Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Alejandro Flores,Jessica Mitchell,Nancy Glenn,Peter Olsoy

doi:10.3390/rs9100981

Abstract

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems.

Highlights

Water-limited ecosystems cover approximately 40% of Earth’s terrestrial surface and, despite lower primary productivity than forested systems on a per unit land area basis, exert significant controls on global water, energy, and biogeochemical cycles (e.g., [1,2])
(2) We evaluated how Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) prediction error varied with (a) the rate of phenological change in a growing season, (b) vegetation functional type (c) topographic aspect, and (d) the presence of seasonal snowpacks
We found that the images synthesized using STARFM yielded accurate predictions of normalized difference vegetation index (NDVI), when compared with a null model consisting of the temporally nearest Landsat observation, for five of the eight dates

Summary

Introduction

Water-limited ecosystems cover approximately 40% of Earth’s terrestrial surface and, despite lower primary productivity than forested systems on a per unit land area basis, exert significant controls on global water, energy, and biogeochemical cycles (e.g., [1,2]). Spatiotemporal patterns of vegetation in water-limited ecosystems are complex, and arise as a function of interacting abiotic and biotic processes that exert influence across a large range of spatial and temporal dimensions. Spatial variability in terrestrial vegetation at hillslope scales (e.g., 10 s to 100 s of m) in these ecosystems is both influenced by and can reveal important patterns in surface water, energy, erosion, and biogeochemical cycling (e.g., [8,9]). Biomass in water-limited ecosystems (or proxies such as remotely sensed greenness) can exhibit relatively rapid temporal variability controlled, among other things, by phenology; disturbances such as fire [13], water stress, and insect infestation [14]; and atmospheric teleconnections that can produce rare but large precipitation events [15]

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