Abstract
Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.
Highlights
Precision agriculture, as a crop management practice based on differential resource application at highly resolved space and time, requires readily available information about the crop status at a resolution that corresponds to its goals
A visual inspection of the WDVI performance suggested that a standardization analogous to normalized difference vegetation index (NDVI) would improve its correlation to Leaf area index (LAI), and a normalized WDVI (NWDVI) was created using Eq 1: NDWVI = WDVI∕(B8 + B4)
This vegetation indices (VI) is almost equivalent to the widespread NDVI, except for a soil correction factor that can be directly extracted from the target image
Summary
As a crop management practice based on differential resource application at highly resolved space and time, requires readily available information about the crop status at a resolution that corresponds to its goals. Precision Agriculture (2022) 23:1449–1472 defined as the amount of (the upper side) of foliar area per unit of projected ground area, is one of the crop biophysical parameters that convey the most valuable information on plant status. LAI being a parameter with typical light interception and reflectance properties (Zheng & Moskal, 2009) and a dominant surface area component, it is suitable to be characterized and measured by remote sensing. LAI quantification through remote sensing, offers a unique opportunity to incorporate measured reference values of this biophysical parameter into modeling routines to improve the accuracy of model predictions, in a process called data assimilation (Huang et al, 2019; Jin et al, 2018)
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