The SCATSAT-1 satellite sensor provides daily updates on the conditions of atmospheric, oceanographic, agriculture and cryospheric parameters. Moreover, it delivers data products (Level 1‒4) in form of different parameters (Sigma-naught σ0, Gamma-naught γ0, brightness temperature BT, wind vectors and velocity) at two different polarization modes (HH and VV). Since launch, several studies have been carried out to explore the potential of SCATSAT-1 satellite sensor for remote observation of the ocean as well as the land surface at the global level. Besides the conventional applications in weather and oceanic domains which are based on wind vector data, emerging applications over land use and land cover are also introduced. This paper aims to address the current status of SCATSAT-1 applications in different scientific domains such as oceanographic, cryospheric, agriculture and land hydrology. It is expected that such an extensive exploration of the applications of SCATSAT-1 satellite sensor will provide important insights for future utilization of scatterometer data.

Crop yield prediction is one of the crucial components of agriculture that plays an important role in the decision-making process for sustainable agriculture. Remote sensing provides the most efficient and cost-effective solution for the measurement of important agricultural parameters such as soil moisture level, but retrieval of the soil moisture contents from coarse resolution datasets, especially microwave datasets, remains a challenging task. In the present work, a machine learning-based framework is proposed to generate the enhanced resolution soil moisture products, i.e., classified maps and change maps, using an optical-based moderate resolution imaging spectroradiometer (MODIS) and microwave-based scatterometer satellite (SCATSAT-1) datasets. In the proposed framework, nearest-neighbor-based image fusion (NNIF), artificial neural networks (ANN), and post-classification-based change detection (PCCD) have been integrated to generate thematic and change maps. To confirm the effectiveness of the proposed framework, random forest post-classification-based change detection (RFPCD) has also been implemented, and it is concluded that the proposed framework achieved better results (88.67–91.80%) as compared to the RFPCD (86.80–87.80%) in the computation of change maps with σ°-HH. This study is important in terms of crop yield prediction analysis via the delivery of enhanced-resolution soil moisture products under all weather conditions.