Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Abstract

Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations

“…NDVI and EVI are perhaps the most widely used VIs for monitoring vegetation conditions and estimating GPP (Dong et al., ; Sims et al., ; Sjöström et al., ; Xiao & Moody, ; Xiao et al., ). The newly proposed near‐infrared reflectance of vegetation (NIR v ), the product of total scene NIR reflectance and NDVI, has been shown to be better related to GPP than NDVI or NIR alone (Badgley, Field, & Berry, ).…”

Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach

“…It is frequently obtained from remote sensing data using vegetation indices (VIs) [16][17][18][19][20]. Some authors use the VIs as proxies of both ε and f APAR and rewrite Equation (1) as GPP = VI × VI × PAR [21,22]. In the latter case, both broad-band VIs and narrow-band indices can be considered.…”

Canopy and physiological controls of GPP during drought and heat wave

“…Dong et al . [] suggested that remote sensing data‐driven models that do not include water limitation factors performed much worse during drought periods. However, even for the models discussed above which consider water stress, their performances are not satisfied [ Liu et al , ; Schaefer et al , ].…”