Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

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

sj-jpg-3-cpc-10.1177_10556656221127549 - Supplemental material for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis

Supplemental material, sj-jpg-3-cpc-10.1177_10556656221127549 for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis by Vaibhav Sahni, Vishakha Grover and Shaveta Sood, Ashish Jain in The Cleft Palate-Craniofacial Journal</p

sj-jpg-2-cpc-10.1177_10556656221127549 - Supplemental material for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis

Supplemental material, sj-jpg-2-cpc-10.1177_10556656221127549 for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis by Vaibhav Sahni, Vishakha Grover and Shaveta Sood, Ashish Jain in The Cleft Palate-Craniofacial Journal</p

Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

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

sj-tiff-1-cpc-10.1177_10556656221127549 - Supplemental material for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis

Supplemental material, sj-tiff-1-cpc-10.1177_10556656221127549 for The Periodontal Status of Orofacial Cleft Patients: A Systematic Review and Meta-Analysis by Vaibhav Sahni, Vishakha Grover and Shaveta Sood, Ashish Jain in The Cleft Palate-Craniofacial Journal</p

Estimation and validation of standalone SCATSAT-1 derived snow cover area using different MODIS products

In the present work, the scatterometer satellite (SCATSAT-1) has been implemented and validated to provide the near-real-time estimation of snow cover area (SCA) in the Western Himalayas, India. The SCA derived from standalone SCATSAT-1 L4 (Level-4 India) products, i.e. sigma-nought (), and gamma-nought () has been validated with different MODIS products individually, i.e. MOD02 L1B (calibrated-radiances) derived NDSI (normalized difference snow index), MOD10A1 L3 (daily composite snow cover), and MOD10A2 L3 (8-Day composite snow cover). The experimental outcomes confirm the potential of SCATSAT-1 in estimating the SCA with respect to other MODIS products and also, suggested the utilization of the different MODIS products for referencing/validation in different scenarios. The findings of this paper suggest that SCATSAT-1 offers the near real-time mapping and monitoring of large-scale snow extent at the global level even under cloudy conditions