Abstract
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances.
Highlights
With the aim of managing plant needs in a more efficient way, precision agriculture has arisen as a rush of technological enhancements to classical farm management tools [1,2]
A general overview of the PAIe f f measurements obtained during the field campaign shows that the range of PAIe f f values obtained using all three instruments is according to the values reported in the literature for rice [29,52]
The results presented in this work bring to light the good performance of a brand new smartphone mobile app called PocketLAI for effective plant area index acquisitions over rice
Summary
With the aim of managing plant needs in a more efficient way, precision agriculture has arisen as a rush of technological enhancements to classical farm management tools [1,2]. Detailed geo-spatial information on plant and soil properties is essential knowledge in crop management In this context, remote sensing has become a very efficient tool for precision farming of large areas through data acquired by sensors on-board satellite platforms [3], airborne imagery [4], and unmanned aerial vehicles (UAVs) [5]. Remote sensing has become a very efficient tool for precision farming of large areas through data acquired by sensors on-board satellite platforms [3], airborne imagery [4], and unmanned aerial vehicles (UAVs) [5] In this framework, rice cultivation is one of the most extended land uses for food production worldwide and has been the main objective of many studies using optical [6,7]. LAI has been used in agricultural and remote sensing studies [12,13], including precision agriculture [14], and is regarded as a key input in global models of ecosystem, hydrology, climate, ecology, biogeochemistry, and productivity [15]
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