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An Operational Multisource Land Surface Phenology Product from Landsat and Sentinel 2
Project Start Date
01/15/2018
Project End Date
01/14/2022
Grant Number
80NSSC18K0334
Solicitation
default

Team Members:

Person Name Person role on project Affiliation
Mark Friedl Principal Investigator Boston University, Boston, USA
Joshua M Gray Co-Investigator North Carolina State University, Raleigh, US
Lars Eklundh Collaborator Lund University, Lund, SE-223 62, Sweden
Thomas Maiersperger Collaborator SGT, contractor to USGS EROS, Sioux Falls, US
Minkyu Moon Postdoc Researcher Boston University, Boston, USA
Abstract

Dense time series of imagery from Landsat 8 and Sentinel 2 are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager (OLI) and the MultiSpectral Instrument (MSI) onboard Sentinel 2A and 2B, users will have access to moderate spatial resolution imagery with repeat frequencies that are more than three times higher than those available prior to the launch of Sentinel 2A. At the same time, the large data volumes and highdimensionality of blended time series from Landsat 8 and Sentinel 2 introduce substantial new challenges for users who wish to exploit these data sets. Land surface phenology (LSP) products, which synthesize the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide a simple and intuitive way to reduce data volumes and redundancy, while also furnishing rich feature sets that are useful to a wide range of applications including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. Methods to monitor and map phenology from coarse spatial resolution instruments such as MODIS are mature and operational. However, the spatial resolution of MODIS is inadequate for many applications. The goal of this proposal is to create an operational land surface phenology product based on blended time series of Landsat 8 OLI and Sentinel 2A and 2B MSI data. To explain the motivation and methodological basis for our approach, the proposal includes four main elements. First, we summarize the empirical basis and justification for our proposed product. Second, we provide a formal definition for our proposed land surface phenology product, which includes a set of Science Data Sets that: (1) identify the timing of phenophase transitions, (2) provide the user community with reduced dimensionality image data sets that capture the primary modes of multispectral and multi-temporal variability and minimize temporal correlation in image time series, and (3) identify inseason anomalies in near real-time. In this way, our proposed product goes well beyond what current coarse spatial resolution LSP products provide, and is designed to support a wide and diverse user community. Third, we describe the algorithm that will be used for this effort, which has been developed and tested over the last several years, along with the input data requirements required to generate our proposed product. Fourth, we present results from our algorithm applied to blended time series of Landsat 8 and Sentinel 2A data that demonstrate the effectiveness and accuracy of our algorithm, along with a strategy for operational product validation. For initial implementation, we propose to generate our product at continental scale for North America at 30-meter spatial resolution using the Harmonized Landsat-Sentinel (HLS) data set that is being generated by NASA, and to distribute our results via the Land Processes DAAC. To support this effort, we will collaborate with Prof. Lars Eklundh at Lund University in Sweden, one of the pioneers of land surface phenology, who is funded in Europe to develop land surface phenology algorithms based on Sentinel-2, and with whom we have an ongoing and successful collaboration.

Project Research Area

Project Documents

Year Authors Type Title
2021 Xiaoxie Gao Joshua M Gray Publications Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484
2020 Doug Bolton Joshua M Gray Publications Bolton, K.B., Gray, J.M,Melaas, E.K.,Moon,M., Eklundh, L, Friedl, M.A. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment, Volume 240, 2020, 111685
2020 Xiaoxie Gao Joshua M Gray Publications Gao, X.J., Gray, J., Coors, CW, Cook, R., Albaugh T.J., Longer greenup periods associated with greater wood volume growth in managed pine stands, Agricultural and Forest Meteorology, 297, DOI: 10.1016/j.agrformet.2020.108237
2020 Mark Friedl NASA LCLUC Science Team Presentation Moving Multi-Source Land Imaging of Seasonal Dynamics in Land Surface to Production
2019 Mark Friedl Doug Bolton NASA LCLUC Science Team Presentation Progress on Moving Multi-Source Land Imaging of Seasonal Dynamics in Land Surface to Production(Type-1)-Friedl
2019 Publications Stanimirova, R., Cai, Z., Melaas, E.K., Gray, J.M., Eklundh, L., Jonsson, P. and M.A. Friedl (2019). An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms, Remote Sensing, 11(19), 2201, https://doi.org/10.3390/rs11192201
2018 Publications Jönsson, P., Cai, Z., Melaas, E., Friedl, M.A., and L. Eklundh (2018), A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data, Remote Sensing, 10(4), 635; doi:10.3390/rs10040635