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Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

1
Stinger Ghaffarian Technologies, Inc., contractor to the U.S. Geological Survey, Sioux Falls, SD 57198, USA
2
Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA
3
U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA
4
U.S. Geological Survey, National Land Imaging Program, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 791; https://doi.org/10.3390/rs10050791
Received: 11 April 2018 / Revised: 4 May 2018 / Accepted: 17 May 2018 / Published: 19 May 2018
(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands)
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Abstract

Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling. View Full-Text
Keywords: Landsat 8; Sentinel 2; Harmonized Landsat-8 Sentinel-2 (HLS); MODIS; time series analysis; phenology; data mining Landsat 8; Sentinel 2; Harmonized Landsat-8 Sentinel-2 (HLS); MODIS; time series analysis; phenology; data mining
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pastick, N.J.; Wylie, B.K.; Wu, Z. Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems. Remote Sens. 2018, 10, 791.

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