Abstract
The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the relationship between LAI and climatic variable rainfall and Growing Degree Days (GDD) on the basis of data of a cold semi-arid region in Southwest Iran. For this purpose, monthly rainfall and temperature data were collected from ground stations between 2003 and 2015; LAI data were obtained from MODIS for the same period. The best relationship for predicting the monthly LAI values was selected from a set of single- and two-variable candidate models by considering their statistical goodness of fit (correlation coefficients, Nash-Sutcliffe coefficients, Root Mean Square Error and mean absolute error). Although various forms of linear and nonlinear relationships were tested, none showed a statistically meaningful relationship between LAI and rainfall for the study area. However, a two-variable nonlinear function was selected based on an iterative procedure linking rainfall and GDD to the expected LAI. By taking advantage of map algebra tools, this relationship can be used to predict missing LAI data for time series simulations. It is also concluded that the relationship between MODIS LAI and modeled LAI on basis of climatic variables shows a higher correlation for the wet season than for dry season.
Highlights
With progress in satellite Earth observation, remote sensing (RS) plays a major role in determining and monitoring the status of vegetation as by the use of Vegetation Indices (VI) [1,2]
The relationship between Leaf Area Index (LAI) and rainfall/Growing Degree Days (GDD) was investigated with example data from the BeheTshhte-AreblaadtionBsahsiipn,bewtwheicehn LisAItahned lraarignefastll/bGaDsiDn winas CinhvaehsatirgmataehdalwiathndexaBmakphletiadraitapfrroovmintchee, Bsoeuhtehswhte-Astbearnd BIraasnin(,Fwighuicrhe i1s)t.hTehlaergbeasstinbadsirnaiinnsCahnahaarrema aohfaal banoudtB3a8k6h0tiakrmi p2 rtoovitnhcee,GsoreuatthwKeasrtoeurnn Idrraanin(aFgigeusryes1te)m
When GDD starts to increase from early April to early October, rainfall shows minimum value
Summary
With progress in satellite Earth observation, remote sensing (RS) plays a major role in determining and monitoring the status of vegetation as by the use of Vegetation Indices (VI) [1,2]. VI is a type of multi-band spectral transformation with two or more reflective bands. These indices are designed to enhance the contribution of vegetation properties by providing more reliable spatial data about canopy structural variations and spatial-temporal inter-comparisons of terrestrial photosynthetic activities [3]. LAI controls many physical and biological processes associated with plant, water, soil and energy fluxes [13] It affects the soil-water balance via its control on the interception and transpiration [13,14,15] and plays a critical role in natural systems and their ecophysiological, hydrological, ecological and meteorological processes [16,17]
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