Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis

Abstract

High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Spaceborne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR GPP < SIF < VIs/VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models.

Nearly Half of Global Vegetated Area Experienced Inconsistent Vegetation Growth in Terms of Greenness, Cover, and Productivity

“…This study found that the inconsistencies in vegetation growth mainly existed in EBF, GRA, OS, WS, CRO, MF, and SAV (Figure 6); and these findings could also be inferred from previous studies (Asner et al, 2009; Dong & Sutton, 2015). Vegetation types explained this inconsistency to some extent, because of the different efficiency in using sunlight resulting from differentiated vegetation biophysical and environmental properties (Walther et al, 2018). For example, farmland had higher photosynthetic capacity than other vegetation types (Huang et al, 2018).…”

Widespread Decline in Vegetation Photosynthesis in Southeast Asia Due to the Prolonged Drought During the 2015/2016 El Niño

“…This variable can indicate the dynamic process of SIF and photosynthetic efficiency of plants when the drought occurs [29]. Since the amount of incoming photosynthetically active radiation is proportional to the total downwelling shortwave radiation, the products of shortwave radiation and EVI were used to calculate APAR [50].…”

Landscape Vegetation Productivity Influences Population Dynamics of Key Pests in Small Avocado Farms in Kenya

“…(Myrtaceae), pineapple, Ananas comosus (L.) (Bromeliaceae) and avocado in the south. Landscape characterization was based on the vegetation productivity estimated by NDVI [ 40 , 41 ]. Freely available multi-date Sentinel-2 (S2) satellite data of 10 m spatial resolution were used to create a composite image with images of wet (March to May) and dry (December to February) seasons in the year 2019.…”