Mr. Omer Perach

Leaf area index (LAI) is an important plant biophysical trait that has a key role in plant development and is essential in the response of vegetation to climate change. LAI monitoring can improve the understanding of plant nutritional and health status and to assess potential stress and damages. In agriculture, LAI development is positively related to biomass accumulation and eventually has a direct impact on final yield. Chickpea, one of the most cultivated legumes worldwide, sowed in Israel during the wet season while yield accumulates later in the season, thus drought can impose substantial penalty on final yield. The current study aimed to estimate Chickpea LAI using Sentinel-2 spectral data. Six commercial Chickpea fields were monitored throughout the growing season of 2022, four fields at the south and two fields at the north of Israel. LAI data was retrieved in 219 points using ACCUPAR LP-80 ceptometer. Canopy reflectance was acquired by Sentinel-2 (9 bands, 20-meter spatial resolution, 490-2190 nm). Multiple linear regression model was derived to estimate LAI based on field measurements and Sentinel-2. Best subset selection approach was used for variable selection on the training dataset. Seven-variable model [Sentinel-2 bands 2 (blue), 3 (green), 5-7 (rededge), 11 (SWIR1), 12 (SWIR2)] resulted in the lowest root mean square error (RMSE) of the independent validation dataset (33%) 1.15 (LAI values ranged from 0.653 to 7.92) with R2 of 0.66 (train RMSE and R2 resulted in 0.82 and 0.85, respectively). Chickpea LAI estimation is feasible using simple regression approach