Next Article in Journal
Online Global Land Surface Temperature Estimation from Landsat
Next Article in Special Issue
Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning
Previous Article in Journal
Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks
Previous Article in Special Issue
Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1207;

Spatial-Temporal Simulation of LAI on Basis of Rainfall and Growing Degree Days

Faculty of Natural Resources and Earth Sciences, Kashan University, KM 6 BLVD. Ghotb Ravandi, Kashan 8731753153, Iran
National Centre for Groundwater Research and Training, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
Faculty of Natural Resources and Earth Sciences, Shahrekord University, BLVD. Rahbar, Shahrekord 8815648456, Iran
All authors contributed equally to this manuscript.
Author to whom correspondence should be addressed.
Received: 11 October 2017 / Revised: 1 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
Full-Text   |   PDF [4323 KB, uploaded 24 November 2017]   |  


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. View Full-Text
Keywords: LAI estimation; growing degree days; rainfall; remote sensing; Behesht-Abad basin LAI estimation; growing degree days; rainfall; remote sensing; Behesht-Abad basin

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Davoodi, E.; Ghasemieh, H.; Batelaan, O.; Abdollahi, K. Spatial-Temporal Simulation of LAI on Basis of Rainfall and Growing Degree Days. Remote Sens. 2017, 9, 1207.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top