Simple models to predict grassland ecosystem C exchange and actual evapotranspiration using NDVI and environmental variables

Publication date: 15 February 2018
Source:Agricultural and Forest Meteorology, Volume 249
Author(s): Stephen J. Del Grosso, W.J. Parton, Justin D. Derner, Maosi Chen, Compton J. Tucker
Semiarid grasslands contribute significantly to net terrestrial carbon flux as plant productivity and heterotrophic respiration in these moisture-limited systems are correlated with metrics related to water availability (e.g., precipitation, Actual EvapoTranspiration or AET). These variables are also correlated with remotely sensed metrics such as the Normalized Difference Vegetation Index (NDVI). We used measurements of growing season net ecosystem exchange of carbon (NEE), NDVI from eMODIS and AVHRR, precipitation, and volumetric soil water content (VSWC) from grazed pastures in the semiarid, shortgrass steppe to quantify the correlation of NEE with these driving variables. eMODIS NDVI explained 60 and 40% of the variability in daytime and nighttime NEE, respectively, on non-rain days; these correlations were reduced to 41 and 15%, respectively, on rain days. Daytime NEE was almost always negative (sink) on non-rain days but positive on most rain days. In contrast, nighttime NEE was always positive (source), across rain and non-rain days. A model based on eMODIS NDVI, VSWC, daytime vs. nighttime, and rain vs. non-rain days explained 48% of observed variability in NEE at a daily scale; this increased to 62% and 77%, respectively, at the weekly and monthly scales. eMODIS NDVI explained 50–52% of the variability in AET regardless of rain or non-rain days. A model based on eMODIS NDVI, VSWC, Potential EvapoTranspiration (or PET), and rain vs. non-rain days explained 70% of the observed variability in AET at a daily scale; this increased to 90 and 96%, respectively, at weekly and monthly scales. Models based on AVHRR NDVI showed similar patterns as those using eMODIS, but correlations with observations were lower. We conclude that remotely-sensed NDVI is a robust tool, when combined with VSWC and knowledge of rain events, for predicting NEE and AET across multiple temporal scales (day to season) in semiarid grasslands.



from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2B7CWXO