Assessing MODIS GPP in Non-Forested Biomes in Water Limited Areas Using EC Tower Data

Abstract

Although shrublands, savannas and grasslands account for 37% of the world's terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to help bridge this gap. In this study, the agreement between the MODIS GPP product (GPPm) and the GPP Eddy Covariance tower data (GPPec) was tested for six different sites in temperate and dry climatic regions (three grasslands, two shrublands and one evergreen forest). Results of this study show that for the non-forest sites in water-limited areas, GPPm is well correlated with GPPec at annual scales (r ), although it tends to overestimate GPP and it is less accurate in the sites with permanent water restrictions. The use of biome-specific models based on precipitation measurements at a finer spatial resolution than the Data Assimilation Office (DAO) values can increase the accuracy of these estimations. The seasonal dynamics and the beginning and end of the growing season were well captured by GPPm for the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. The agreement between GPPec and GPPm in non-forested OPEN ACCESSRemote Sens. 2015, 7 3275 sites was lower on a weekly basis than at an annual scale (0.44 ≤ r 2 ≤ 0.49), but these results were improved by including meteorological data at a finer spatial scale, and soil water content and temperature measurements in the model developed to predict GPPec (0.52 ≤ r 2 ≤ 0.65).

Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China

“…However, the MODIS product was found to have the tendency to generally overestimate GPP for low productivity and to underestimate it for high productivity in North America, compared to ecosystem-level measurements of GPP at eddy covariance flux towers from AmeriFlux and Fluxnet-Canada [15,41,42]. Moreover, MODIS GPP overestimates GPP in a deciduous forest in Korea [43], three subtropical forest ecosystems [41] and non-forest areas in North America [44]. The underestimation in GPP was associated with the low estimation of light use efficiency, especially for cropland [15,21,45,46].…”

Interplay of drought and tropical cyclone activity in SE U.S. gross primary productivity

“…In particular, clay‐rich soils show smaller GPP increases with LAI compared to other soil types. The lack of sensitivity to water stress in the MODIS algorithm results in GPP estimates that do not capture well the beginning and end of the growing season, as well as abrupt changes in CO 2 fluxes caused by water limitations, and is well documented in the literature [ Álvarez‐Taboada et al , ; Zhang et al , ; Verma et al , ; Gebremichael and Barros , ; Turner et al , ].…”

Unveiling Trends and Hotspots in Air Pollution Control: A Bibliometric Analysis

“…Furthermore, the top 10 clusters identified in the study on air pollution control encompassed various aspects, such as clean air (#0), PM 2.5 concentration (#1), diesel emissions (#2), an air quality prediction model system (#3), mercury salt (#4), IMED model (#5), argon-oxygen decarbonization (#6), health benefits (#7), Global Land actual EvAPOtranspiration dataset (#8), and meteorological conditions (#9). Specifically, Cluster 3 (air quality prediction model system), Cluster 5 (IMED model), and Cluster 8 (Global Land EvAPOtranspiration dataset) were found to be closely associated with the analysis of mechanisms for controlling air pollution [43][44][45]. In terms of regional integrated air pollution control strategies, Cluster 0 (clean air), Cluster 1 (PM 2.5 concentration), and Cluster 9 (meteorological conditions) were considered [46][47][48][49][50].…”