Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard

Xiaoliang Lu,Shuqing An,Yuyu Zhou,Jianwu Tang,Yaling Liu,Zhunqiao Liu

doi:10.3390/rs10060932

Abstract

We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn—the two most important metrics—can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.

Highlights

Vegetation phenology characterizes timing of leaf development, senescence and abscission
We compare phenological transition dates derived from three reflectance-based indices: GCC90, MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) (NDVIMODIS) and the ground-based NDVI sensor (NDVIGround)
GCC90 demonstrates a more rapid change in both rising and falling parts of the seasonal trajectory (Figure 2c) and it shows a clear peak at the end of spring, which can be explained by seasonal variations in foliage pigments [15]

Summary

Introduction

Vegetation phenology characterizes timing of leaf development, senescence and abscission. Changes in vegetation phenology may in turn alter land ecosystem productivity and seasonal variations in CO2 fluxes and the energy balance [6,7] Phenology metrics such as start-of-season (SOS) and end-of-season (EOS) can be derived from either satellite or near surface remote sensing observations. Logistic functions [13] are used to fit time series observations If both visible and infrared band data are available, the Normalized Difference Vegetation Index (NDVI) [16], a spectral vegetation index (VI), is most widely used to characterize the seasonal dynamics of vegetation. Both field measured NDVI and satellite remotely sensed NDVI have shown their potential to monitor phenology at different spatial scales. Yang et al [18] used ground-based NDVI time series to assess the impacts of structural and biochemical changes on the phenological patterns

Methods

Results

Discussion

Conclusion