About

Jeff Dozier was a distinguished professor at UC Santa Barbara and the founding dean of the Bren School of Environmental Science & Management. He joined UCSB in 1974 and played a pivotal role in building the university's Department of Geography and establishing the Earth Research Institute. His research focused on snow hydrology, Earth system science, remote sensing, and information systems, with significant contributions to understanding snow storage and melting in mountain ecosystems, which have economic and social implications for water resources. Dozier's work combined field studies in high mountains with satellite remote sensing, and his algorithms are used worldwide by hydrologists, water managers, and climate researchers. He served as Project Scientist for NASA’s Earth Observing System and Jet Propulsion Laboratory’s High-resolution Imaging Spectrometer program. His expertise extended beyond snow to fire detection from space, and he contributed to the film 'Frozen' as an advisor on snow illustration. Recognized as a global leader, he was an elected Fellow of the American Geophysical Union and the American Association for the Advancement of Science, and received awards including NASA’s William T. Pecora award and Public Service Medal. Dozier was also known for his adventurous spirit, leading climbing expeditions in Afghanistan, and his legacy is commemorated by Dozier Dome in the Sierra Nevada. He passed away in November 2024, leaving a lasting impact on environmental science and water resource management.

Research topics

• Environmental science
• Computer Science
• Artificial Intelligence
• Ecology
• Geology
• Physics
• Remote sensing
• Optics
• Geomorphology
• Environmental engineering
• Engineering
• Soil science
• Water resource management
• Geography

Selected publications

• Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra

  ˜The œcryosphere · 2025-06-27 · 1 citations

  articleOpen accessSenior author

  Abstract. Key to the success of spaceborne missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that surface reflectance products over snow show an erroneous hook with decreases in the visible wavelengths, causing per-band and broadband reflectance errors of up to 33 % and 11 %, respectively. This hook is sometimes mistaken for soot or dust but can result from three artifacts: (1) background reflectance that is too dark, (2) an assumption of level terrain, or (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are being implemented.

  PublisherOA PDFDOI

• Atmospheric controls on river temperature: Sensitivity of river temperature downstream of a dam to changes in a Mediterranean climate

  Journal of Hydrology Regional Studies · 2025-07-15 · 1 citations

  articleOpen accessSenior author

  San Joaquin River in the Central Valley of California, a regulated lowland river in a Mediterranean climate. Assessing the sensitivity of river temperature to climate is challenging due to the complexity of the processes that control the energy balance. In this work, we utilize a spectral river energy balance model (FLUVIAL-EB) together with a local sensitivity analysis framework to assess the river temperature’s response to changes in atmospheric variables downstream of a dam. We show that many of Earth’s climate variables, including incoming shortwave radiation, net longwave radiation, atmospheric emissivity, and air temperature, along with the fundamental optical properties of water, control the heating of rivers. Predicted river temperature either warms or cools with minor increases to atmospheric variables. Warming trends in river temperature are shown to be dominated by incoming solar radiation across all seasons and river distances, where a minor increase in solar radiation yields increases in river temperatures by 1–10°C. In contrast, a comparable minor increase in longwave radiation and air temperature results in river temperature warming of 0.1–2.6°C and 0.1–3.1°C, respectively, and varies seasonally. An examination of mechanisms driving absorption of shortwave and longwave radiation, together with a sensitivity analysis of how changes in the atmosphere affect a river’s heat fluxes and temperature, improves understanding of likely changes in river temperatures under future climates. • Net radiation rather than air temperature primarily controls river warming below a dam. • Air temperature and longwave radiation control atmospheric emissivity and river warming. • Emitted longwave and windspeed control river cooling but are insufficient to balance warming. • River temperature models should include atmospheric emissivity and spectral absorption. • A spectral energy balance model and sensitivity framework can illustrate atmospheric controls.

  PublisherDOI

• Assessment of methods for mapping snow albedo from MODIS

  Remote Sensing of Environment · 2025-05-05 · 2 citations

  articleOpen access

  We compare five daily MODIS-derived snow albedo products to terrain-corrected, in situ data from sites in California and Colorado, USA, and to snow albedo derived from airborne hyperspectral imagery over several basins in California and Colorado. The MODIS-derived products we consider are NASA standard products MOD10A1, MCD43A3, and MCD19A3D, along with STC-MODSCAG/MODDRFS and MODIS SPIReS. These products vary in their retrieval algorithms, including whether, for mixed pixels, they represent the albedo of snow within the pixel or the albedo of the whole pixel. When compared to in situ data, STC-MODSCAG/MODDRFS and SPIReS products have the highest accuracy (RMSE ≤0.093) and most spatially and temporally complete data records (∼99 %) because the algorithms each have independently developed gap filling and interpolation methods. The MOD10A1 and MCD43A3 products underestimate snow albedo (RMSE ≤0.248) because they incorporate non-snow land surfaces into their calculations and have less complete data records (∼76 %) due to less accurate snow detection and lack of interpolation. The MCD19A3D product has accuracy similar to STC-MODSCAG/MODDRFS and SPIReS (RMSE = 0.090) but the lowest data completeness of all datasets (56 %). We found similar performance trends when comparing the MODIS products to airborne hyperspectral data. Our analysis shows algorithms that account for fractional snow cover and incorporate all available spectral information result in the best snow albedo products across time and space. Similar algorithms applied to hyperspectral data can better resolve spectral features to retrieve optical properties of snow; hence we can expect improvements in snow albedo retrievals from future hyperspectral satellite missions. • We compared five MODIS-derived products in mountainous basins in the western US • Products derived using spectral mixture analysis were most accurate • NASA standard products MOD10A1 and MCD43A3 generally underesXmated snow albedo • Results were consistent in analyses using in situ staXon data and airborne surveys

  PublisherDOI

• Merging Regional Climate Models and Remote Sensing Observations to Better Estimate Mountain Snow Water Equivalent

  2025-09-29

  preprintOpen access

  Large-scale, high-resolution estimation of snow water equivalent (SWE) in mountainous areas is challenging. Two approaches currently deployable at continental scale are SWE­­ reconstruction and regional climate model (RCM) simulation. We present a method (Blender) that computes SWE timeseries from RCM estimates and remote sensing of snow cover fraction (SCF). Blender is a variational data assimilation (DA) method that minimizes differences between prior (RCM) and posterior (Blender) precipitation and energy balance, constrained by SCF. Blender leverages the beneficial qualities of RCMs and reconstruction and can be applied as a post-processing step to any snow model simulation. We test in the Tuolumne watershed in the Sierra Nevada, USA, for water years 2013, 2016, and 2017 (low, average, and high snow accumulation, respectively) at 500 m spatial resolution. We validate against SWE measurements from 18 Airborne Snow Observatory (ASO) flights and compare with results from a SWE dataset of similar spatial resolution, the Western United States Snow Reanalysis (WUS-SR) dataset. Compared to ASO, Blender improves the spatial SWE pattern over the prior while comparing well with WUS-SR (average spatial RMSE of 11.5% for Blender, 17.0% for WRF, 9.5% for WUS-SR). Comparing average basin snow water storage to observations from ASO, Blender SWS absolute bias is improved in 15/18 flights vs WRF, and is comparable with WUS-SR in the melt season. This method enables large-scale estimates of mass and energy fluxes and storages applicable to mountain regions at a finer spatial resolution than the climate model estimates.

  PublisherOA PDFDOI

• A data fusion approach to produce daily, high resolution snow albedo using multispectral and hyperspectral imagery

  2024-03-09 · 1 citations

  preprintOpen accessCorresponding

  Snow albedo data are required for various research and applications at a wide range of spatial and temporal scales. Typically, spatially-distributed snow albedo measurements are generated using multispectral satellite data, including MODIS, Sentinel-2, and Landsat imagery. While a number of algorithms can be employed to create snow albedo products from multispectral satellite imagery, a recent MODIS-focused analysis shows that spectrally-based approaches result in the most accurate snow albedo. These approaches use spectral libraries of snow, vegetation, and rock reflectance to solve for snow fraction, grain size, and the impact of light absorbing particles (LAP) on snow albedo; snow albedo is estimated by combining the grain size with darkening due to LAP.Spectral unmixing algorithms produce more accurate snow albedo measurements when applied to hyperspectral data because the additional spectral information removes ambiguities associated with sparser multispectral imagery. Various airborne sensors and satellite missions EnMAP, EMIT, and PRISMA provide hyperspectral data with spatial resolutions on the order of tens of meters, but depending on the platform have repeat periods between 8-29 days, and may miss important albedo changes related to early season snow accumulation and late season dust events.In this presentation, we show initial results from a data fusion approach to produce daily snow albedo data at high spatial resolutions using multispectral and hyperspectral imagery. Our model fuses snow albedo measurements directly instead of reflectance data to take advantage of the improved ability of the spectral unmixing algorithm to address mixed pixels and better discern clouds from snow. To demonstrate our approach, we train a random forest model on snow albedo measurements generated from airborne hyperspectral data at 50 m resolution. Predictor variables include daily, 463 m MODIS snow albedo generated using a spectral unmixing algorithm, as well as terrain characteristics and solar illumination. The fused snow albedo data take advantage of the more accurate and finer resolution hyperspectral data will maintaining the daily temporal resolution of multispectral MODIS imagery. Additionally, our fusion approach is flexible and can incorporate snow albedo measurements from additional airborne or satellite sensors, including multispectral VIIRS data and hyperspectral data from the upcoming SBG and CHIME satellite missions.

  PublisherDOI

• Examining the Effects of Climate Change on Managed Lowland Rivers in the California Bay Delta Watershed

  HydroShare Resources · 2024-04-09

  datasetOpen accessSenior author
  PublisherDOI

• Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra

  2024-07-25 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Abstract. Key to the success of spaceborne missions is understanding snowmelt in our warming climate, having implications for nearly 2 billion people. An obstacle is that surface reflectance products over snow show an erroneous hook that often shows sharp decreases in the visible wavelengths. This hook is sometimes mistaken for soot or dust but can result from three artifacts: 1) a background reflectance that is too dark; 2) an assumption of level terrain; 3) or differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions currently being implemented address these artifacts.

  PublisherDOI

• Atmospheric Controls on River Temperature: Sensitivity of River Temperature Downstream of a Dam to Changes in a Mediterranean Climate

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Evaluating the effects of climate change on managed lowland rivers in the California Bay Delta Watershed

  Journal of Hydrology · 2024-11-19 · 3 citations

  articleOpen accessSenior author

  Water that is released from reservoirs can affect the downstream thermal regimes of rivers. During the summer months, these flow releases can lower the river temperature downstream of dams in an extension that mainly depends on the volume and temperature of these releases and the energy exchange with the overlying atmosphere. The benefit of this cooling effect has been suggested as an approach to mitigate the effects of climate change in downstream-regulated rivers. However, anticipated climate change conditions may weaken these cooling benefits, especially in managed lowland rivers (MLRs), as they are subjected to large withdrawals, are shallow, and convey clear water. Here, we show that MLRs in the California Bay Delta Watershed are vulnerable to water temperature increases, especially during future summer months subjected to a future high-emission greenhouse scenario. Low-flow conditions exacerbate this vulnerability, especially at locations downstream of high-flow diversions. By using a physical energy balance model (FLUVIAL-EB) paired with a downscaled climate regional model (CRCM5-RCP8.5), we found that for summer months between 2030 and 2100, longwave and latent heat fluxes will contribute to water temperature increases, while absorbed solar radiation will likely decrease under future climate scenarios. Despite the warming effects of climate change on MLRs in the California Bay Delta Watershed, our findings suggest that increasing the release of hypolimnetic water from reservoirs during summer months can be a viable solution to mitigate the river temperature increase. • Climate change will impact managed lowland rivers in summer during low flows. • Reservoir releases may limit river warming over short distances under climate change. • Energy balance model assessed climate warming in managed lowland rivers.

  PublisherDOI

• Sensitivity of River Temperature Downstream of a Dam to Mechanisms in a Warming Mediterranean Climate

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

Recent grants

• Sintering in Snow and the Possible Role of Soluble Impurities

  NSF · $150k · 2006–2010

• Rapid Quantitative Snow Stratigraphy for Avalanche Forecasting Using Near-Infrared Photography

  NSF · $34k · 2010–2014

• Science Plan of the WATer and Environmental Research Systems Network (WATERS Network)

  NSF · $900k · 2008–2010

Frequent coauthors

• T. H. Painter

  89 shared

• Jiancheng Shi

  China Agricultural University

  76 shared

• Karl Rittger

  Institute of Arctic and Alpine Research

  59 shared

• Robert E. Davis

  55 shared

• Edward H. Bair

  53 shared

• Helmut Rott

  Environmental Earth Observation Information Technology (Austria)

  33 shared

• Dar A. Roberts

  The Ohio State University

  27 shared

• N. P. Molotch

  24 shared

Labs

• Bren School of Environmental Science & ManagementPI

Education

• PhD, Geography

  University of Michigan

  1973

• B.A., Geography

  California State University East Bay

  1968

Awards & honors

• 2009 Jim Gray Award from Microsoft
• 2010 Nye Lecturer for the Cryosphere group of the American G…
• NASA’s William T. Pecora award for scientific excellence and…
• NASA’s Public Service Medal
• Fellow of the American Geophysical Union