Exploration of scaling effects on coarse resolution land surface phenology

Abstract

Numerous land surface phenology (LSP) datasets have been produced from various coarse resolution satellite data and different detection algorithms from regional to global scales. In contrast to field-observed phenological events that are defined by clearly evident organismal changes with biophysical meaning, current approaches to detecting transitions in LSP only determine the timing of variations in remotely sensed observations of surface greenness. Since activities to bridge LSP and field observations are challenging and limited, our understanding of the biophysical characteristics of LSP transitions is poor. Therefore, we set out to explore the scaling effects on LSP transitions at the nominal start of growing season (SOS) by comparing detections from coarse resolution data with those from finer resolution imagery. Specifically, using a hybrid piecewise-logistic-model-based LSP detection algorithm, we detected SOS in the agricultural core of the United States-central Iowa-at two scales: first, at a finer scale (30 m) using reflectance generated by fusing MODIS data with Landsat 8 OLI data (OLI SOS) and, second, at a coarser resolution of 500 m using Visible Infrared Imaging Radiometer Suite (VIIRS) observations. The VIIRS SOS data were compared with OLI SOS that had been aggregated using a percentile approach at various degrees of heterogeneity. The results revealed the complexities of SOS detections and the scaling effects that are latent at the coarser resolution. Specifically, OLI SOS variation defined using standard deviation (SD) was as large as 40 days within a highly spatially heterogeneous VIIRS pixel; whereas, SD could be b10 days for a more homogeneous set of pixels. Furthermore, the VIIRS SOS detections equaled the OLI SOS (with an absolute difference less than one day) in N60% of OLI pixels within a homogeneous VIIRS pixel, but in b 20% of OLI pixels within a spatially heterogeneous VIIRS pixel. Moreover, the SOS detections in a coarser resolution pixel reflected the timing at which vegetation greenup onset occurred in 30% of area, despite variation in SOS heterogeneities. This result suggests that (1) the SOS detections at coarser resolution are controlled more by the earlier SOS pixels at the finer resolution rather than by the later SOS pixels, and (2) it should be possible to well simulate the coarser SOS value by selecting the timing at 30th percentile SOS at the finer resolution. Finally, it was demonstrated that in homogeneous areas the VIIRS SOS was comparable with OLI SOS with an overall difference of b5 days.

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

“…Our results also hint at a systematic bias toward earlier timing of maturity and senescence of grass and shrub/scrub cover types ( Figures 5 and 6), which is not caused by inadequate density of observations in either time series but rather the enhanced capacity for tracking phenology using moderate resolution data than has been previously possible with coarser resolution data (e.g., MODIS). This finding agrees with those of references [18,36], where phenological transition dates (i.e., green-up, start-of-season, and maturity) extracted from Landsat time series were generally earlier than those from derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) phenocurves. Others have also found a two-week lag when comparing MODIS NDVI to flux tower gross photosynthesis in rangeland ecosystems [37].…”

Shifting relative importance of climatic constraints on land surface phenology

“…an increase in predominantly moisture limited areas over the study period ( figure S3). Finally, it should be noted that our 50% threshold for excluding croplands is unlikely to exclude the effects of crop phenology in the detected LSP entirely (Zhang et al 2017). Moreover, our analysis did not take into account land-use/landcover changes and fire, which may have large annual effects on land surface phenology (Forkel et al 2015).…”

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

“…The scale effects between 500 m and 4.6 km, as well as different determinations of SOS and different representations of vegetation growth cycles by NDVI and MTCI, likely explain these differences. First, the SOS at a coarse resolution was possible to simulate by selecting the date at the 30th percentile SOS at finer resolution [39], resulting in the advanced SOS at a coarse resolution. Second, the SOS in the MERIS phenology product was identified using the first derivative value from negative to positive (valley point), whereas the SOS in the GLOBMAP-IndianSOS product was determined from the date when NDVI exceeded 9.18% of VGA.…”