Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

Haibo Wang,Mingguo Ma,Liying Geng,Xin Li

doi:10.3390/rs11030225

Abstract

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

Highlights

Drylands, including arid and semi-arid ecosystems, cover 30%–45% of the Earth’s land surface [1,2], and play an important role in the global carbon cycle and future carbon sequestration [3,4]
The eight-day eddy covariance (EC) flux tower gross primary production (GPP) (GPP_obs) was compared with the results of MOD17A2H GPP (GPP_MODIS), GPP simulated with the in situ meteorology forcing data (GPP_Insitu), and GPP simulated with optimized maximum light-use efficiency (LUE) parameter (GPP_LUEopt) and with optimized all five parameters (GPP_Fiveopt)
By contrast, when we optimized the maximum LUE parameter (Figure 3c), a significant improvement of model performance for all sites was seen, with R2 = 0.86, root mean squared error (RMSE) = 1.01 gC/m2/day, rRMSE = 6.99%, and the slope of the regression lines was closer to the 1:1 line, which signifies the importance of the LUE parameter in GPP modelling

Summary

Introduction

Drylands, including arid and semi-arid ecosystems, cover 30%–45% of the Earth’s land surface [1,2], and play an important role in the global carbon cycle and future carbon sequestration [3,4]. Satellite remote sensing provides continuous and temporally repetitive observation of land surfaces and has advanced tremendously over the past few decades that has become a useful tool in estimating the terrestrial ecosystem production across broad temporal and spatial scales. Production efficiency models (PEMs), developed for predicting global GPP with remote sensing, have been widely used to quantify the spatial and temporal variation of terrestrial ecosystem productivity [8,9,10]. There is a need to understand how well commonly used remote sensing models capture the magnitude and inter-annual variability of measured CO2 exchange [13]

Objectives

Methods

Results

Discussion

Conclusion