Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

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.

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

“…Optical imagery from one satellite system could supplement the imagery from another system to address this problem. Previous studies have analyzed the performance of such conjunction of imagery from different platforms, for example, Landsat-7 and Landsat-8 [16], MODIS and Landsat-8 [17], as well as Landsat-8 and Sentinel-2 [18][19][20][21], and finally, Landsat-7, Landsat-8, and Sentinel-2 combined [22]. Similarly, the present study exploits the possibility of conjoint use of imagery acquired by the Sentinel-2 and the new Vegetation and Environment monitoring on a New MicroSatellite (VENµS) satellite, which has similar spectral bands in the visual, near infrared spectral region, and a 5-10 m spatial resolution (depending on the Collection) as Sentinel-2 in addition to a very high temporal resolution of two days [23].…”

Characterizing bracken fern phenological cycle using time series data derived from Sentinel-2 satellite sensor

“…EVI2 was also used based on its sensitivity to coherent inter-band (blue, red and NIR) atmospheric correction and thus may become much better over extreme bright or dark surfaces, such as subpixel clouds, desert playas, and inland water bodies, where the EVI values are usually problematic [ 60 ]. Additionally, EVI2 has also been reported to solve resolve Leaf area Index (LAI) differences for vegetation with different background soil reflectance [ 61 ]. The NDVI and EVI2 indices were calculated using the 108 Sentinel-2A and 2B satellite images in the TerrSet Geospatial Monitoring and Modelling System (Version 18.21) software based on the following equations: where NIR, red and blue represents the quantity of NIR, red and blue light reflected by vegetation and measured by the satellite sensor [ 62 ], 2.5 is the gain or scaling factor; 6 and 7.5 are coefficients of the aerosol resistance term while 1 represents the canopy background adjustment for correcting the nonlinear, differential NIR and red radiant transfer through a canopy.…”

Digital image processing methods for estimating leaf area of cucumber plants

“…Nondestructive leaf area estimation with a high accuracy rate and in a small running time is critical in many specific fields of plant growth modelling (e.g., plant physiological and ecological experiments [43]). Leaf area may be used for leaf area index calculation [9], [44] which is likely to model vegetation productivity and photosynthetic capacity [45], [46]. Moreover, LA is a very useful parameter in plant growth and morphological studies of the plants [47].…”