Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau

Abstract

Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.

Assessing meadow carbon cycling using digital repeat photography and Eddy Covariance Techniquesm

“…The mismatch in timing between peak GCC and peak GPP also confounds the statistics in the growth phase relationship between GCC and GPP. This was also found in the Japanese alpine meadow case (Zhou et al, 2020).…”

“…In both meadows studied here (Table 6 and 7) and the Japanese alpine meadow (Zhou et al 2020), growth phase models were statistically weaker compared with senescence phase models. One reason for this could be that we have fewer data for growth phase than senescence phase, because the growth phase (or the measurement period) was shorter.…”

“…Later, as the LAI and biomass continued to increase GCC increased by a small amount compared with GPP. In an alpine meadow site in Japan, GCC reached its peak 20-30 days after GPP peaked, indicating a significant lag time between peak greenness detected remotely and the actual carbon uptake by plants (Zhou et al 2020).…”

“…The reason for separation of phases, was to separate the phenological changes of vegetation based on meadow phenological pattern (phenophase). The growth phase characterizes the spring vegetation "green-up" and the senescence phase characterizes the autumnal senescence or "green-down" (Richardson et al, 2009;Zhou et al, 2020). Figure 16 shows the relationship between GCC and GPP (a) and GCC and NEP (b) separated into the two phases.…”

“…The late start to measurements in both meadow cases in this study means GCC was already quite high, and varied over the period by only a few percentages, while GPP changed considerably. Zhou et al (2020), had a similar imbalance in data length. The mismatch in timing between peak GCC and peak GPP also confounds the statistics in the growth phase relationship between GCC and GPP.…”