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In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

Crusiol, Luís Guilherme Teixeira ; Sun, Liang ; Sun, Zheng ; Chen, Ruiqing ; Wu, Yongfeng ; Ma, Juncheng ; Song, Chenxi

Sustainability, 2022-08, Vol.14 (15), p.9039 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data
Autor: Crusiol, Luís Guilherme Teixeira ; Sun, Liang ; Sun, Zheng ; Chen, Ruiqing ; Wu, Yongfeng ; Ma, Juncheng ; Song, Chenxi
Assuntos: Agricultural practices ; Agricultural production ; Agricultural sciences ; Agriculture ; Broadband ; Canopies ; Corn ; Crop yield ; Data smoothing ; Efficiency ; Irrigation ; Irrigation scheduling ; Least squares method ; Leaves ; Moisture content ; Monitoring ; Noise ; Precision farming ; Reflectance ; Remote sensing ; Sensors ; Soil water ; Spectral sensitivity ; Spectroradiometers ; Sustainability ; Unmanned aerial vehicles ; Vegetation ; Water content ; Water deficit ; Water management ; Water relations ; Wavelengths
É parte de: Sustainability, 2022-08, Vol.14 (15), p.9039
Descrição: China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R2 from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R2 from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.
Editor: Basel: MDPI AG
Idioma: Inglês

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In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

Crusiol, Luís Guilherme Teixeira ; Sun, Liang ; Sun, Zheng ; Chen, Ruiqing ; Wu, Yongfeng ; Ma, Juncheng ; Song, Chenxi

Basel: MDPI AG

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