Punjab Remote Sensing Centre

Pectinophora gossypiella is a key pest of cotton responsible for causing huge economic loss to the cotton grower in India. After two decades of Bt cotton introduction, an outbreak of pink bollworm recorded during 2021 in Punjab. Insecticides are largely used to control pest infestation, In this regard, 21 insecticides recommended by Central Insecticide Board (CIB) including conventional and new generation insecticides were evaluated during 2020-2022. Spraying was initiated at reproductive stage of cotton (100 DAS) when pink bollworm cross 5% ETH level at an interval of 7-15 days. Emamectin benzoate 5SG (Proclaim) @ 100gm, fanpropathrin 10 EC (danitol) @ 300 ml, profenophos 50EC (curacron) @ 500 ml, spinetoram 11.7SC (delegate) @ 170 ml, indoxacarb 15SC (kingdoxa) @ 200 ml, chlorpyriphos 20EC (radar) @ 500 ml and cypermethrin 25EC (cyperguard) @ 80 ml were found most effective in reducing the pink bollworm. Among these, emamectin benzoate 5SG, indoxacarb 15SC, profenophos 50EC and spinetoram 11.7SC recorded significantly lower boll (5.92± 0.10, 4.67± 0.10) and locule damage (7.11± 0.42, 5.00± 0.32) at the time of harvesting along with higher seed cotton yield (21.58, 21.95 qtl/ha), respectively. Similarly, profenophos 50EC, emamectin benzoate 5SG and fanpropathrin 10EC cause more than 70 % larval mortality under laboratory conditions.

Fire is an Essential Climatic Variable (ECV) that helps understand the Earth’s ecosystem. Human-induced fire events, such as crop residue burning, have exacerbated air pollution, while forest fires have caused biodiversity loss. Considering the rising concerns around fire-induced environmental damage, detailed mapping and analysis of fire regimes have become necessary. The study analysed the mapping of fire regimes and spatio-temporal fire patterns. The k-means algorithm has been used to cluster the fire regimes based on various fire regime variables. Six distinct fire regimes have been identified, and statistical analyses have been conducted to observe fire trends over time. The study revealed more than 88% of the land has been affected by vegetation fires. Cropland burning has accounted for most fire events, particularly in the northern Indo-Gangetic plains, where agricultural residue burning has become prevalent. The results show more than 48% of the area is affected by cropland burning.

The increased demand, driven by a growing population and changing dietary habits, has created immense pressure on agroecosystems throughout the globe. In semi-arid areas, this increased pressure has led to excessive use of agrochemicals in agriculture, which poses a threat of potentially toxic metal (PTM) contamination in agricultural soils. PTMs in agricultural soils result in significant environmental and health threats for humans and livestock. The current study aimed to evaluate the risks associated with five PTMs-nickel (Ni), lead (Pb), chromium (Cr), cadmium (Cd), and cobalt (Co)-in agricultural soils of the semi-arid South Punjab region of India. The study area included sites from district Bathinda of Punjab (3385 km2). Among the PTMs analyzed, the concentration of Cd (0.65-1.85mg/kg) in several agricultural soil samples was above the international permissible limits (1.0mg/kg). Principal component analysis (PCA) pointed to parent rock materials as the main sources for Cr, Co, Ni, and Pb, while for Cd, agricultural sources (phosphatic fertilizers) can be a prominent contributor. The values observed for Pollution Load Index (PLI: 0.93-1.64) and Ecological Risk Index (RI: 206.66-584.03) indicated that soil samples were significantly contaminated with the tested PTMs, posing considerable ecological risks. Analysis of other physicochemical characteristics showed that soil samples were alkaline and saline, with low levels of soil organic matter. The human health risk assessment pointed to minimal non-carcinogenic (NCR) and carcinogenic risks (CR) associated with the presence of PTMs in the soil. Spatial distribution analysis revealed a higher level of metallic contamination in the eastern part of the district. Thus, adequate steps must be taken to control the increase in levels of these PTMs in the soils of the study area.

This study demonstrate the suitability of an Active-Passive algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to generate high resolution (at sub-km) soil moisture products over India. This algorithm utilize the high resolution ($\sim 25 \mathrm{~m}$) SAR data from ISRO’s EOS-04 C-band SAR mission and coarse ($\sim 9 \mathrm{~km}$) L-band brightness temperature from NASA’s SMAP to generate high resolution ($\sim 500 \mathrm{~m}$) soil moisture product over Indian regions for agricultural applications, especially for irrigation scheduling, crop water demand and crop stress management etc. In this work, we adopted and modified the Active-Passive (snapshot approach) soil moisture algorithm to evaluate its effectiveness at sub-kilometer scale ($\sim 500 \mathrm{~m}$). The high resolution soil moisture ($\sim 500 \mathrm{~m}$) have been validated over multiple dates, covering Kharif and Rabi seasons with multiple crop conditions over selected site. A good agreement was observed with in-situ datasets with ubRMSE, ranging from 0.039 to $0.06 \mathrm{~m} 3 / \mathrm{m} 3$ as an accuracy metric which demonstrated the potential of an Active-Passive algorithm for soil moisture retrieval using NISAR data at sub-km grid with required accuracy, in order to meet the agricultural application requirements.

Monitoring the health of crops is essential for estimating the effects of climatic variability and biotic and abiotic stressors for the agricultural state of Punjab. Large-scale, continuous information about vegetation can be obtained using satellite remote sensing. Mapping a state's crop stress is a difficult task that requires a lot of resources. This study uses the computational capability of the Google Earth Engine (GEE) platform to map crop stress in the state of Punjab using the enhanced vegetation index (EVI) dataset from the MOD13A1.006 Terra Vegetation collection. The deviation of the EVI of the current (2019–2020) period from the reference period (2014–2019) has been analyzed to classify the crop stress as severe, high, no-change, and improved. The results show that the Gurdaspur district experienced maximum crop stress in the rabi cropping season of 2019–2020. Amritsar, Patiala, Pathankot, and Fatehgarh Sahib followed the Gurdaspur district. The least stress was identified in the Patiala, Nawan Shehar, and SAS Nagar (Mohali) districts.

With the advances in new technologies, there has been an increasing interest in the application of Building Information Modelling (BIM). BIM is a process that involves creating and managing digital representations of physical and functional characteristics of a building or infrastructure. Understanding the concept of BIM involves recognizing it as a digital approach to designing, constructing, and managing buildings and infrastructure. This paper presents a comprehensive overview of BIM, exploring its fundamental concepts, evolution, and diverse applications. It begins with an examination of the core concept underlying what actually is BIM, including its data-centric approach and collaborative framework. The review then delves into the various applications of BIM across different stages of a building project, from conceptual design to construction and facility management. Understanding the concept of BIM involves grasping its fundamental principles, components and how it revolutionizes the AEC industry. By synthesizing current research and industry practices, this review aims to provide a holistic understanding of BIM’s role in advancing modern construction practices and its potential for future development.