Remote Sensing Commons^™

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …

Geospatial Investigations Of Big Buckhead Cemetery, Millen, Ga, Audrey E. Popard

College of Graduate Studies: Theses & Dissertations

Geospatial investigations of burials are increasingly recognized as the most efficient and ethical means of determining grave locations in forensic and bioarchaeological research. A methodology of multi-tiered geospatial investigation has been applied to the Big Buckhead Baptist Church cemetery in Millen, GA. Using the systematic layering of geospatial technologies, the present study seeks to identify ground surface anomalies, with the objective of delineating known and potential unknown burial locations. It is hypothesized that the layered use of Light Detection and Ranging (LiDAR), Geographic Information Systems (GIS), and Ground Penetrating Radar (GPR), will allow for the most efficient and accurate demarcation …

Datums And Benchmarks From Sylvester Manor Umass Boston Archaeological Work, Shelter Island, Ny, John M. Steinberg, John Schoenfelder, Chiara M. Torrini, Joseph E. Kinney, Stephen A. Mrozowski, David B. Landon

Data and Datasets

Datums from Sylvester Manor Archaeological work (2019-2026).  Includes zipped shapefile of points and complementary csv, that includes the site areas for each datum.

High-Resolution Mapping Of Soil Moisture Variation Using Uas Thermal And Multispectral Imagery, Jackline Amma Timah

Theses and Dissertations

In agricultural landscapes, soil moisture regulates hydrologic partitioning, nutrient transport and water quality, land-atmosphere energy exchange that shapes local climate, and ecosystem resilience. However, traditional monitoring approaches, such as in-situ sensors and satellite imagery, often lack the spatial resolution required to capture fine-scale soil moisture variability. This study evaluated whether unmanned aerial system (UAS)-derived thermal, multispectral, and terrain variables can capture fine-scale spatial variability in volumetric water content (VWC) within an SRB in central Illinois.

High-resolution imagery was collected and paired with 50 field-measured VWC observations. Land surface temperature (LST), vegetation indices (NDVI and NDRE), spectral bands, and slope were …

Geospatial Analysis Of Wildfire Ignitions And Proximity To Electric Transmission Lines In Arizona’S National Forests, Shane Ishmael, Ronny Schroeder

Student Works

The number of wildfires in Arizona rose by 18% from 2023 to 2024. Wildfires hit the Western United States hard, especially in states like Arizona and California, where vast national forests often fall victim to the biggest blazes. According to the Western Fire Chiefs Association, 19% of wildfires from 2016 to 2020 were sparked by electrical transmission lines.

This study explores whether wildfire start-location hotspots line up with power transmission routes running through Arizona’s Coconino and Tonto National Forests. The main hypothesis is that areas near power lines are more likely to become wildfire hotspots than other regions.

We used …

Integrated Geospatial Analysis Of Burn Severity And Vegetation Recovery Of The California August Complex Fire In 2020, Dharm Barot, Ronny Schroeder, Elise Anderson

Student Works

Large wildfires increasingly alter vegetation structure and ecosystem recovery trajectories at landscape scales, requiring reliable geospatial methods for post-fire assessment. This study evaluates burn severity and vegetation recovery following the 2020 California August Complex Fire using an integrated framework combining multispectral satellite imagery, spatial statistics, and airborne LiDAR data.

Burn severity was quantified using differenced Normalized Burn Ratio (dNBR), and vegetation recovery was assessed through a multi-temporal NBR time series spanning pre-fire (2015), fire-year (2020), and post-fire (2025) conditions. Optimized Hotspot Analysis (Gi*) was applied to isolate statistically significant clusters of high burn severity and reduce bias in recovery estimates. …

Validating Uas-Based Ndvi Data With Satellite Landsat Imagery For Bald Eagle Habitat Prediction In The Del Rio Springs Ecosystem, Noah Morales, Colton Weeks, Hank Vincent, Ronny Schroeder

Student Works

Vegetation health is commonly assessed using the Normalized Difference Vegetation Index (NDVI), which can be derived from multispectral sensors operating at different spatial resolutions. Validating NDVI products across sensor platforms is essential to determine their reliability for environmental monitoring and habitat assessment. This research compares NDVI derived from moderate-resolution satellite imagery and high-resolution unmanned aircraft system (UAS) imagery collected over the same study area. Landsat imagery, provided through the joint USGS–NASA mission, was used to represent satellite-based vegetation patterns, while high-resolution multispectral data were acquired using a MicaSense sensor mounted on a UAS to capture fine-scale vegetation detail.

NDVI values …

Integrated Geospatial Analysis Of Burn Severity And Vegetation Recovery Of The California August Complex Fire In 2020, Dharm Barot, Ronny Schroeder

Student Works

Large wildfires increasingly alter vegetation structure and ecosystem recovery trajectories at landscape scales, requiring reliable geospatial methods for post-fire assessment. This study evaluates burn severity and vegetation recovery following the 2020 California August Complex Fire using an integrated framework combining multispectral satellite imagery, spatial statistics, and airborne LiDAR data.

Burn severity was quantified using differenced Normalized Burn Ratio (dNBR), and vegetation recovery was assessed through a multi-temporal NBR time series spanning pre-fire (2015), fire-year (2020), and post-fire (2025) conditions. Optimized Hotspot Analysis (Gi*) was applied to isolate statistically significant clusters of high burn severity and reduce bias in recovery estimates. …

Precision Rockslide Hazard Mapping With Multispectral Imaging And Lidar Along Arizona Highway 89a, Hank Warner, Ronny Schroeder

Student Works

Along mountainous roads, rockslides, mud slides and avalanches pose a significant risk for continued access to a region and can cause large amounts of damage to infrastructure, taking time to clear and repair. The prediction of where these events will occur can allow preventative measures to be taken, allowing sustained access and preventing costly repairs.

This study develops a method to analyze and predict rockslide risk using satellite-sourced multispectral imagery and airborne LiDAR data.

The developed method started with multispectral LANDSAT 8 imagery and airborne LiDAR captures over Arizona Highway 89A, with all data taken between late August and early …

Submesoscale Dynamics Of Phytoplankton And Carbon Export Revealed By High-Resolution Airborne And Satellite Remote Sensing Of Currents And Ocean Color, Sarah E. Lang

Open Access Dissertations

Satellites and airborne sensors reveal submesoscale (1 - 10 km) variability in ocean color in the form of filaments, eddies, and patches. The variability in ocean color is closely tied to the physical dynamics that restructure phytoplankton distributions and drive active biological responses like changes in primary productivity and community structure. As the base of the marine food web and a key component of the biological carbon pump, phytoplankton are crucial to the overall health of marine ecosystems and to the ocean's role in climate. This dissertation focuses on the use of airborne and satellite remote sensing to study the …

Using Remote Sensing Technology To Develop A Framework For Improving Hydrologic Models, Marissa Cook

Theses, Dissertations and Capstones

With increased storm intensity due to climate change and urbanization, flash flooding has become an increasingly significant issue globally and regionally. Although the factors influencing urban flash flooding are well-known, there is a growing need for technology to accurately and remotely predict the chance of a flash flood occurring from any given rain event to give people time to prepare. This study aims to use multispectral satellite imagery to provide a framework for improving near real-time flood predictions in an urban area of a high gradient, fourth order stream impacted by flooding. Specifically, we utilize satellite imagery to create the …

Data-Driven Methodologies For Mapping Cultural Heritage: The Case Of The National Coal Heritage Area, West Virginia, Usa, Hossain Mohammad Nahyan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The objective of this dissertation was to develop a comprehensive, data-driven spatial framework for characterizing the complex cultural landscape of the National Coal Heritage Area (NCHA) in West Virginia. By transitioning away from traditional, heuristic spatial mapping, this research integrates advanced spatial statistics, machine learning, and GIS-based methodologies to objectively quantify the physical, visual, and cultural dimensions of the post-mining environment. The research is structured around three interconnected empirical studies, each addressing a specific scale of the Landscape Character Assessment (LCA) framework to support heritage conservation and sustainable spatial planning. The first paper focused on landform classification, developing an automated …

Validating Uas Lidar With Airborne Lidar For Precision Streamline Generation In Del Rio Springs, Arizona, Brad Rudy, Colton Weeks, Hank Vincent, Ronny Schroeder

Student Works

Accurate streamline delineation and high-resolution topographic products are essential across numerous disciplines, including hydrological analysis, environmental monitoring, construction, and erosion modeling. Products derived from high-accuracy elevation data provide greater reliability and improved decision-making outcomes for all fields that depend on them. A 2018 USGS airborne LiDAR dataset covering the Del Rio Springs riparian area north of Chino Valley, Arizona, offers a valuable opportunity to evaluate the relative accuracy of the DJI L1 LiDAR sensor when mounted on a Matrice 300 RTK UAV platform. Compared to traditional manned airborne systems, the UAV-mounted L1 provides high-accuracy, high-density point cloud data over small …

Spatiotemporal Assessment Of Coastal Urban Heat In Buenos Aires Using Satellite Landsat Lst, Noah Morales, Lleyton Naar, Dan Macchiarella, Kevin Adkins, Ronny Schroeder

Student Works

Coastal urban environments exhibit complex surface temperature patterns driven by interactions among water, vegetation, and built infrastructure. This study investigates land surface temperature (LST) variability along a coastal-to-urban transect in Parque de los Niños, Buenos Aires, by integrating multi-year satellite Landsat LST with high-resolution thermal data collected from an uncrewed aircraft system (UAS). Landsat provides the temporal depth necessary to assess seasonal and interannual variability in surface temperature, including responses to extreme summer conditions. However, their spatial resolution limits the ability to resolve fine-scale thermal gradients near shoreline boundaries and within heterogeneous urban landscapes. UAS thermal observations address this limitation …

Regional Oceanographic Controls On Water Column Nitrogen Fixation In Northern Australian Waters, Douglas G. Capone, Ajit Subramaniam, Yubin Raut, Joseph P. Montoya, Margaret R. Mulholland, Rachel Ann Foster, Miles Furnas, Edward J. Carpenter

OES Faculty Publications

Large blooms of the diazotrophic cyanobacteria, Trichodesmium, have been re-ported along the north coast of Australia and are readily evident in remote sensing images. During a research cruise in November 1999, we sampled from Townsville to Broome, examined Trichodesmium population densities and their rates of carbon and N₂ fixation alongside microscopy-based cell counts of them and other cyanobacte-rial diazotrophs. Additionally, we also enumerated the picophytoplankton community using flow-cytometry, measured bulk chlorophyll concentrations, carbon and N₂ fixation rates, and water column hydrography, enabling comparison of diazotrophic and picophytoplankton functional groups across the system. Agglomerative hierarchical clustering analysis of physicochemical oceanographic …

Climate Change In Gilgit-Baltistan: Satellite-Based Land Use/Land Cover Change Detection, Socio-Economic Dimensions, And Adaptation Strategies, Ali Muhammad

Graduate Theses/Dissertations

Climate change is increasingly transforming the cryosphere, hydrology, and human landscape of Gilgit-Baltistan, a highly climate-sensitive mountain region in northern Pakistan. This thesis investigates these transformations through satellite-based land use/land cover (LULC) change detection in four representative tehsils of Gilgit-Baltistan—Ali Abad, Gilgit, Nagar, and Sikander Abad—selected to span a gradient of human pressure and cryospheric exposure within the region. Using summer, cloud-free (< 10%) imagery from USGS Landsat 7 (2000) and Landsat 8 (2025), it conducts a multi-temporal comparison of environmental and socio-spatial change. After atmospheric correction and band compositing, the imagery is classified in ArcGIS Pro into six classes—Water, Barren land, Vegetation, Snow, Glacier, and Built-up—using a Support Vector Machine (SVM) classifier, with Maximum Likelihood Classification and Random Forest also tested but found less suitable for the final workflow. The analysis detects a pronounced reduction in mapped glacier-class area alongside a comparatively stable snow class, together with built-up expansion, while examining how temperature, precipitation, tourism, and population dynamics relate to observed LULC transitions. The results reveal a pattern of cryospheric decline and urban growth, with implications for water availability, ecological stability, hazard exposure, and settlement pressure. By integrating geospatial change detection with climatic and socio-economic interpretation, the thesis moves beyond mapping to explain interacting environmental and human drivers of landscape transformation and provides a reproducible remote-sensing baseline for monitoring land-surface change in heterogeneous mountainous terrain. It recommends integrated water-resource management, climate-resilient land-use planning, watershed and glacier monitoring, and sustainable tourism governance, supporting evidence-based decision-making by the Government of Gilgit-Baltistan and organizations working on climate adaptation, disaster risk reduction, and sustainable regional development.

Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher …

Marooned: A Western Frontier Dismal Swamp Narrative, Professor Meya E. Hargett

The Scholarship Without Borders Journal

Marooned: A Western Frontier Dismal Swamp Narrative centers the erased figure of Samuel Mars, a lost Black station master whose movements through the Great Dismal Swamp have been excluded from dominant Underground Railroad cartographies. Here, the term “Marooned” refers not to abandonment, but to the maroon geographies of Black and Indigenous resistance encoded in land, lineage, and fugitive infrastructure.

Guided by Diasporic Maroon Memory Theory, Lineage as Method, and the Maroon Geographies Framework, this research reconstructs Mars’s trajectory through oral testimony, spatial patterning, and ancestral land memory. Rather than casting the Dismal Swamp merely as a site of refuge, this …

Towards Spatial Inversion Of Aerial Gamma-Ray Survey Data For Measurement Of Naturally Occurring Radioactivity, Daniel A. Haber

UNLV Theses, Dissertations, Professional Papers, and Capstones

Aerial gamma-ray surveying (AGRS) is used in geologic and environmental contexts to provide data on the surface spatial distribution of naturally occurring radioactive material (NORM) or other gamma-ray-emitting isotopes. The standard data reduction workflow for AGRS data involves a process whereby pointwise gamma-ray spectral data are denoised, corrected for numerous sources of background radiation and aircraft height, and are finally converted to physical values by empirical conversion factors. The reduced pointwise data are then typically spatially interpolated to form a continuous surface map.

This dissertation introduces and explores a novel spatial inversion method for reduced AGRS data that considers aircraft …

Unmixing In Very High Spatial Resolution Hyperspectral Images, Ana C. Chavez Lopez

Open Access Theses & Dissertations

Hyperspectral Imaging (HSI) captures hundreds of contiguous narrow wavelength bands across the optical region of the electromagnetic spectrum collecting the spectral signature of materials in the field of view of the sensor enabling detailed analysis of each pixel's spectral signature. Satellite or airborne remote sensing systems often capture imagery with low to moderate spatial resolution (LMSR). At these resolutions, the measured spectral signature is a mixture of the signatures of the materials within a single pixel. This mixing of spectral information makes analysis and material identification difficult. Hyperspectral unmixing is an analysis technique that decomposes a pixel's spectrum into constituent …

Rainfall-Runoff Modelling In An Indonesian Humid Tropical Area Using Satellite-Based Precipitation Products, Noordiah Helda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation explores three rainfall-runoff models in the humid tropical regions of Indonesia using satellite-based precipitation products (SBPPs) and develops integrated machine-learning modeling frameworks. Several ground-based observations from BMKG (Badan Meteorologi, Klimatologi, dan Geofisika (also known as the Indonesian Agency for Meteorology, Climatology, and Geophysics)) stations across Indonesia (133−165 stations) are compared and evaluated against satellite products, indicating that GPM performs well, with R-squared values ranging from 0.54 to 0.76 and correlation coefficients ranging from 0.45 to 0.69, respectively.

In the Martapura Watershed, South Kalimantan, due to a lack of observational discharge data, streamflow was generated using the FJ Mock …

Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation developed disturbance detection models to fulfill the need for remote sensing landcover products describing grassland structure. The use of landcover products derived from remote sensing is increasing over time in pheasant (Phasianus colchicus) research. Such landcover, however, does not provide relevant pheasant structural habitat information (Chapter 1). Pheasants require tall, high-density grassland for nesting, tall grassland with medium density for brood rearing, and tall grassland for wintering. Time since disturbance can serve as a proxy for structure, as it shapes vegetation by removing biomass and resetting succession. Disturbance is easier to detect than structure with current …

Advancing Precision Agriculture Through The Application Of Remote Sensing Technologies In Plant Health Assessment, Thomas Wilbur Davis

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Monitoring plant health, including nutritional status assessment, is an important component of crop management decisions. This doctoral document addresses the use of agricultural remote sensing through focused investigations of plant disease detection and nutrient status assessment technologies that advance precision agriculture. The first chapter provides a comprehensive overview of remote sensing technologies in agriculture and examines current capabilities, limitations, and future directions. The final two chapters address studies that evaluated the virus-nitrogen interaction in wheat and compared methods to determine the nitrogen status of midseason field corn.

The first study evaluated the wheat streak mosaic virus (WSMV) × nitrogen interaction …

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …

A Comparative Analysis Of Yolo, Ssd, And R-Cnn Models For Ship Detection In Satellite Imagery, Abdulla Mohamed Alhemeiri

Thesis/ Dissertation Defenses

The importance of rapid, reliable ship detection in satellite imagery is underscored by needs in maritime safety, environmental protection, and sustainable fisheries. In this study, a comparative assessment of three widely used object detectors—Faster R-CNN, YOLOv3, and SSD (300/512)—is presented to clarify how accuracy and speed are balanced for ship and dock detection. A unified pipeline (MMDetection) was employed so that model training, validation, and evaluation were standardized. ShipRSImageNet, a high-resolution dataset with COCO-style annotations, was used as the primary benchmark, while Airbus Ship Detection data were reformatted from masks to bounding boxes and standardized to COCO to ensure consistency. …

Analyzing Sea Level Rise Scenarios Impact On The Mobility, Infrastructures, Environment Of Abu Dhabi And Defining Solutions By Creating A Digital Twin Using Gis And Game Engine, Justine Sylviane Lucie Sarrau

Thesis/ Dissertation Defenses

This dissertation is concerned with the potential impact of future sea level rise scenarios on the city of Abu Dhabi. With climate change, many coastal towns are at risk of experiencing this type of natural hazard. Currently, no precise scenario simulations have been developed, mainly related to the use of low spatial resolution elevation data. Additionally, the difficulty in understanding the real impacts persists when relying solely on traditional cartography and GIS methods. This dissertation mainly aims to take a further step by offering a new approach, which involves creating a real-time 3D simulation and dynamic flowing water. This is …

Studying The Difference Between Mapping Accuracy Of Non-Rtk Ultra-Lightweight And Rtk-Enabled Survey-Grade Drones, Mostafa Arastounia

Faculty Articles

This study compares the mapping accuracy of a non-RTK ultra-lightweight drone (DJI Mini2) with two survey-grade RTK-enabled drones (DJI Mavic3E and Phantom4) in three different sites. Flight parameters and weather conditions were the same on each site. The outputs were orthomosaics and digital surface models, whose accuracies were inspected by descriptive statistics and variance analysis tools. The data of the ultralight drone on the first site could not be processed due to strong wind, but its results for the second site (11 hectares) were comparable to those of survey-grade drones, i.e., the range and average of checkpoint errors for Mini2 …

Validation And Trend Analysis Of Satellite-Derived Surface Water Temperature Observations Over Adirondack Lakes, Marzi Azarderakhsh, Carolien Mossel, Abdou Rachid Bah, Aisha Malik, Fahmeda Khanom, Jonathan Borrelli, Pete Mcintyre, Hamidreza Norouzi, Kevin Rose

Publications and Research

This study aims to validate and evaluate satellite remote sensing observations from the Landsat series over 135 lakes in the Adirondack State Park, located in upstate New York, and to examine their surface temperature trends over the past 40 years. It utilizes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), along with Landsat 5 and 7. Park-scale results were derived by extracting MODIS surface temperatures within the park boundary, while lake-scale results were estimated using Landsat 5 (1984-2012) and Landsat 7 (1999-2023) observations. In addition, field observations were utilized to perform a comprehensive validation and evaluation of satellite-based surface temperature …