About

Mark Friedl is the William Goodwin Aurelio Professor of Mathematics and Science at Boston University’s Earth & Environment department. His research utilizes remote sensing to examine biogeophysical patterns and processes at the Earth’s surface. He is particularly interested in how land cover and ecosystem properties influence surface climate, how land surface biophysics affect the Earth’s weather and climate system, and how human activities are shaping the planet and impacting the global biosphere. Friedl teaches courses including Micrometeorology: Energy & Mass Transfer at Earth’s Surface, Multivariate Analysis for Geographers, and Advanced Topics in Remote Sensing. He holds a Ph.D. in Geography from the University of California, Santa Barbara, an M.A. in Geography from the same institution, and a B.Sc. (Honors) in Physical Geography from McGill University.

Research topics

• Environmental science
• Remote sensing
• Geology
• Computer Science
• Geography
• Ecology
• Forestry
• Cartography
• Atmospheric sciences
• Biology

Selected publications

• Global Land cover Mapping and Estimation (GLanCE): a record of land cover and land cover change at Landsat scale across decades

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• MONITORING SALT MARSH CHANGE IN THE VIRGINIA COAST RESERVE USING LANDSAT TIME SERIES

  Estuarine Coastal and Shelf Science · 2026-05-01

  articleSenior author
  PublisherDOI

• Supplementary material to "Tracking vegetation phenology across diverse biomes using Version 3.0 of the PhenoCam Dataset"

  2025-03-28

  preprintOpen access
  PublisherDOI

• Global agricultural lands in the year 2015

  Earth system science data · 2025-07-22 · 3 citations

  articleOpen accessCorresponding

  Abstract. While there are many global geospatial datasets representing the extent of agriculture, they predominantly represent croplands. Only a couple of global data products represent the full global agricultural footprint, including both cropland and pastures. Our own research team's most recent complete publicly available agricultural land cover dataset, including both croplands, and pastures, represents circa 2000. These data, distributed on a graticule of 5 arcmin (∼ 10 km2 at the Equator), have been integrated into a considerable number and diversity of research studies, modelling, data science, and media applications. Further, users of these data have been interested in them for studying a variety of issues such as land use, food security, climate change, and biodiversity loss. Here we present an updated dataset on the global distribution of agricultural lands (cropland and pasture) circa 2015 (15 years on since the initial study). Past studies that have constructed such datasets have been one-off exercises that have been infrequently repeated due to the amount of effort required. Therefore, in this work, we developed a transparent and reproducible approach to update our data product while also enabling easier reproduction of future datasets. We distribute our 2015 product at the same resolution and formats as the prior product, and accompany it with a full set of replicable code and data for reconstruction. In this article we explain how the data was constructed, with links to the permanent DOIs where the data can be readily downloaded by the user community (Mehrabi et al., 2024; https://doi.org/10.5281/zenodo.11540553).

  PublisherOA PDFDOI

• Climate Change Is Altering Ecosystem Water Use Efficiency in Water‐Limited Ecosystems

  Global Change Biology · 2025-08-01 · 6 citations

  articleSenior author

  ABSTRACT Dryland ecosystems are expected to expand globally as a result of rising atmospheric water demand and vapor pressure deficit. However, the nature and magnitude of how water‐limited ecosystems are adapting to increases in aridity is unclear. Here we examine changes in ecosystem water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET), in global water‐limited regions over the past two decades. Our analysis uses remotely sensed data, process‐based models, and reanalysis datasets to quantify changes in WUE and examine the role that changes in atmospheric CO 2 , atmospheric water demand, and soil moisture exert on WUE dynamics in water‐limited ecosystems. Our results show that on average WUE increased by 17% in water‐limited regions worldwide. Asia, North America, and Africa showed the largest increases in WUE (24%, 17%, and 17%, respectively), followed by Europe, South America, and Oceania (15%, 10%, and 9%, respectively). Ecosystems with low mean annual WUE showed the largest increases of WUE. CO 2 fertilization from increasing atmospheric CO 2 concentrations was the dominant driver behind observed changes in WUE, especially in the Northern Hemisphere. Our findings indicate that vegetation in water‐limited ecosystems is adapting to climate change by optimizing water use efficiency but also suggest that the ability of many ecosystems to adapt may decrease as they become drier.

  PublisherDOI

• Bernstein Polynomial Processes for Continuous Time Change Detection

  ArXiv.org · 2025-04-24

  preprintOpen access

  There is a lack of methodological results for continuous time change detection due to the challenges of noninformative prior specification and efficient posterior inference in this setting. Most methodologies to date assume data are collected according to uniformly spaced time intervals. This assumption incurs bias in the continuous time setting where, a priori, two consecutive observations measured closely in time are less likely to change than two consecutive observations that are far apart in time. Models proposed in this setting have required MCMC sampling which is not ideal. To address these issues, we derive the heterogeneous continuous time Markov chain that models change point transition probabilities noninformatively. By construction, change points under this model can be inferred efficiently using the forward backward algorithm and do not require MCMC sampling. We then develop a novel loss function for the continuous time setting, derive its Bayes estimator, and demonstrate its performance on synthetic data. A case study using time series of remotely sensed observations is then carried out on three change detection applications. To reduce falsely detected changes in this setting, we develop a semiparametric mean function that captures interannual variability due to weather in addition to trend and seasonal components.

  PublisherOA PDFDOI

• Tracking vegetation phenology across diverse biomes using Version 3.0 of the PhenoCam Dataset

  2025-03-28 · 2 citations

  preprintOpen access

  Abstract. Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, GCC), and how it varies in space and time. These phenological cameras (i.e., “PhenoCams”) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (cameraNDVI), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both GCC and cameraNDVI showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, GCC time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both GCC and cameraNDVI provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at https://doi.org/10.3334/ORNLDAAC/2364 (Ballou et al., 2025), and the summarized phenology data are available at https://doi.org/10.3334/ORNLDAAC/2389 (Zimmerman et al., 2025).

  PublisherDOI

• Cross-scalar analysis of multisensor land surface phenology

  Remote Sensing of Environment · 2025-01-31 · 6 citations

  articleSenior authorCorresponding
  PublisherDOI

• Asymmetric Spring and Autumn Phenology Control Growing Season Length in Temperate Deciduous Forests

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-22

  preprintOpen access

  Abstract Forest phenological responses to climatic variation among species and populations across broad spatial scales remain poorly understood. Here, we quantify four decades of phenological dynamics for 10 major deciduous tree species by compiling a unique dataset that integrates high spatial resolution remote sensing with extensive field inventory data across the Northeastern and Midwestern United States. We found that spring and autumn phenology impose asymmetric controls on growing season length, with spring regulating interannual variation and autumn driving long-term trends. Spring phenology across all species was primarily controlled by temperature, while autumn phenology was influenced by spring phenology and an interacting suite of species-specific environmental factors. Our study demonstrates the promise of combining high resolution remote sensing with forest inventory data to investigate phenological dynamics over large regions. More importantly, our results provide new insights into how species-specific sensitivities to environmental drivers regulate growing season length in temperate forests.

  PublisherOA PDFDOI

• Asymmetric Spring and Autumn Phenology Control Growing Season Length in Temperate Deciduous Forests

  Research Square · 2025-11-26 · 2 citations

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Continental-Scale Monitoring, Modeling and Forecasting of Phenological Responses to Climate Change

  NSF · $276k · 2011–2017

• Collaborative Proposal: MSB-FRA: Improved Understanding of Feedbacks between Ecosystem Phenology and the Weather-Environment Nexus at Local-to-Continental Scales

  NSF · $396k · 2017–2023

• Collaborative Research: WSC-Category 3: Crops, climate, canals, and the cryosphere in Asia - changing water resources around the Earth's third pole

  NSF · $224k · 2010–2015

Frequent coauthors

• Crystal Schaaf

  60 shared

• Alan H. Strahler

  Boston University

  55 shared

• Andrew D. Richardson

  Northern Arizona University

  49 shared

• Xiaoyang Zhang

  Zhejiang University of Technology

  45 shared

• Steve Frolking

  University of New Hampshire

  44 shared

• Damien Sulla‐Menashe

  Indigo Information Services (United States)

  40 shared

• Koen Hufkens

  Interactions Sol Plante Atmosphère

  37 shared

• Curtis E. Woodcock

  35 shared

Labs

• Land Cover & Surface Climate GroupPI

Education

• Ph.D., Geography

  University of California Santa Barbara

  1993

• B.Sc., Geography

  McGill University

  1985