Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields by Maria C. da S. Andrea, Cristiano F. de Oliveira, Fabrícia C. M. Mota, Rafael C. dos Santos, Edilson F. Rodrigues Junior, Lucas M. Bianchi, Rodrigo S. de Oliveira, Caio M. de Gouveia, Victor G. S. Barbosa, Marco A. Bispo E Silva

“…During 2022–2023 agricultural seasons, an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton, satellite imagery, soil texture, and phenology data. Subsequently, a SUF (based on historical data between 2020–2021 to 2022–2023 agricultural seasons) was developed to predict plant height using remote sensing and phenology data, aiming to replicate same zonal maps but without relying on direct field measurements of plant height. …”
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Spatiotemporal analysis of oil palm land clearing by M.K. Rosyidy, E. Frimawaty

“…This study utilizes the change of oil Palm in spatial-temporal (spatial and temporal) in Jambi province related to land change and environmental impacts.METHODS: This research uses data from Landsat 8 satellite imagery. The land cover classification was done using the Maximum Likelihood approach, while the overlay method was used for land change analysis. …”
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Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan by Tanweer Abbas, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam Baig, Irfan Ali, Hafiz Umar Farid, Muhammad Usman Ali

“…Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). …”
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Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. …”
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Modeling regional aboveground carbon stock dynamics affected by land use and land cover changes by A.D. Malik, M.C.W. Arief, S. Withaningsih, P. Parikesit

“…Vegetation biomass was assessed using an allometric equation, and aboveground carbon stock data were extrapolated to the landscape scale using a linear regression model of measured carbon stocks and the Normalized Difference Vegetation Index derived from recent satellite imagery.FINDINGS: Vegetated areas were predominant in 2009 and 2021. …”
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Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects by Aaron N. Schad, Molly K. Reif, Joseph H. Harwood, Christopher L. Macon, Lynde L. Dodd, Katie L. Vasquez, Kevin D. Philley, Glenn E. Dobson, Katie M. Steinmetz

“…Uncrewed aircraft system (UAS) technology can help fill data gaps between high-to-moderate spatial resolution (e.g., 1–30 m) satellite imagery, manned airborne data, and traditional field surveys, yet it has not been thoroughly evaluated in a virtual capacity as an alternative to traditional field vegetation plot surveys. …”
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Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets by K. R. Mankin, S. Mehan, T. R. Green, D. M. Barnard, D. M. Barnard

“…Gridded datasets built on ground-based observations (G; <span class="inline-formula"><i>n</i>=</span> 20), satellite imagery (S; <span class="inline-formula"><i>n</i>=</span> 20), and/or reanalysis products (R; <span class="inline-formula"><i>n</i>=</span> 23) are compiled and described, with focus on the characteristics that hydrologic investigators may find useful in discerning acceptable datasets (variables, coverage, resolution, accessibility, and latency). …”
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Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments by Jianqiang He, Yonglin Jia, Yi Li, Asim Biswas, Hao Feng, Qiang Yu, Shufang Wu, Guang Yang, Kadambot.H.M. Siddique

“…It introduces an innovative framework for assessing regional cotton crop suitability by integrating ground-measured soil water and salt data with UAV multispectral and Sentinel-2A satellite imagery from the 2022 cotton growing season. …”
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Agricultural Drought Assessment using Remote Sensing Data (Case study: Tuyserkan County) by Maedeh Malmir, Kamran Shayesteh, Iman Pazhouhan

“…The use of remote sensing and satellite imagery as effective tools for monitoring agricultural drought has gained significant attention from researchers. …”
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Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction by Jian Li, Kewen Shao, Jia Du, Kaishan Song, Weilin Yu, Zhengwei Liang, Weijian Zhang, Jie Qin, Kaizeng Zhuo, Cangming Zhang, Yu Han, Yiwei Zhang, Bingrun Sui

“…Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. …”
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The responses of vegetation water use efficiency to biomass density and CO2 balance in dryland of Central Asia during 21st century by Alphonse Kayiranga, Xi Chen, Xuexi Ma, Dative Ingabire, Tie Liu, Yaoming Li, Emeka Edwin Igboeli, Eldiiar Duulatov, Hubert Hirwa, Clement Nzabanita, Ping Hu

“…In this study, we examined the patterns and magnitude of WUE effects on desert vegetation aboveground biomass (AGB) and carbon stocks (C. stocks) and related fluxes by utilizing multiple streams of state-of-the-art multispectral satellite imagery and polynomial model across the temperate drylands of Central Asia (CA) from 2000 to 2023. …”
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Benchmarking data-driven inversion methods for the estimation of local CO₂ emissions from synthetic satellite images of XCO₂ and NO₂ by D. Santaren, J. Hakkarainen, G. Kuhlmann, E. Koene, F. Chevallier, I. Ialongo, H. Lindqvist, J. Nurmela, J. Tamminen, L. Amorós, D. Brunner, G. Broquet

“…To support the development of the operational processing of satellite imagery of the column-averaged CO<span class="inline-formula">₂</span> dry-air mole fraction (XCO<span class="inline-formula">₂</span>) and tropospheric-column NO<span class="inline-formula">₂</span>, this study evaluates <i>data-driven inversion methods</i>, i.e., computationally light inversion methods that directly process information from satellite images, local winds, and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. …”
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Measuring spatial - temporal of Yazd urban form using spatial metrics by saeed zanganeh

“…To achieve the main objective of the paper, measuring Yazd growth and expansion by spatial metrics, it has used remote sensing data and satellite imageries and ArcGIS software. The conclusion revealed that in four periods of study, complexity or irregularity of the urban patch shapes has increased, centrality or average distance of the dispersed parts to the city center has decreased, compactness or the number and area of patches their distance from each other has decreased, porosity or ratio of open space has increased and finally population density of city has decreased in a large amount. …”
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Impacts of Land Cover and Greenness Change on Soil Loss and Erosion Risk in Damota Area Districts, Southern Ethiopia by Mamush Masha, Teshome Yirgu, Mulugeta Debele

“…The RUSLE modeling was applied using satellite imageries, ASTER GDEM, rainfall, and soil data to estimate total annual soil loss for a 100 km2 hilly and highly populated area in Ethiopia. …”
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Assessment of groundwater potential zones using geospatial techniques in Mangalore Taluk, Dakshina Kannada District, Karnataka, India by Eka Putri Elsa, Hamidi Masyhuri, M Shet Suraj, S A Swapna

“…Groundwater availability in Mangalore taluk was divided based on its hydro geomorphologic conditions. Satellite imageries are used for preparing various thematic maps, viz. slope, drainage density, lineament, land use/cover, soil, rainfall, geology and geomorphology map, which were transformed to raster class data using the feature to raster converter tool in ArcGIS. …”
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Assesment of Vegetation Cover Status in Dry Lands of The Sudan Using Social and Terrestrial Data by Mohammed Hamed Mohammed, Suzan Abdelrahman Hamad, Hassan Elnour Adam

“…Quantitative data was collected using terrestrial inventory and satellite imageries. In terrestrial inventory, 22 ground control points (GCPs) were randomly registered using GPS in order to get general overview of the land cover of the study area. …”
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Predicting of Temporal-Spatial Sand Dunes Transition Caused by Marine Storms (Case Study: The Coast of Makran, Iran) by Soleiman PirouzZadeh, Mahmood Khosravi, Samad Fotohi

“…Then to determine the changes in the movement of sand dunes in the study area ranged from twenty-three years (1991-2014), satellite imageries from Landsat 7 and 8(ETM+ sensor) with 15 and 30 meters spatial resolution , was used. …”
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Trends in vegetation cover changes in Bonny area of the Niger Delta by A Adoki

“…There is a dearth of epiphytic bryophytes and lichens on the boles and branches of the trees. From satellite imageries of the area, it is evident that the landcover classes changed across the three epochs. …”
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Trends in vegetation cover changes in Bonny area of the Niger Delta by A Adoki

“…There is a dearth of epiphytic bryophytes and lichens on the boles and branches of the trees. From satellite imageries of the area, it is evident that the landcover classes changed across the three epochs. …”
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