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Article

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content

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International Maize and Wheat Improvement Center—CIMMYT, Texcoco 56237, Mexico
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Food and Rural Development, School of Agriculture, Newcastle University, Newcastle NE1 7RU, UK
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Earth and Life Institute, Université Catholique de Louvain, Croix du Sud L5.07.16, B-1348 Louvain-la-Neuve, Belgium
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International Maize and Wheat Improvement Center—CIMMYT, Henan Agricultural University, 63 Nongye Road, Zhengzhou 450002, Henan, China
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Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), 14004 Cordoba, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 930; https://doi.org/10.3390/rs10060930
Received: 14 May 2018 / Revised: 5 June 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI—Ri, 750 to 840 nm and Rj, ±720–736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: −0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices. View Full-Text
Keywords: narrow-band indices; normalized difference spectral index; spatial-temporal variability; within-field variability; principal component analysis; time series narrow-band indices; normalized difference spectral index; spatial-temporal variability; within-field variability; principal component analysis; time series
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MDPI and ACS Style

Rodrigues, F.A., Jr.; Blasch, G.; Defourny, P.; Ortiz-Monasterio, J.I.; Schulthess, U.; Zarco-Tejada, P.J.; Taylor, J.A.; Gérard, B. Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content. Remote Sens. 2018, 10, 930. https://doi.org/10.3390/rs10060930

AMA Style

Rodrigues FA Jr., Blasch G, Defourny P, Ortiz-Monasterio JI, Schulthess U, Zarco-Tejada PJ, Taylor JA, Gérard B. Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content. Remote Sensing. 2018; 10(6):930. https://doi.org/10.3390/rs10060930

Chicago/Turabian Style

Rodrigues, Francelino A., Jr., Gerald Blasch, Pierre Defourny, J. I. Ortiz-Monasterio, Urs Schulthess, Pablo J. Zarco-Tejada, James A. Taylor, and Bruno Gérard. 2018. "Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content" Remote Sensing 10, no. 6: 930. https://doi.org/10.3390/rs10060930

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