Assessment of leaf area index derived from the harmonized Landsat and Sentinel-2 surface reflectance-based vegetation indices and crop height in semi-arid irrigated landscapes

Authors: MOURAD, R. - AMERICAN UNIVERSITY OF BEIRUT; JAAFAR, H. - AMERICAN UNIVERSITY OF BEIRUT; Anderson, Martha; Gao, Feng

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/16/2020
Publication Date: 9/23/2020
Citation: Mourad, R., Jaafar, H., Anderson, M.C., Gao, F.N. 2020. Assessment of leaf area index derived from the harmonized Landsat and Sentinel-2 surface reflectance-based vegetation indices and crop height in semi-arid irrigated landscapes. Remote Sensing. 12(19):3121. https://doi.org/10.3390/rs12193121.
DOI: https://doi.org/10.3390/rs12193121

Interpretive Summary: Biomass in crops, grasslands and forests is often quantified in terms of the Leaf Area Index (LAI), or total leaf area per unit ground area. As such, LAI is an important input to modeling of crop development, carbon and water fluxes, and crop water requirements. The ability to map LAI accurately using satellite remote sensing data was tested over an agricultural production site in the Bekaa Valley of Lebanon, taking advantage of a new image timeseries created by harmonizing acquisitions from the U.S. Landsat and European Sentinel-2 satellites. Several existing relationships, both empirical and physically based, were compared to identify the best approaches and satellite band combinations for mapping LAI for a wide range of vegetables, grains, herbs, potato and tobacco crops. In comparison with extensive ground-based observations, it was found that the combined Landsat-Sentinel timeseries performed better than either satellite in isolation and that an index generated from this combined timeseries outperformed other band combinations tested. The results from this study will inform operational efforts for monitoring crops in semi-arid irrigated regions using remote sensing

Technical 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 reflectances 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 LAI and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. Also, crop-specific height and above-ground biomass relationships with LAI were investigated. Results show that, of the empirical VI relationships tested, the EVI2-based HLS models statistically performed best, specifically the LAI models originally developed for wheat (RMSE:1.27), for maize (RMSE:1.34), and for row crops (RMSE:1.38). LAI derived through ESA’s SNAP biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm produced an acceptable accuracy level compared to HLS-EVI2 Models (RMSE of 1.38) but with significant underestimation at high LAI. Our findings show that LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other indices for the crops 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 heterogenous landscapes.