How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

To examine the effect of nitrogen (N) fertilization on forest carbon (C) sequestration and water loss, we used an artificial neural network model to estimate C fluxes and evapotranspiration (ET) in response to N fertilization during four post-fertilization years in a Pacific Northwest chronosequence of three Douglas-fir stands aged 61, 22 and 10 years old in 2010 (DF49, HDF88 and HDF00, respectively). Results showed that N fertilization increased gross primary productivity (GPP) for all three sites in all four years with the largest absolute increase at HDF00 followed by HDF88. Ecosystem respiration increased in all four years at HDF00, but decreased over the last three years at HDF88 and over all four years at DF49. As a result, fertilization increased the net ecosystem productivity of all three stands with the largest increase at HDF88, followed by DF49. Fertilization had no discernible effect on ET in any of the stands. Consequently, fertilization increased water use efficiency (WUE) in all four post-fertilization years at all three sites and also increased light use efficiency (LUE) of all the stands, especially HDF00. Our results suggest that the effects of fertilization on forest C sequestration and water loss may be associated with stand age and fertilization; the two younger stands appeared to be more efficient than the older stand with respect to GPP, WUE and LUE.

Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges.

The growing degree day (GDD) model and the growing season index (GSI) model are two common approaches used in various land surface models (LSMs) for simulating phenophases. The capacity of these two models for simulating phenolphases was evaluated by coupling them to a LSM (DLM: Dynamic Land Model) and validated by observation data from the 22 selected eddy covariance flux towers representing six typical plant functional types. The main findings are threefold: (i) the simulated phenophases using DLM-GSI were much closer to the observations derived from the green chromatic coordinate data than using DLM-GDD. The start of the growing season (SGS) was estimated to be earlier by DLM-GSI and later by DLM-GDD. Meanwhile, the end of growing season (EGS) was estimated to be later by DLM-GSI and earlier by DLM-GDD; (ii) compared to the GDD model, the GSI model significantly decreased the absolute bias of the phenophases simulated by DLM for all sites. The DLM-GSI model simulated biases for SGS and EGS decreased by 48.2 % and by 39 % on average, respectively; and (iii) the accuracy of modeled GPP using the DLM-GSI model is much higher than using the DLM-GDD model for all sites. The DLM-GSI model reduced the root mean square error of simulated GPP by 8.0 % and increased the corresponding index of agreement by 7.5 %.