Abstract
Abstract. DESIS products offer information-rich images, but these data are rarely acquired for most sites. We explored how these image “jewels” fit within dense image time series of higher spatial resolution but lower spectral resolution data to enhance the characterization of the land surface phenologies exhibited by montane pastures in central Kyrgyzstan. We used surface reflectance data at 5 m spatial resolution from the French-Israeli VENμS mission over the NARYN intensive observation site from 2019 and 2020. Upon evaluating the quality of the DESIS data, we found substantial geolocation problems in multiple images and limitations in some of the Quality-2 masks. Here we have reported on these problems and focused analysis on the DESIS images from 2020 that substantially overlap the NARYN footprint, occurred during the growing season, and had sufficiently low cloud cover to enable comparisons. We calculated multiple vegetation indices enabled by the spectral resolution of the VENμS bands, having averaged the DESIS bands to correspond to the VENμS bands. Of the 12 ground locations we had sampled in July 2021, just four locations had sufficient DESIS data in 2020 to characterize the growing season. Comparison of the higher spatio-temporal resolution of the VENμS data with the lower spatio-temporal resolution of the DESIS data revealed that DESIS can capture the broad outlines of land surface phenology and spatial heterogeneity of the landscape with responses comparable to VENμS, but it could not reveal the finer temporal resolution of transhumance dynamics. DESIS data merit further analysis in other highland pasture areas.
Highlights
Rural communities rely on montane pastures to support the agro-pastoralist livelihoods that form the foundation of rural economies in Kyrgyzstan, Tajikistan, and other areas of montane Central Asia
We explore how DESIS data can enhance characterization of the spatial heterogeneity of land surface phenologies in montane pastures by leveraging a high spatial and temporal resolution dataset from the French-Israeli VENμS mission, which is available over a limited area in eastern Naryn oblast of central Kyrgyzstan from 2018-2020
We calculated four vegetation indices to facilitate the comparison of VENμS and DESIS data: (1) NDVI for reference; (2) anthocyanin index (Gitelson et al, 2006), which is sensitive to plant stress; (3) red-edge chlorophyll index tuned for erectophile canopies (Clevers and Gitelson, 2013), which is more sensitive than NDVI to canopy differences; and (4) chlorophyll/carotenoid index for MODIS bands (Gamon et al, 2016; Wang et al, 2020), which is sensitive to seasonal dynamics in coniferous canopies
Summary
Rural communities rely on montane pastures to support the agro-pastoralist livelihoods that form the foundation of rural economies in Kyrgyzstan, Tajikistan, and other areas of montane Central Asia. The recent literature on montane pasture degradation in Central Asia has focused on coarser spatial resolution imagery (Dubovyk et al, 2016; Kulikov et al, 2016; Wang et al, 2020; Zhumanova et al, 2018). We explore how DESIS data can enhance characterization of the spatial heterogeneity of land surface phenologies in montane pastures by leveraging a high spatial and temporal resolution dataset from the French-Israeli VENμS mission, which is available over a limited area in eastern Naryn oblast of central Kyrgyzstan from 2018-2020. We are interested whether DESIS can provide a visualization of the pasture landscape that is comparable to that imaged by the much higher spatial resolution VENμS data
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