Correlated change in normalized difference vegetation index and the seasonal trajectory of photosynthetic capacity in a conifer stand

Abstract

Seasonal changes in canopy photosynthetic activity play an important role in carbon assimilation. However, few simulation models for estimating carbon balances have included them due to scarcity in quality data. This paper investigates some important aspects of the relationship between the seasonal trajectory of photosynthetic capacity and the time series of a common vegetation index (normalized difference vegetation index, NDVI), which was derived from on site micrometeorological measurements or smoothed and downscaled from satellite-borne NDVI sensors. A parameter indicating the seasonality of canopy physiological activity, P E , was retrieved through fitting a half-hour step process model, PROXEL NEE , to gross primary production (GPP) estimates by inversion for carboxylation and light utilization efficiencies. The relative maximum rate of carboxylation (V rm ), a parameter that indicates the seasonality of CO 2 uptake potential under prevailing temperature, was then calculated from P E and daily average air temperature. Statistical analysis revealed that there were obvious exponential relationships between NDVI and the seasonal courses for both canopy physiological activities P E and V rm . Among them, the on-site broadband NDVI provided a robust and consistent relationship with canopy physiological activities (R 2 50.84). The relationships between satellite-borne NDVI time series with instantaneous canopy physiological activities at the time of satellite passing were also checked. The results indicate that daily step NDVI time series (data downscaled from composite temporal resolution NDVI) better represent the daily average activity of the canopy. These findings may enable us to retrieve the seasonal course of canopy physiological activity from widely available NDVI data series and, thus, to include it into carbon assimilation models. However, both smoothing methods for satellite-borne NDVI time series may generate incorrect estimates and must be treated with care.

Close relationship between spectral vegetation indices and V_cmax in deciduous and mixed forests

“…Studies by Wang et al (2008aWang et al ( , 2009) and Jin et al (2012) revealed noticeable inconsistencies in the relationship between V cmax and VIs in certain periods, for example, V cmax increasing with increasing VIs in spring and then decreasing or remaining steady with further increase of VIs. Stronger relationships might be obtained by allowing for hysteresis or asymmetric V cmax ÁVI relationships.…”

“…Research has shown that remotely sensed reflectance can provide a useful data source to sense photosynthetic capacity because of the linkage of reflectance with canopy photosynthesis (Gamon et al, 1995;Carter, 1998;Choudhury, 2001;Dobrowski et al, 2005). A variety of vegetation indices have been used that can be related to photosynthetic capacity, notably the normalised difference vegetation index (NDVI), and the enhanced vegetation index (EVI) (Wang et al, 2009;Jin et al, 2012;Muraoka et al, 2012;Houborg et al, 2013).…”

Gross primary production simulation in a coniferous forest using a daily gas exchange model with seasonal change of leaf physiological parameters derived from remote sensing data

“…Among them, Scenario 2 performs best. This is because the broadband NDVI has a closer relationship with canopy physiology than its remote sensing counterparts (Wang et al 2009). Even so, the satellite-borne NDVI time-series can still provide useful information on the seasonality of canopy physiology and improve the daily model performance, as indicted in table 1.…”

“…Since the analysis in Wang et al (2009) was based on relative daily average V rm , the parameter a 1 was obtained by multiplying parameter a in Wang et al (2009) with species-specific V m,25 , while parameter b 1 was kept the same as b in Wang et al (2009). Therefore, seasonal changes in canopy physiology were retrieved for the first time in this paper from commonly available NDVI time-series.…”

“…, then the canopy-level daily GPP is estimated as: GPP c, day~G PP sun LAI sun zGPP shade LAI shadeWang et al (2009) investigated the relationships between NDVI from various sources and canopy physiological activity through the seasonal courses at this site. The NDVI sources include broadband NDVI and satellite-borne NDVI.…”