Abstract
Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.
Highlights
One of the most fundamental vegetation biophysical parameters is the Leaf Area Index (LAI), defined as a dimensionless measure of the one-sided leaf area (m2) per unit ground surface area (m2) [1,2]
The present work aims to perform a comparative analysis between LAI generated from three methods: existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the Sentinel Application Platform (SNAP) biophysical model, and L2A THEIA product from Sentinel-2
Our analysis shows that the relation between the ground measured LAI and the measured height of the potato crop is essentially linear with an R2 value of 0.82, greater than that obtained when regressing the crop height to LAI obtained from Model 7 (EVI2, row crops; R2 = 0.48) (Figure 10)
Summary
One of the most fundamental vegetation biophysical parameters is the Leaf Area Index (LAI), defined as a dimensionless measure of the one-sided leaf area (m2) per unit ground surface area (m2) [1,2]. Remote sensing has proved to be a promising alternative tool for estimating crop LAI quickly over large areas, and without damaging the canopy [13,14]. The retrieval of crop biophysical variables from remote sensing falls into two categories: empirical and physical modeling approaches. The simplest method of estimating LAI from remote sensing is by establishing an empirical relationship between the remotely sensed vegetation indices (VIs) and measured LAI, referred to as the LAI-VI approach [16,17]. Vegetation indices are computed based on the reflectance in two or more spectral bands and reflect biophysical characteristics of the plant canopy such as greenness, biomass, and LAI [18,19]. Correlations have been computed using various mathematical forms, such as linear, logarithmic, polynomial, or exponential functions of VIs [5,24]
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