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STARFM

Published by Agricultural Research Service · Updated 02/15/2024

Last updated
02/15/2024
Update frequency
Not listed
Formats
Web only
Coverage
Nationwide
Federal data

About this dataset

Landsat 30m resolution observations provide sufficient spatial details for monitoring land surface and changes. However, the 16-day revisit cycle and cloud contamination have limited its use for studying global biophysical processes, which evolve rapidly during the growing season. Meanwhile, MODIS sensors aboard the NASA EOS Terra and Aqua satellites provide daily global observations valuable for capturing rapid surface changes. However, the spatial resolution of 250m to 1000m may not good enough for heterogeneous areas. To better utilize Landsat and MODIS data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed (Gao et al., 2006). The STARFM algorithm uses spatial information from fine-resolution Landsat imagery and temporal information from coarse-resolution MODIS imagery to produce estimates of surface reflectance that are high resolution in both space and time. In essence, the collection of daily MODIS imagery and seasonal Landsat imagery allows the generation of synthetic daily Landsat-like views of the Earth’s surface. The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao. Resources in this dataset: - Resource Title: STARFM. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=432&modecode=80-42-05-10 download page

Files and access

Links open the official government source

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Frequently asked questions

Who publishes STARFM?
Agricultural Research Service, part of Department of Agriculture, publishes this dataset on Data.gov.
How often is it updated?
The publisher does not list a schedule. The last update was on 02/15/2024.
How do I download it?
Use the official source link above to view or request the data.

Where this catalog comes from

Catalog checked 10/24/2026

We list dataset descriptions only. Downloads and APIs link straight to the official government source, which holds the current version and its terms of use.

Source: Data.gov · Last updated 10/24/2026 · Listing only; the data stays with the publisher

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