A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera

Autor: Qingjiu Tian, Huanhuan Yuan, Zhenhai Li, Chengquan Zhou, Guijun Yang, Jibo Yue, Haikuan Feng, Xiuliang Jin
Přispěvatelé: Beijing Research Center for Information Technology In Agriculture, Partenaires INRAE, Nanjing Agricultural University, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), China Academy of Chinese Medicinal Sciences, National Key Research and Development Program 2016YFD0300603, Natural Science Foundation of China 41601346,41601369,41501481,61661136003,41771370,41471285,41471351
Jazyk: angličtina
Rok vydání: 2018
Předmět:
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences
business.product_category
010504 meteorology & atmospheric sciences
Science
crop height
0211 other engineering and technologies
photo numérique
02 engineering and technology
01 natural sciences
modèle de surface
modèle de culture
crop surface model
aboveground biomass
LAI
random forest regression
partial least squares regression
Partial least squares regression
Leaf area index
donnée hyperspectrale
biomasse aérienne
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Digital camera
Mathematics
Remote sensing
2. Zero hunger
Spectrometer
régression aleatoire
Hyperspectral imaging
dégradation de la forêt
15. Life on land
Agricultural sciences
Random forest
modèle de récolte
General Earth and Planetary Sciences
RGB color model
Snapshot (computer storage)
business
Sciences agricoles
Zdroj: Remote Sensing
Remote Sensing, MDPI, 2018, 10 (7), pp.1138. ⟨10.3390/rs10071138⟩
Remote Sensing, Vol 10, Iss 7, p 1138 (2018)
Remote Sensing; Volume 10; Issue 7; Pages: 1138
Remote Sensing 7 (10), 1138. (2018)
ISSN: 2072-4292
DOI: 10.3390/rs10071138⟩
Popis: Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.
Databáze: OpenAIRE
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