Gurwinder Singh

Global navigation satellite systems (GNSSs) are widely utilized in various scientific domains such as agriculture, health monitoring, weather forecasting, and crop health detection. GNSS is a satellite network utilized in positioning and navigation including location-based services and precise orbit determination. The satellite navigation system offers various advantages such as real-time location and temporal synchronization. The Global Positioning System (GPS) is one of the well-known GNSSs, operated by the United States. Some of the other examples may include GPS; Galileo; Russia’s GLONASS, known as the Global Navigation Satellite System; and the BeiDou Navigation Satellite System (BDS). GNSS has found many applications in the field of precision agriculture, timing and synchronization in telecommunication, search and rescue operations, military and defense purposes, Earth observation, construction, and weather forecasting. However, GNSS has also been explored in the accurate and reliable positioning and timing information for climate change analysis and modeling. Therefore, it is essential to examine the potential of GNSS in climate monitoring and modeling. This chapter will present a review of GNSS types, GNSS remote sensing applications, and challenges faced by GNSS. Additionally, this chapter also explores the meta-analysis of the publications or research contributions made by different authors in the field of GNSSs.

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012–2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20–0.05%) and fallow land (1–0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping.

To plan production, the sowing, and harvesting of a particular crop, and the performance of marketing activities information about yields is important for both the traders and producers. In this study, various efforts have been made to extract critical information for agriculture land use classification areas using Sentinel-2 datasets, which was not possible with the help of multi-spectral datasets. As part of the current work, the artificial neural networks (ANN) classifier is combined with the post-classification comparison (PCC), thereby predicting seasonal variability from satellite imagery. The ANN classifier is incorporated into the post-classification comparison procedure, called ANN-based change detection. As part of the demonstration, the datasets were acquired using Sentinel-2 datasets during the period 2017 – 2018 over the agricultural land in Block Khamanon, District Fatehgarh Sahib, Punjab State, India. This process cross-validated the performance of ANN with a conventional maximum likelihood classifier (MLC) for confirmation. In comparison with the conventional PCC-MLC model (classified maps have an average of 86 – 88.8%, and change maps have an average of 83.6 – 84.2%), the PCC-ANN model achieved accuracy (classified maps have an average of 90.4 – 93.4%, and change maps have an average of 87.4 – 90%). In addition to identifying water surfaces, crop types, and man-made features, this study can also help in performing a wide range of land-use patterns.

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.