Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

Sosdito Mananze,Isabel Pôças,Mario Cunha

doi:10.3390/rs10121942

Abstract

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

Highlights

The leaf area index (LAI) is a parameter of crop structure, which is key for many agronomic and physiologic studies involving plant growth, light interception, photosynthetic efficiency, evapotranspiration, and plant response to irrigation, fertilization, and other types of agricultural practices [1]
Remote sensing data acquired from different types of sensors and processed through various modelling approaches have been revealing a high potential for retrieving and mapping LAI, which is an important contribution to the management of agricultural fields at different spatial and temporal scales [5,6,7]
The LAI insensitive bands included in hyperspectral data require additional computational time and distort the accuracy of LAI retrieval, with this the reason why there is a need for dimensionality reduction of hyperspectral data [78]

Summary

Introduction

The leaf area index (LAI) is a parameter of crop structure, which is key for many agronomic and physiologic studies involving plant growth, light interception, photosynthetic efficiency, evapotranspiration, and plant response to irrigation, fertilization, and other types of agricultural practices [1]. Due to its role as an interface between the ecosystems and the atmosphere, studies involving LAI have applications in a wide range of fields, in agriculture, forests, ecology, hydrology, eco-physiology, and meteorology [3,4]. The principle inherent to the application of spectral information for LAI (or other biophysical variable) retrieval is related to the changes in crop spectral behavior in response to variations of physiologic and structural conditions of plants as well as to the surrounding environment conditions [8]. Through hyperspectral data it is possible to use narrow bands suited for quantifying biophysical and/or biochemical variables of vegetation [13]

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