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The response of a long-period fiber grating (LPFG) based refractometric sensor is directly related to the optical modes' effective index of refraction (EIR). In this paper, the quantitative behavior of EIR is investigated with LPFG surrounded dielectric medium's refractive index (RI) higher than cladding RI and cladding radius. To analyze the impact on EIR, the 2-layer and 3-layer fiber structure-based mathematical models have been introduced. In the past literature, the interaction of the ambient medium with the core mode field is rarely investigated. Therefore, such studies have been considered and as a result, a significant impact on core mode EIR has been observed. The influence of ambient-medium RI (ARI = 1.458-1.738) on EIR is found ascended for its value near to cladding RI at normal cladding radius. However, the range of influencing ARIs has been extended with mode order and cladding radius reduction. This study encourages the measuring of LPFG RI response for the aforesaid ARI range in terms of shift in coupled mode resonance wavelength and improves the precision in sensing ARI.

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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.