Relationship between MODIS EVI and LAI across time and space

Abstract

The Leaf Area Index (LAI) is used as input in hydrological and biochemical models for the estimation of water-cycle characteristics, agricultural primary production and other processes. Vegetation Indices (VIs) are used to monitor vegetation state and health. Considering that easily computed VIs can be used for the estimation of LAI, this study applied a regression analysis between MODIS Enhanced Vegetation Index (EVI) and LAI data in five sites around the world. A linear model was found to provide a good description of the LAI-EVI relationship across all examined vegetation types and times. Medium accuracy models were improved when variability of time and vegetation type were considered, indicating that these parameters highly affect the LAI-EVI relationship. Sensitivity of EVI to LAI was higher in periods of high biomass production. Regression analysis between LAI-EVI showed a stronger relationship for the study sites characterized by dry and warm tropical climatic conditions.

Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

“…In agriculture, LAI is essential for, among others, monitoring the variability in crops and rangelands productivity, crop stress and health, biomass, phenology, and yield estimation (Mulla 2013;Cho, Ramoelo, and Dziba 2017;Novelli et al 2019). Unfortunately, traditional direct LAI (in-situ) measurement methods are spatially and temporally limited, expensive, time-consuming, labour-intensive, and destructive (Alexandridis et al 2013). Therefore, remotely sensed effective LAI (hereafter, LAI) provides a promising alternative for operational agricultural monitoring to support the implementation of global and regional food security mandates such as the United Nations Sustainable Development Goals (UN-SDGs) and Agenda 2063.…”

“…However, the validation of SNAP-derived LAI from Sentinel-2 data, as well as its consistency with existing LAI products, were explored by few studies (Bochenek et al 2017;Campos-Taberner et al 2018). This is indispensable since accurate and reliable agricultural monitoring strongly hinges on the consistency and inter-comparability of biophysical parameters such as LAI (Alexandridis, Ovakoglou, and Clevers 2019). Furthermore, uncertainties related to the effect of the spatial resolution and processing level of the data on derived biophysical parameters such as LAI need further assessment, especially in Africa, where such studies are rare.…”

Downscaling of MODIS leaf area index using landsat vegetation index

“…The MODIS LAI-EVI relationship was examined through regression analysis, taking into account both the characteristics of seasonality (Day of Year, DOY) and the vegetation type (T. K. Alexandridis, Ovakoglou, & Clevers, 2019). The regression analysis was applied using a linear fit (y = a + b * x) for each available pair of a MODIS LAI and EVI image of the same period and for each study area, while the data were grouped per vegetation type.…”

“…After the application of the regression analysis, the equations that connected MODIS LAI with MODIS EVI per date and vegetation type were generated for each test site. An example of the regression analysis results for the study area of Umbeluzi on DOY 193-2013 can be found in Table 6, while a detailed presentation of all results is available at T. K. Alexandridis et al (2019). In all vegetation types of Umbeluzi, the probability value (p) was <0.0001.…”

Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data

“…A number of studies on various vegetation types have led to the general conclusion that spectral vegetation indices may be considerably sensitive to LAI/FPAR changes [55][56][57][58]. The normalized difference vegetation index (NDVI) is one of the most extensively used vegetation indices related to LAI/FPAR [57].…”