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Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts. [ABSTRACT FROM AUTHOR]

Monthly night-time light (NTL) data can be very useful for studying intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines started providing monthly NTL imagery for free since 2021; based on Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data. In the current study, an attempt has been made to produce an improved monthly DMSP product from 1992 to 2013. The monthly DMSP product suffers from several drawbacks such as spatial inconsistency, random fluctuations in the data for consecutive years, pixel saturation in bright cores, and blooming effect around settlements. As a result, geometric errors, background noise, and radiometric errors persist in the monthly DMSP images. This research tries to address each of these errors by applying various techniques to produce a vigorously tested and improved monthly DMSP dataset consisting of 216 images (1992–2013). Automatic intensity-based registration has been used to register the monthly images to their corresponding annual composite stable light image. Thresholding and land cover data have been used to remove the background noise. Finally, ridgeline regression is implemented considering F14 December 2003 as the reference image. Each technique has been verified by qualitative analysis of the intermediate outputs. The quantitative assessments of the improved monthly DMSP product reveal that it is a good proxy measure when split from 1992 to 2002 and 2003–2013 with an R2 of 0.84 & 0.82 w.r.t GDP and 0.83 & 0.80 w.r.t population in contrast to the original DMSP data with R2 of 0.70 & 0.75 w.r.t GDP and 0.73 and 0.80 w.r.t population. Transect analysis across six urban cities verifies that the improved product shows reduced saturation in urban areas and increased contrast in sub-urban regions. Finally, a consistent monthly DMSP product is delivered to the research community which will be useful for studying time-series intra-year changes in urbanization, economic growth, and other socio-economic dynamics. [ABSTRACT FROM AUTHOR]

The night-time light (NTL) data from Visible Infrared Imaging Radiometer Suite's Day/Night Band (VIIRS-DNB) onboard Suomi National Polar-Orbiting Partnership (S-NPP) spacecraft available since April, 2012 have successfully addressed some of the limitations of Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), owing to improved radiometric and spatial resolutions and onboard calibration. Earlier, DMSP-OLS NTL images majorly contributed for understanding the spatio-temporal dynamics of socio-economic factors such as Gross Domestic Product (GDP) and Electric Power Consumption (EPC), but the problem of saturated pixels and blooming effect affected the correlation with sum of lights (SOL). In the present study, we evaluate different methods to generate the annual VIIRS-DNB NTL composites from available monthly products for estimating EPC at different scales, and compare their performance with DMSP-OLS annual composites. The VIIRS-DNB annual NTL composites are prepared using various statistical measures, seasonal decomposition and principal component analysis (PCA). The SOL derived from these composites were analysed with respect to EPC at Zonal and Electricity Distribution Company (DISCOM) levels in the Uttar Pradesh State of India to identify the most appropriate annual representation of NTL from VIIRS-DNB. It is found that the annual composites prepared using PCA method provide the best results at both Zonal and DISCOM levels. Further, VIIRS-DNB annual NTL composites perform better than the DMSP-OLS composites. At the Zonal level, some of the highly populated and fully electrified Zones exhibit a distinct SOL-EPC relationship, attributable to enhanced power consumption by indoor equipment and industrial activities, which is not captured by satellite. Finally, empirical EPC prediction models are developed based on time-series VIIRS-DNB data from 2013 to 2017, which will help understanding the EPC patterns at regional level in the State of Uttar Pradesh. [ABSTRACT FROM AUTHOR]

Digital elevation model (DEM) provides elevation information in raster format for an area which help in analysis as these phenomena are gravity depended. Hydrological study requires creation of drainage network map. DEM is the primary input for this process. Generally 6 or more processes are required to be completed to get the drainage network. These are available in form of hydrology tools in the ArcGIS software. These processes are manual and time consuming which increases the chances of human error. An automated process is constructed in this paper which aims to create a tool that can handle multiple files in an intelligent manner. The input DEM plays a significant role in hydrological studies. This study attempts to do a comparative study to analyse the variation in the drainage network and the intermediate products with the change in the input DEM. A tool is created using ArcPy site package in Python programming language to integrate all required hydrology tools. The script is then used to create a tool in ArcGIS 10 which takes location as an input parameter and perform the process on all the DEM files inside the directory. The tool creates separate directory for every DEM file and thus reduces the chances of file mismanagement. The proposed tool is tested on two different datasets namely ASTER GDEM and Cartosat DEM. The tool runs efficiently on both the datasets and thus provides results to compare the drainage pattern produced by these different data sources. [ABSTRACT FROM AUTHOR]