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Continuous estimation of canopy leaf area index (LAI) and clumping index over broadleaf crop fields: An investigation of the PASTIS-57 instrument and smartphone applications
- Fang, Hongliang, Ye, Yongchang, Liu, Weiwei, Wei, Shanshan, Ma, Li
- Agricultural and forest meteorology 2018 v.253-254 pp. 48-61
- canopy, corn, crops, data collection, developmental stages, grain sorghum, leaf area index, measuring devices, photography, remote sensing, seasonal variation, soybeans, telephones, transmittance, vegetative growth, China
- Automatic leaf area index (LAI) measurements are important for obtaining sufficient amounts of field data over an extended period of time. A seasonal field campaign was carried out to obtain continuous LAI measurements over maize, soybean, and sorghum fields in northeast China in 2016. Field LAI measurements were acquired with the automatic PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensed from 57°) instrument and two smartphone applications, PocketLAI and LAISmart. These measurements were compared with data obtained using the LAI-2200 Plant Canopy Analyzer, digital hemispherical photography (DHP), and destructive sampling measurements.The effective plant area index (PAIeff) estimates from LAI-2200 and DHP are consistent over the season, with the overall relative errors (RE) of less than 5%. The PASTIS-57 data exhibit a small underestimation of the LAI-2200 and DHP values (RE < 20%). The relative errors for the LAISmart data are between −20% and −30%. PocketLAI significantly underestimates the LAI-2200 values (RE > 40%) and saturates at around PAIeff = 3.5. The canopy clumping index (CI) exhibits an S-shaped seasonal variation that decreases with the increase of PAIeff during the vegetative growth stage but increases after this stage. PASTIS-57 shows great potential for obtaining continuous LAI measurements in agricultural crop fields, but the smartphone applications should be further examined before they can be used for research purposes. The data collected in this study are valuable for the validation of remote sensing products.