Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery.

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.

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis.