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

Neal Pastick,Bruce Wylie,Zhuoting Wu

doi:10.3390/rs10050791

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.

Highlights

Drylands occupy approximately 41% of Earth’s land surface and are currently home to over 38% of the world’s population [1]
We address the following questions: (1) Are surface reflectance data from Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data comparable in this region? (2) Can Landsat-8 OLI and Sentinel-2 MSI data be used in conjunction to accurately characterize seasonal vegetation index signatures within dryland environments? (3) To what extent does excluding MSI data impact our ability to effectively monitor land-surface phenology in drylands? This research fills a critical gap in the understanding of current monitoring capabilities of arid and semi-arid environments that cover a substantial portion of the globe
The overall accuracy for both decision tree masking models (i.e., OLI and MSI) was above 99%, with similar commission and omission error rates, despite that lack of cirrus and thermal bands in MSI data (Table 2). These accuracy metrics far exceed those of the Harmonized Landsat-8 Sentinel-2 (HLS) mask layers, as calculated from a subset of these interpretations made on three dates of imagery for each sensor, where OLI and MSI masks had an overall accuracy of 76% and 89%, respectively (Table S1)

Summary

Introduction

Drylands occupy approximately 41% of Earth’s land surface and are currently home to over 38% of the world’s population [1]. Drylands exert significant controls on socio-environmental systems, including global energy, water, and carbon cycles [2]. Enhanced warming has promoted dryland expansion throughout portions of the world [3], by increasing evaporative demand and decreasing soil moisture, and strongly modifies the capacity of dryland ecosystems to sequester carbon through vegetation shifts and warming effects on photosynthesis and decomposition [4]. Because vegetation dynamics exert a strong control on water, energy, and biogeochemical budgets in drylands [6], knowledge of land-surface phenology is vital for effectively monitoring dryland ecosystems and is important for both the research community and the policy community in the management of Earth’s landscapes. Land-surface phenology is defined as the spatiotemporal development of vegetated land surfaces as imaged by spaceborne sensors

Results

Discussion

Conclusion