Library · Signals, sensing & imaging
What Remote Sensing & Satellite Imagery got wrong, from 69 theses
Summary written by a model from the records below: Remote sensing and satellite imagery workflows regularly confront obstacles ranging from cloud contamination and insufficient spatial resolution to proxy decoupling from ground reality. Machine learning models and retrieval pipelines often experience severe geographic generalization errors, sensor inconsistencies, and data attrition from strict quality filtering. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 9 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Spatial resolution limitations and mixed pixel contamination obscure fine-scale targets
Moderate and coarse sensor resolutions fail to resolve small urban infrastructure, individual agricultural fields, or micro-topographic variations. Surrounding matrix contamination and mixed pixels blur fine boundaries, causing feature extraction and localized detection workflows to fail.
Tried and failed
direct remote sensing image measurement applied to multiscale river width size distribution. Outcome: data insufficient. Reason: sensor resolution limits, canopy obstruction, and finite-size effects truncate small features and skew distribution tails
Quantifying the Size Distribution of Rivers Across Spatial Scales · Virginia Tech · Master's
Tried and failed
GIS digital elevation models for hydrological modeling applied to excavated terrain and plateau surface hydrology. Outcome: data insufficient. Reason: Coarse spatial resolution and missing historical imagery failed to resolve excavation micro-topography and hydrology
From Wasteland to Biocultural Heritage: Negotiation by Design in Khotale, Konkan, India · Cornell · Master's
Tried and failed
moderate resolution multispectral satellite vegetation index analysis applied to small urban green infrastructure features. Outcome: no signal. Reason: Feature sizes were smaller than pixel spatial resolution, causing surrounding matrix contamination to dominate pixel reflectance values.
Pipes or People? An Analysis of Green Stormwater Infrastructure Co-benefits to Accelerate Regime Change · Harvard · Master's
Considered and rejected
Considered and rejected: Rejected analyzing small cities with urban radius < 5 km because 0.1° satellite data cannot resolve them and small areas do not exert sufficient rainfall impacts.
Urbanization’s impacts on precipitation : observational analysis of global patterns and storm dynamics · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected using Moderate Resolution Imaging Spectroradiometer (MODIS) 250m imagery for NDVI because spatial resolution was too coarse for small-scale urban greenness.
RESIDENTIAL CONTEXT AND PATHWAYS OF EXPOSURE: EXAMINING RESIDENTIAL SEGREGATION, NEIGHBORHOOD ADVERSITY, AND NEIGHBORHOOD GREENNESS IN RELATION TO HEALTH · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Decided against building-by-building footprint shape extraction directly from raster-to-vector converted satellite data in dense/undulated areas due to mixed pixels grouping multiple buildings.
On the reliable generation of 3D city models from open data · Nottingham · PhD
Tried and failed
coarse-resolution satellite burned area mapping applied to low-severity wildfire detection. Outcome: no signal. Reason: spatial resolution limitations and subtle spectral changes prevented detection of low-severity events
Burned severity assessment and mapping in the Hindu Kush-Himalayan region of Asia · Texas Tech · Master's
Tried and failed
multispectral vegetation indices from UAV imagery applied to canopy wilting assessment. Outcome: no signal. Reason: lower spatial resolution in multispectral bands reduced correlation with ground-truth visual scores compared to high-resolution RGB
Multi-modal phenotyping and breeding for drought tolerance in soybean · Iowa State · PhD
Tried and failed
spectral mixture analysis with lower-resolution bands applied to satellite imagery land cover classification. Reason: model misspecification occurred due to lack of pure endmember pixels at the coarser spatial resolution
Multiscale spatiotemporal approaches to contemporary pyrogeography in savanna systems · UT Austin · PhD
Tried and failed
satellite-guided spatial cluster sampling applied to short-range spatial nutrient variation. Outcome: no signal. Reason: pixel resolution and sampling box design failed to resolve variables with high short-distance spatial variance
MULTI-SENSOR APPROACHES FOR VINEYARD MANAGEMENT PRACTICES: FROM NUTRIENT SAMPLING TO CROPLOAD ASSESSMENT · Cornell · PhD
Considered and rejected
Considered and rejected: Using satellite-derived land surface temperature or net ecosystem productivity was rejected due to coarse spatial resolution.
Studying the Effects of Large-Scale Solar Farms on Plant Ecosystems in New York State Using NDVI Based Geospatial Analysis · Cornell · Master's
Considered and rejected
Considered and rejected: Rejected relying solely on satellite-informed PlanetScope/ClimateTRACE feedlot imagery as prior due to lack of comprehensiveness for small facilities
Understanding Tropical Methane Emissions: Uniting Top-Down Inversions of Satellite Observations with Bottom-Up Inventories · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected using Planet's SkySat 50 cm satellite imagery for direct burrow object detection because the resolution was insufficient.
Analyzing Colony Structural Characteristics to Assess Establishment of Reintroduced Black-Tailed Prairie Dogs (Cynomys ludovicianus) · Texas State University · Master's
Considered and rejected
Considered and rejected: Rejected using 60 m Landsat MSS imagery prior to 1999 due to spatial resolution being too coarse for meaningful localized erosion delineation.
Channel erosion projections for threatened Alaska-Native communities along the Kuskokwim River · Iowa State · Master's
Considered and rejected
Considered and rejected: Rejected standard MODIS 250 m imagery because the pixel footprint (6.25 ha) was too coarse for smallholder median field sizes (0.63–1.05 ha).
Earth Observation for Sustainable Sugarcane Production · Cranfield · PhD
Tried and failed
consumer GPS and satellite imagery georeferencing applied to camera calibration over small spatial extents. Outcome: data insufficient. Reason: spatial resolution and consumer GPS accuracy were inadequate over short distances
Towards a Comprehensive Bicycle Motion Behavior Model and Naturalistic Cycling Dataset · Virginia Tech · Master's
Tried and failed
social media and remote sensing damage mapping applied to asset-level infrastructure damage assessment. Outcome: data insufficient. Reason: high uncertainty and insufficient precision prevent reliable asset-level structural or utility status evaluation
Leveraging Post-Disaster Data for Rapid Damage Assessment of Infrastructure Systems · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected conducting a randomized household survey across Nakuru peri-urban settlements due to lack of formal postal addresses and aerial imagery inability to resolve multi-household informal rental units per structure
Safety in Separation? Ethnic Demography and Violence in Kenya · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected grid-based spatial random point sampling stratified by LANDSAT SEDAC satellite density due to aerial inability to detect multiple informal single-story rental units per structure
Safety in Separation? Ethnic Demography and Violence in Kenya · Cornell · PhD
Cloud cover and atmospheric obstruction create severe data gaps and masking artifacts
Persistent cloudiness, shadows, and polar night darkness obscure study footprints and produce high rates of missing observations across satellite time series. Standard automated cloud masking pipelines frequently misclassify cloud features or fail to distinguish them from snow and ice surfaces.
Considered and rejected
Considered and rejected: Rejected using raw satellite imagery due to inaccuracies caused by cloud cover over UK study sites.
Enhancing Winter Wheat Crop Yield Predictions: A Data-Driven, Incremental and Integrative Approach with Machine Learning · University College Dublin · PhD
Tried and failed
daily satellite nighttime light time-series analysis applied to flood exposure and vulnerability mapping. Outcome: data insufficient. Reason: daily observations had severe cloud contamination and satellite fluctuations, requiring temporal aggregation to filter noise
Floods, rivers, and night-time lights: analysing human presence and vulnerability in the indus - ganga basin · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected using observational satellite flood extent (Sentinel-1/2) as ML ground truth due to cloud cover, small flood extents, and lack of validation data, switching to Floods Directive numerical model outputs.
Assessing Vulnerability to Rapid-Onset Coastal Flooding: Advancing Methodological Approaches with a Focus on the Mediterranean Sea Basin. · Politecnico di Torino · PhD
Tried and failed
satellite optical remote sensing column retrievals applied to regional greenhouse gas emission estimation. Outcome: data insufficient. Reason: frequent cloud cover resulted in extremely low clear-sky observation availability across the target region
Using measurements and modelling of carbon isotopes for evaluating methane emissions and sources in the UK · Imperial · PhD
Tried and failed
Scene-wide cloud cover threshold filtering applied to satellite vegetation index time series. Reason: retained localized cloud and shadow artifacts causing erroneous drops in mid-season vegetation indices
Earth Observation for Sustainable Sugarcane Production · Cranfield · PhD
Tried and failed
rule-based optical satellite cloud masking applied to mountain glacier satellite imagery. Reason: failed to distinguish high-altitude clouds from snow and ice, causing unmasked clouds to be misclassified as water
Observing the Seasonal Evolution of Supraglacial Ponds in High Mountain Asia: A Supervised Classification Approach · Cambridge · Master's
Tried and failed
satellite multispectral imagery classification applied to crop disease detection across years. Outcome: did not generalise. Reason: poor cross-year model transferability and cloud cover interference
Multidisciplinary strategies for grapevine disease monitoring in the age of digital viticulture · Cornell · PhD
Lost to a baseline
229 reservations in the southeast corner of the satellite imagery footprint were excluded due to cloud cover in the 1973 Landsat MSS imagery preventing baseline land cover estimation.
Essays on the Influence of Western Institutions on Indigenous Societies · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected using satellite remote-sensed chlorophyll-a data (NDCI index) due to massive missing data (<30% data availability) caused by persistent Irish cloud cover.
A risk assessment and an early warning system for marine biotoxins in Irish produced shellfish · University College Dublin · PhD
Considered and rejected
Considered and rejected: Excluded Landsat 8 2014 imagery due to excessive cloud obscuration over the study area.
An approach to map soil texture class on the Iowan Erosion Surface · Iowa State · Master's
Considered and rejected
Considered and rejected: Rejected using daily satellite Aerosol Optical Depth (AOD) for the primary subway expansion study due to large numbers of missing observations caused by cloud coverage.
Essays on Environmental Challenges and Regulations in China · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected using satellite imagery alone for estimating ice-on/ice-off dates for Lake Pesiöjärvi due to polar night darkness and cloud obstructions, requiring integration of FDD and water temperature profiles.
Reclaiming Autumn in Limnology: Evidence of Shifting Autumn Phenology and Impacts on Winter Conditions and Cross-Seasonal Lake Dynamics · York University · Master's
Considered and rejected
Considered and rejected: Decided against purely satellite DTC/downscaling models due to dependency on clear skies and lack of atmospheric physics.
Impact of urbanization and climate change on building energy use and heat emissions · Iowa State · PhD
Considered and rejected
Considered and rejected: Filtering out satellite images with cloud coverage above 5% was rejected because it caused substantial geographic and seasonal data imbalance (e.g., selecting 80% of Yuma vs 19% of Broward data)
Machine Learning Approaches to Predict PM2.5 Using Satellite Images · Johns Hopkins · Master's
Surface reflectance proxies decouple from true ecological and subsurface conditions
Satellite-derived optical vegetation indices and surface metrics fail to track deeper groundwater drawdowns, true forest mortality, or subtle crop disease symptoms. Optical reflectance signals cannot reliably separate distinct biological agents, complex soil units, or understory structures that drive ground conditions.
Tried and failed
satellite-derived vegetation productivity models applied to regional forest productivity estimation. Outcome: no signal. Reason: optical remote sensing product failed to capture known productivity differences across distinct forest management types and regions
Relationships between Forest Productivity and Water Yield Across Virginia's Forests · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected using standard satellite Relativized Burn Ratio (RBR) alone for post-fire mortality assessment because RBR fails in areas with high pre-existing drought mortality or prior fires.
Developing a framework for studying fine resolution impacts of climate change on forest ecosystems at regional scale using remote sensing and artificial intelligence · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected relying on satellite dNBR-derived fire severity classes because they differed substantially from ground-truth medium-to-large tree basal area mortality.
The structural impacts of wildfire and defoliation on Mexican spotted owl (Strix occidentalis lucida) nesting habitat · Texas Tech · Master's
Tried and failed
multispectral satellite regression without understory biomass covariates applied to forest basal area estimation. Outcome: no signal. Reason: omitting understory biomass predictors severely degraded overstory basal area model accuracy
Quantifying riparian forest structure and decadal streambank erosion trends in an agriculture-dominated watershed, central Iowa, USA– links and implications · Iowa State · Master's
Tried and failed
satellite remote sensing proxy validation against in-situ sensors applied to upper ocean chlorophyll concentration estimation. Outcome: no signal. Reason: optical depth satellite estimates failed to correlate with autonomous float measurements
Delineating Planktonic Habitats in Dynamic Marine Environments: From Oceanographic Expeditions to Autonomous Platform · Georgia Tech · PhD
Tried and failed
multisatellite combined soil moisture product applied to long-term soil moisture trend estimation. Reason: Satellite-merged product underestimates the magnitude of trends compared to in situ observations
Fidelity of climate data: Trends, extremes, and nonlinearities in instrumental and tree-growth proxies of climate · Harvard · PhD
Tried and failed
satellite remote sensing proxies for subsurface tracking applied to local groundwater availability estimation. Outcome: no signal. Reason: Surface vegetation and shallow soil moisture indicators decoupled from deep dry-season water level declines.
Towards resolving data scarcity in water resources management · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected satellite-derived drought/flood indices in favor of self-reported survey shock measures because satellite indicators (e.g. during Ethiopia's 2015 drought) failed to show agricultural drought signals and lacked exact household coordinates.
Essays in Environmental and Development Economics · Cornell · PhD
Tried and failed
multispectral vegetation indices from aerial imagery applied to foliar crop disease severity assessment. Outcome: no signal. Reason: Indices showed no significant correlation with disease scores across inbred trial seasons
NORTHERN LEAF BLIGHT RESISTANCE IN MAIZE: BENEFTIS, COSTS, AND HIGH-THROUGHPUT PHENOTYPING · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected individual-agent satellite remote sensing due to inability to differentiate co-occurring biotic agent species identities.
Spatio-temporal patterns of forest disturbance in western North America: implications for forest resilience · University of Washington · PhD
Predictive models suffer out-of-sample transferability and regional generalization failures
Machine learning algorithms and downscaling models trained on remote sensing data experience large error spikes when evaluated on out-of-sample geographic locations. Without regional stratification, models fail to accommodate spatial differences in terrain, seasonality, and extreme environmental classes.
Tried and failed
random forest regressor on remote sensing data applied to spatial crop yield prediction. Outcome: did not generalise. Reason: out-of-sample prediction on held-out geographic locations resulted in high prediction errors across fields
Using digital agriculture and decision-support systems for agronomic and environmental benefit: Towards novel applications and holistic landscape analysis · Iowa State · PhD
Tried and failed
random forest classification on satellite imagery applied to archaeological soil and terrain mapping. Outcome: did not generalise. Reason: sensor artifacts and spectral overlap caused high false positives and missed recent land-use changes
Tectonic and Climatic Controls on Continental River Systems · MIT · PhD
Tried and failed
single global random forest without spatial stratification applied to satellite remote sensing downscaling. Outcome: did not generalise. Reason: lack of regional stratification caused systematic regional biases and poor seasonal dynamic capture
Understanding the Vegetation Dynamics of Tropical Rainforests and Drylands Using Solar-induced Chlorophyll Fluorescence (SIF) · Cornell · PhD
Tried and failed
entropy thresholding on SAR imagery applied to flood extent mapping. Outcome: did not generalise. Reason: failed in sloped terrain and permanent water, misclassifying urban rooftops as dry
Compound Flood Analysis with GIS-Integrated Dynamic Flood Models for Coastal Urban Areas · Georgia Tech · PhD
Tried and failed
conditional GAN image-to-image translation applied to satellite imagery site suitability mapping. Outcome: did not generalise. Reason: Struggled to capture satellite imagery features representing extreme suitability classes on test data
Urban planning AI in the loop : case studies of public EV charger placement in transportation electrification planning · UT Austin · PhD
Tried and failed
principal components regression spatial downscaling applied to satellite land surface temperature data. Outcome: did not generalise. Reason: generated extreme urban outliers and high errors (RMSE >8 K)
Impact of urbanization and climate change on building energy use and heat emissions · Iowa State · PhD
Lost to a baseline
GBM-Visitor-Lidar and GBM-Visitor-Satellite models showed worse transferability error (NRMSE) on aboveground live tree carbon compared to canopy cover across increasing Kolmogorov-Smirnov distances.
Leveraging Open Data to Support Forest Mapping, Modeling, and Policy Analysis in the Pacific Northwest, USA · University of Washington · PhD
Considered and rejected
Considered and rejected: Map-based matching against pre-stored satellite databases was rejected because of database inaccuracies in disaster/deformed areas and high overhead over unknown territory.
ASSESSMENT OF ELECTRO-OPTICAL IMAGING TECHNOLOGY FOR UNMANNED AERIAL SYSTEM NAVIGATION IN A GPS-DENIED ENVIRONMENT · Naval Postgraduate School · Master's
Complex machine learning models underperform compared to standard baselines
Complex approaches like neural networks, tree ensembles, and naive Bayes classifiers frequently fall short of simpler linear baselines or alternative boosted trees. These models often overfit to spectral-temporal dimensions, violate feature independence assumptions, or omit critical spatial priors such as elevation data.
Tried and failed
random forest regression applied to satellite retrieval bias correction. Outcome: worse than baseline. Reason: significantly underperformed gradient boosted decision tree algorithms like LightGBM and XGBoost
Tried and failed
Support vector regression on remote sensing features applied to surface air temperature prediction. Outcome: worse than baseline. Reason: None
Lost to a baseline
ANN SST prediction without satellite bands (RMSE 1.45°C) was beaten by the Random Forest baseline without satellite bands (RMSE 1.04°C).
Machine Learning for Improvement of Ocean Data Resolution for Weather Forecasting and Climatological Research · Virginia Tech · Doctorate
Tried and failed
Gaussian Naive Bayes classification applied to satellite urban land cover mapping. Outcome: worse than baseline. Reason: overlapping spectral features violated naive independence and distribution assumptions across complex urban surface classes
Improved urban extreme weather simulation by capturing urban heterogeneity · UT Austin · PhD
Lost to a baseline
PIV failed to resolve suspended sediment plume displacements away from sharp plume edges in PlanetScope satellite imagery where Horn-Schunck succeeded.
Advancing coastal ocean modelling through deep learning and remote sensing data integration · Imperial · PhD
Tried and failed
tree-based ensemble regression applied to satellite multi-spectral time-series data. Outcome: worse than baseline. Reason: overfit without capturing complex spectral-temporal structures compared to regularized linear models
Novel deep learning-based methods for high-throughput image-based plant phenotyping and large scale crop yield prediction · Iowa State · PhD
Tried and failed
random background sampling for semantic segmentation applied to satellite imagery land cover classification. Outcome: worse than baseline. Reason: underperformed compared to boundary-targeted and feature-based sampling strategies
Mapping agricultural land in support of opium monitoring in Afghanistan with Convolutional Neural Networks (CNNs). · Cranfield · PhD
Tried and failed
omitting digital elevation data in visual segmentation applied to aerial imagery semantic segmentation. Outcome: worse than baseline. Reason: lacks crucial topographic priors, causing false positive segmentations above natural elevation thresholds
Monitoring and understanding treeline dynamics in the Swiss Alps from 80 years of aerial imagery · EPFL · PhD
Sensor discrepancies and retrieval biases degrade process models and downstream analysis
Inconsistent satellite sensors, pixel saturation, and atmospheric retrieval biases lead to substantial discrepancies against airborne and ground station measurements. Ingesting these biased remote sensing inputs or assimilating them into dynamic process models degrades simulation accuracy compared to calibrated baselines.
Tried and failed
standard regression on remote sensing derived features applied to forest canopy height and age modeling. Reason: disturbance detection misclassified thinning events, generating severe label noise and extreme outliers in estimated age
Assessing age-height relationship using ICESat-2 and Landsat time series products of southern pines in southeastern region · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected direct single-source modeling and naive averaging of heterogeneous satellite streams due to inability to resolve spatial discrepancies and bias.
Statistical spatio-temporal models with applications to natural processes · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected using nighttime light imageries as primary longitudinal data due to pixel saturation and inconsistent satellite sensors.
POLYCENTRIC SPATIAL DEVELOPMENT—MEASURES, EXPLANATIONS, PERFORMANCE · Cornell · PhD
Tried and failed
higher spatial resolution vegetation mapping applied to biogenic atmospheric emission modeling. Outcome: worse than baseline. Reason: outdated high-resolution vegetation distribution maps introduced spatial biases compared to coarser satellite data
Constraining Regional and Global Atmospheric Emissions and Chemistry Using Models and Observations · Georgia Tech · PhD
Tried and failed
satellite soil moisture data assimilation for initialisation applied to hydrological flood event modeling. Outcome: worse than baseline. Reason: remotely sensed soil moisture values degraded performance for specific flood events compared to baseline calibration
Potential of incorporating satellite soil moisture observations in flood modeling in Kelani river basin, Sri Lanka · University of Moratuwa · Master's
Tried and failed
dynamic parameter ingestion from remote sensing applied to process-based crop ecosystem models. Outcome: worse than baseline. Reason: dynamic parameters caused mis-specification in leaf area accumulation dynamics degrading biomass predictions
Remote sensing and agro-ecosystem modeling: Mutually beneficial in analysis, validation, and application · Iowa State · PhD
Tried and failed
gridded satellite meteorological datasets for local simulation applied to photovoltaic power output modeling. Outcome: data insufficient. Reason: satellite gridded data had systematic temperature bias and overly smoothed wind speeds compared to ground stations
Essays on Economics of Distributed Energy · Georgia Tech · PhD
Lost to a baseline
NOAA DCOMP and NASA SatCORPS satellite retrieval methods were outperformed by airborne in situ PDI and CAS probes, underestimating Nd by up to 2.5x.
CHARACTERIZING AND UNDERSTANDING THE AEROSOL-CLOUD INTERACTIONS OVER THE SOUTHERN CALIFORNIA BIGHT AND ITS EFFECTS ON THE MARINE ENVIRONMENT · Naval Postgraduate School · Master's
Considered and rejected
Considered and rejected: Rejected using NCAR satellite data for relative humidity boundary conditions due to unrealistically high winter RH values compared to ground stations.
Evaluating Uncertainty in Hygrothermal Modelling of Heritage Masonry Buildings · Carleton University · PhD
Strict filtering thresholds cause severe data attrition and destroy statistical power
Imposing strict spatiotemporal colocation criteria, rainfall thresholds, and heterogeneity filters often discards most of the available satellite dataset. This heavy sample elimination reduces statistical power, breaks temporal observation sequences, and fails to eliminate underlying sensor discrepancies.
Tried and failed
threshold-based microphysical quality filtering applied to satellite cloud liquid water retrieval. Outcome: worse than baseline. Reason: filtering thin clouds increased retrieval discrepancy and degraded cross-instrument correlation
Observing aerosol impacts on Arctic clouds · Imperial · PhD
Tried and failed
quality filtering by spatial heterogeneity and thresholding applied to satellite remote sensing retrieval bias. Outcome: no signal. Reason: filtering cloud fraction, heterogeneity, and precipitation failed to reduce retrieval discrepancies between distinct sensors
Observing aerosol impacts on Arctic clouds · Imperial · PhD
Tried and failed
strict rainfall filtering on satellite radiometric time-series applied to satellite diurnal vegetation water detection. Outcome: data insufficient. Reason: filtering for dry antecedent conditions eliminated 85 percent of samples, destroying statistical power for diurnal tests
Microwave and destructive measurements of maize seasonal, daily, and sub-daily variations in water quantity · Iowa State · PhD
Tried and failed
satellite NDVI time series analysis applied to grassland vegetation dynamics monitoring. Outcome: data insufficient. Reason: the multi-year satellite imagery dataset was incomplete over the target region and time period
Securing landscape resilience · Cranfield · PhD
Tried and failed
strict spatiotemporal colocation thresholding applied to satellite and ground measurement validation. Outcome: data insufficient. Reason: overly strict matching criteria severely reduced sample size, degrading statistical significance and correlation metrics
Left open by the authors
Problems the authors named and did not get to.
Left open
Quantify uncertainty propagation from satellite input datasets through the Random Forest SIF downscaling model. Blocker: None
Understanding the Vegetation Dynamics of Tropical Rainforests and Drylands Using Solar-induced Chlorophyll Fluorescence (SIF) · Cornell · PhD
Left open
Evaluate Meteosat-8 geostationary satellite data for precipitation retrieval over the Tibetan Plateau using random forest models. Blocker: None
Measuring Precipitation from Space - a Satellite View on the Tibetan Plateau · University of Marburg · PhD
Left open
Extract riverbed vegetation and environmental descriptors from satellite imagery to evaluate their effect on stream velocity prediction using Random Forest models. Blocker: None
Machine Learning and Optimization Model Development for Northern Community Energy Planning · Carleton University · Master's
Left open
Develop improved remote sensing disturbance mapping methods for thinned forest plantations. Blocker: No specific algorithmic approach, target metrics, or baseline dataset are specified.
Assessing age-height relationship using ICESat-2 and Landsat time series products of southern pines in southeastern region · Virginia Tech · Master's
Left open
Evaluate the spatial transferability of the random forest temperature model by applying it to additional cities using public satellite and reanalysis data. Blocker: None
Spatio-temporal variability of temperature effects on mortality in Sao Paulo, Brazil · Imperial · PhD
Left open
Evaluate higher spatial resolution satellite imagery against temporal resolution trade-offs for high-resolution crop yield prediction models. Blocker: Lacks specific target sensors, methodologies, and accessible matched ground-truth subplot yield data
High-resolution crop yield predictions from satellite-generated NDVI images · Iowa State · Master's
Left open
Train region-specific Random Forest classifiers on satellite or geographic data for ecological subregions across the Amazon basin outside Xingu Indigenous Territory. Blocker: None
Tectonic and Climatic Controls on Continental River Systems · MIT · PhD
Left open
Train and evaluate out-of-sample ML crop yield prediction models using multi-year field data and high-resolution daily satellite imagery. Blocker: Requires commercial high-resolution satellite imagery (e.g., Planet) and proprietary multi-site ground-truth yield monitor data.
Using digital agriculture and decision-support systems for agronomic and environmental benefit: Towards novel applications and holistic landscape analysis · Iowa State · PhD
Left open
Benchmark Bayesian convolutional neural networks for rainfall prediction regression using multispectral satellite meteorological imagery and open-source Bayesian deep learning frameworks. Blocker: None
BENCHMARKING BAYESIAN DEEP LEARNING METHODS WITH MULTI-SPECTRAL SATELLITE IMAGERY · Naval Postgraduate School · Master's
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Gaurav Inder Toor · Cornell · PhD · 2024
Safety in Separation? Ethnic Demography and Violence in Kenya - Rebecca Murray-Watson · Imperial · PhD · 2024
Observing aerosol impacts on Arctic clouds - Neha Joshi · Cranfield · PhD · 2024
Earth Observation for Sustainable Sugarcane Production - Sonia Sharma Banjade · Virginia Tech · Master's · 2024
Assessing age-height relationship using ICESat-2 and Landsat time series products of southern pines in southeastern region - Jiaming Wen · Cornell · PhD · 2023
Understanding the Vegetation Dynamics of Tropical Rainforests and Drylands Using Solar-induced Chlorophyll Fluorescence (SIF) - Wei Chen · Iowa State · PhD · 2023
Impact of urbanization and climate change on building energy use and heat emissions - Matthew Nowatzke · Iowa State · PhD · 2022
Using digital agriculture and decision-support systems for agronomic and environmental benefit: Towards novel applications and holistic landscape analysis - Samuel L. Goldberg · MIT · PhD · 2021
Tectonic and Climatic Controls on Continental River Systems - Sarah Elizabeth Hancock · Harvard · PhD · 2026
Understanding Tropical Methane Emissions: Uniting Top-Down Inversions of Satellite Observations with Bottom-Up Inventories - Nicholas Balasus · Harvard · PhD · 2026
Correcting Biases in Satellite Methane Observations: Applications to Landfill Emissions in the United States and Continental-Scale Emissions in Africa - Gayatri Paudel · Texas Tech · Master's · 2026
Burned severity assessment and mapping in the Hindu Kush-Himalayan region of Asia - Kathleen Kanaley · Cornell · PhD · 2025
Multidisciplinary strategies for grapevine disease monitoring in the age of digital viticulture
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