Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS

Abstract

Climate change is predicted to alter the canopy phenology of temperate and boreal forests, which will affect carbon, water, and energy budgets. Therefore, there is a great need to evaluate remotely sensed products for their potential to accurately capture canopy dynamics. The objective of this study was to compare several products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to field measurements of fraction photosynthetically active radiation (FPAR) and plant area index (PAI) for a deciduous broadleaf forest in northern Wisconsin in 2002. MODIS products captured the general phenological development of the canopy although MODIS products overestimated the leaf area during the overstory leaf out period. Field data suggest that the period from budburst to canopy maturity, or maximum PAI, occurred in 10 to 12 days while MODIS products predicted onset of greenness and maturity from 1 to 21 days and 0 to 19 days earlier than that from field observations, respectively. Temporal compositing of MODIS data and understory development are likely key factors explaining differences with field data. Maximum PAI estimates differed only by 7% between field derived and MODIS-based estimates of LAI. Implications for ecosystem modeling of carbon and water exchange and future research needs are discussed.

Relating seasonal dynamics of enhanced vegetation index to the recycling of water in two endorheic river basins in north-west China

“…Fig. 3a; Ahl et al, 2006) in an area that can potentially support many species. Ground-based analysis of land-surface phenology, in contrast, focusses on a single plant species at a time.…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…Interpretation of such metrics is straightforward for land areas with a dominant vegetation type or a common phenology, but can be complex for areas with horizontal (surface cover) or vertical (understory and overstory) variation in vegetation type and phenology [41][42][43][44]. In the mixed-composition landscapes of the Southwest, the observed time signal may be composed of one or more discrete and potentially unique signals attributable to the individual grass, shrub, tree and succulent components of the landscape, with their respective contributions modulated by their fractional surface cover within the pixel.…”

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

“…It was originally developed for monitoring crop growth based on field measurements (e.g., Richards, 1959;Ratkowsky, 1983) and adopted to simulate temporal satellite vegetation index (Zhang et al, 2003). It has then been applied to investigate seasonal vegetation growth using webcam data (Richardson et al, 2006;Kovalskyy et al, 2012), Landsat TM data (e.g., Fisher et al, 2006;Kovalskyy et al, 2011), AVHRR data (e.g., Zhang et al, 2007), and MODIS data (e.g., Zhang et al, 2003Zhang et al, , 2006Ahl et al, 2006;Liang et al, 2011). Moreover, studies have shown that the sigmoidal model performance is superior to both Fourier functions and asymmetric Gaussian functions for dictping remotely sensed phenology (Beck et al, 2006).…”