Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site
- 1Geological Survey of Denmark and Greenland (GEUS), Øster Voldgade 10, 1350 Copenhagen K, Denmark
- 2Instituto de Economía, Geografía y Demografía, Centro de Ciencias Humanas y Sociales, Consejo Superior de Investigaciones Científicas (CSIC), Albasanz 26–28, 28037, Madrid, Spain
- 3Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350, Copenhagen K, Denmark
- 4Associated Research Unit GEOLAB2 & 6
- 5Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Científicas (CSIC), 14080 Córdoba, Spain
- 6Department of Geography and Geology, University of Alcalá, Calle Colegios 2, 28801, Alcalá de Henares, Spain
Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones.
Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.