Remote Sensing Commons^™

High-Resolution Monitoring Of Intra-Seasonal Agricultural Drought Using Sentinel-2 And Machine Learning Across Bimodal Growing Seasons In Kenya, S. Mohammad Mirmazloumi, Harison Kipkulei, Rose Waswa, Tobias Landmann, Tom Dienya, Maximilian Schwarz, Fabrizio Ramoino, Clément Albergel, Gohar Ghazaryan

All Peer-Reviewed Publications

Drought presents significant challenges to agriculture, threatening food security and livelihoods, across many regions. In Kenya, recurrent droughts across diverse agro-ecological zones emphasize the urgent need for reliable and scalable drought assessment methods. Although drought assessment with various datasets has been carried out for this region, many of them often use course or moderate resolution data. This study uses high-resolution Sentinel-2 observations and machine learning to monitor intra-seasonal crop conditions and assess drought impacts across bimodal growing seasons. Using pixel-based supervised random forest models trained with multiple vegetation indices as input, we classify croplands into drought-affected and unaffected areas. The …

Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Multiple parameters associated with the land, atmosphere, and ocean were analyzed to study short-term and immediate pre-earthquake changes associated with the 28 March 2025 Myanmar earthquake (Mw 7.7). Anomalous clear-sky outgoing longwave radiation (ClrOLR) and trace gases (CH₄, CO, and O₃) were detected within two months prior to the mainshock. Vertical changes at different pressure levels suggest a possible underground source. High-temporal-resolution observations of the infrared brightness temperature and surface air pressure revealed short-lived fluctuations shortly before the earthquake, which may reflect localized stress adjustments and surface latent heat flux release during the final stage of earthquake preparation. In the …

A Review Of Satellite-Derived Terrestrial Evapotranspiration: Theories, Methods And Products, Yunjun Yao, Jiquan Chen, Joshua B. Fisher, Changliang Shao, Yuanbo Liu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurately estimating terrestrial evapotranspiration (ET), the second-largest hydrologic flux in the terrestrial water cycle, is vital for understanding global water and carbon exchanges. It is difficult to measure and estimate terrestrial ET at regional and global scales. Satellites have provided us an effective tool to estimate regional and global terrestrial ET in recent decades. In this article, we provide a comprehensive review of the basic theoretical foundations, methods and products of satellite-derived terrestrial ET. The basic theoretical foundations for estimating terrestrial ET are the Monin–Obukhov similarity theory (MOST) and other budding theories (e.g., Maximum entropy production theory, and generalized Hamilton …

A Distributed Model For Undergraduate Education In Environmental Remote Sensing: Increased Student Interest In Science And Sense Of Science Identity And Belonging, Gregory R. Goldsmith, Monae Verbeke, Jeremy Forsythe, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

"We present results from a new open-access course in satellite remote sensing of the environment that uses evidence-based, active learning pedagogy to train the next generation of interdisciplinary scientists The course, called Observing Earth from Above, teaches students how to access, visualize, and communicate satellite remote sensing data from NASA’s ECOSTRESS instrument to address a variety of environmental challenges. The resources focus on follow-along tutorials for students and also include recorded lectures and interviews with remote sensing scientists."

Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …

Super-Resolution Remote Sensing Datasets For Application To Caral–Supe Archeological Sites Employing Sar And Dems, Jungrack Kim, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

Relationship Between Vegetation Greenness (Ndvi) And Land Surface Temperature Across Land Cover Types In Ciwidey Sub-Watershed (1990–2020), Syal Syabila, Kuswantoro Marko, Revi Hernina

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

Land cover change significantly influences vegetation greenness and land surface temperature (LST), particularly in upstream watershed regions experiencing rapid development. This study aims to analyze changes in vegetation greenness (NDVI), land surface temperature, and their relationship across different land cover types in the Ciwidey Sub-Watershed, Bandung Regency, during 1990–2020. Landsat 5 TM and Landsat 8 OLI images (Path/Row 122/65) acquired in July 1990, 2005, and 2020 were processed using radiometric correction, supervised classification (Maximum Likelihood), NDVI extraction, and mono-window LST algorithm. Land cover classification accuracy was assessed using confusion matrix analysis. Linear regression was applied to evaluate the relationship between …

Satellites, Urban Heat, And Environmental Justice: Community As The Bridge Between Analysis And Action, Joshua B. Fisher, Ambar Rivera, Ava Cison, Ashley Agatep, Kainani Tacazon, Sophia Spiegleman, Alison Mckenery, Rio E. Fisher, Reginald Archer, Jason A. Douglas

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Heat waves are increasing in frequency, intensity, magnitude, and duration, causing a disproportionate impact on marginalized communities exposed to urban heat islands. Newly emerging spaceborne thermal sensing instruments, such as ECOSTRESS and Hydrosat, now have the capabilities to measure urban surface temperatures accurately at the block level (< 100 m) and with enough frequency to capture transient heat waves (daily to subweekly). Such data are critical for monitoring and informing policy and mitigation efforts, such as resurfacing, green space, cooling stations, and medical mobilization. These serve to advance environmental justice and reduce health risks—and deaths—among the most vulnerable: minority, low-income, elderly, those with physical- and mental-health preconditions, unhoused, children, and outdoor workers. While scientists have increasingly used satellite data to quantify urban heat islands and risks to communities, there remains a significant gap in action resulting from such analyses—a figurative and literal “valley of death.” Reviewing over 500 scientific publications, we identify a critical lack of engagement with the communities being analyzed (10.9%; n = 58); yet, community engagement is key to bridging such analysis with subsequent action. Here, we demonstrate how participatory community engagement directly with data and analysis leads to increased policy changes and mitigation efforts. Our framework has immediate implications for how scientists may augment their work and thought processes to achieve …

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

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 …

Prediction Of Methane Gas By Using Hyperspectral Imageries And Remote Sensing Techniques, Aysha Ali Alharmasi Alhajeri

Thesis/ Dissertation Defenses

Greenhouse gases is important for sustaining life on earth as well as in mitigating climate change. Methane (CH4) is considered as one of the most important critical gases for the global climate change and have significant influences on our life. Accordingly, the prediction of this greenhouse gas emissions is very important for avoiding the climate change effects and to maintain environmental sustainability. The objective of this study is to explore the potential applications for remote sensing to predict methane levels in the Earth’s atmosphere with a combination of local ground data and data from hyperspectral satellite imagery. By using hyperspectral …

The World's Largest Saddle Dam At Risk: Multisensor Geohazard Analysis And Downstream Impacts, Hesham El-Askary, Hesham Morgan, Surendra Maharjan, Ali Elgendy, Wenzhao Li, Rejoice Thomas, Austin Madson, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Grand Ethiopian Renaissance Dam (GERD) Saddle Dam, which holds approximately 89% of the main reservoir's live storage, is one of the largest and most critical auxiliary dams globally; its construction on Ethiopia's Blue Nile has consequently raised significant regional and international concerns regarding potential environmental impacts and geohazard risks. This study presents a comprehensive risk assessment of the GERD Saddle Dam by integrating high-resolution satellite data (GRACE, Sentinel-1, Sentinel-2, WorldView-3), hydrological modeling (SWAT), Persistent Scatterer Interferometry (PSI), geospatial analysis, and advanced statistical techniques. The results highlight critical structural vulnerabilities, including groundwater infiltration estimated at approximately 41 ± 6.2 billion …

Evaluating Climatic Niche Suitability For Bos Javanicus Reintroduction In Cagar Alam Pananjung Pangandaran Using Maxent And Native-Habitat Benchmarks From Ujung Kulon And Alas Purwo, Azhari Al Kautsar, Masita Dwi Mandini Manessa

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

The Javan banteng (Bos javanicus) persists on Java mainly in a small number of protected-area strongholds, making robust climatic niche characterization important for conservation planning and for evaluating potential management or restoration targets. Here, we modeled banteng climatic suitability in southwestern Java using a MaxEnt (maxnet) framework calibrated with bioclimatic predictors from CHELSA and benchmark occurrence records from extant populations in Ujung Kulon National Park (UKNP) and Alas Purwo National Park (APNP). To contextualize transferability to non-occupied protected habitat, we also projected suitability to Cagar Alam Pananjung Pangandaran (CAPP) and quantified environmental novelty using the Multivariate Environmental Similarity Surface (MESS). …

Prediction Of Methane Gas By Using Hyperspectral Imageries And Remote Sensing Techniques, Aysha Ali Alhajeri

Theses

Greenhouse gases is important for sustaining life on earth as well as in mitigating climate change. Methane (CH4) is considered as one of the most important critical gases for the global climate change and have significant influences on our life. Accordingly, the prediction of this greenhouse gas emissions is very important for avoiding the climate change effects and to maintain environmental sustainability. The objective of this study is to explore the potential applications for remote sensing to predict methane levels in the Earth’s atmosphere with a combination of local ground data and data from hyperspectral satellite imagery. By using hyperspectral …

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

A Student-Centered Gis Classroom: Second Chances And Real-World Practice Teaching Portfolio For Nres218 Introduction To Geospatial Technologies, Ran Wang

UNL Faculty Course Portfolios

This course portfolio documents the design, implementation, and reflection of NRES218 Introduction to Geospatial Technologies, an introductory undergraduate geographic information system (GIS) course. This course aims to integrate basic and applied sciences to help students develop spatial thinking and spatial analysis skills for proposing spatial science–oriented solutions. Instructional strategies emphasize experiential learning through structured laboratory exercises and outdoor field activities that connects real-world observation with GIS analysis. To support student learning and persistence, the course also incorporated flexible assessment practices, including second-chance exams. Reflection on student engagement and performance revealed that while these strategies were highly beneficial for some students, …

Modelling Land Use Land Cover Change In Banyumas Regency Using Remote Sensing Data For Tourism Policy Evaluation, Revi Hernina, Arif Wicaksono, Adi Wibowo, Astrid Damayanti

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

Tourism is essentially a geographical phenomenon, encompassing the movement and flow of people and spatial distribution patterns relating to land use consumption. The impacts of tourism on LUCC must track and monitor regrading effected to environment and human side. For decades, Banyumas Regency has been known for its famous tourist destinations such Baturaden District and numerous waterfalls. However, the development of tourism infrastructures within its vicinity has sparked complaints from communities, particularly damaged roads and decreasing tourist visits. From environmental perspective, excessive tourism development might cause decreasing natural carrying capacity. Therefore, to provide deeper analysis regarding the current tourism development, …

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 …