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

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 (R 2 = 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 (R 2 = 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.

Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

“…For example, estimated mean start of season, maximum, and end of season NDVI values show good agreement with eMODIS-derived data, with Pearson's correlation, and mean absolute errors (MAEs) ranging from 0.72 to 0.98 and from 0.02 to 0.08, respectively. Estimated timings followed similar patterns, with HLS-derived metrics generally indicating earlier start of season, maximum, and end of season timings as compared to eMODIS which is generally consistent with previous interpretations [32]. For comparison, Zhou et al [44] used a weighted least-squares approach for temporal smoothing of HLS data and a delayed moving average technique to estimate the start of season date in grasslands along the eastern border of North Dakota and western Minnesota, with much higher data densities as compared to this study, and found fair agreement with MODIS-derived date estimates (RMSE = 11.6 days).…”

“…We used a regression tree modeling framework (similar to [32]) for the spatial estimation of weekly NDVI values derived from HLS data acquired from 2016 to 2018. This framework relies on: (1) the generation of spatial inputs (e.g., weekly HLS composites, week of year, and year raster data) to serve as covariates within regression tree models; (2) extraction of model tuning, training, and testing data from raw NDVI image composites; (3) hyperparameter optimization (i.e., number of rules and committees); and (4) application of calibrated and validated models to predict NDVI at each pixel and time step (e.g., weekly), as described in more detail below.…”

“…where d is the day of year the target scene t was acquired, n is the day of year the adjacent scenes (2-3 week search window) were acquired, and q is the likelihood of an unobstructed pixel as derived from the See5 mask confidence, such that q is near 1 and 0 for clear and non-clear pixels, respectively. This approach differs slightly from [32] because of data availability issues present in our larger spatiotemporal domain. An NDVI composite was then constructed as:…”

Status of Phenological Research Using Sentinel-2 Data: A Review

“…The green up, or start of season (SOS), dates estimated from the VIIRS-HLS fused data had a closer agreement, i.e., mean absolute deviation (MAD), of 8 days with the in situ USA-NPN dates than with HLS (MAD of 14 days). Pastick et al (2018) [62] also pointed out that NDVI, SWIR, blue, green and NIR bands in Sentinel-2 and Landsat-8 are highly consistent (R 2 > 0.90) in HLS data. A review of the literature on forest mapping reveals that such studies focused on the use of multi-temporal images representing different periods within the growing season to map broad forest types and their seasonality, rather than focusing on specific phenological stages [63][64][65][66][67].…”

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

“…The fire detection map derived from Landsat-8 was influenced by an artificial effect related to problems with HLS cloud masking [42]. The Landsat-8 cloud detection algorithm (HLS version 1.3) sometimes confused bright targets with clouds, then dilated the target in the Landsat-8 cloud mask, resulting in anomalous square-shaped patterns of change in the Landsat-8 data.…”