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Article

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

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Grassland Science and Renewable Plant Resources, Organic Agricultural Sciences, Universität Kassel, D-37213 Witzenhausen, Germany
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Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences (UAS), GKVK, Bengaluru 560065, Karnataka, India
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All-India Coordinated Research Project on Agroforestry, University of Agricultural Sciences (UAS), GKVK, Bengaluru 560065, Karnataka, India
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All-India Coordinated Research Project on Dryland Agriculture, University of Agricultural Sciences (UAS), GKVK, Bengaluru 560065, Karnataka, India
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Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiyamala, Thiruvananthapuram 695574, Kerala, India
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Centre for Ecological Economics and Natural Resources, Institute for Social and Economic Change, Dr. VKRV Rao Road, Nagarabhavi, Bengaluru 560072, Karnataka, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1771; https://doi.org/10.3390/rs11151771
Received: 25 June 2019 / Revised: 23 July 2019 / Accepted: 25 July 2019 / Published: 27 July 2019
Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016–2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R² validation (R²val) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R²val = 0.53, rRMSEP = 13.9%), maize (R²val = 0.53, rRMSEP = 18.7%) and finger millet (R²val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546–910 nm (lablab), 750–794 nm (maize) and 686–814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth. View Full-Text
Keywords: Cash crops; Hyperspectral imaging; Biomass prediction; Machine learning Cash crops; Hyperspectral imaging; Biomass prediction; Machine learning
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MDPI and ACS Style

Dayananda, S.; Astor, T.; Wijesingha, J.; Chickadibburahalli Thimappa, S.; Dimba Chowdappa, H.; Mudalagiriyappa; Nidamanuri, R.R.; Nautiyal, S.; Wachendorf, M. Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging. Remote Sens. 2019, 11, 1771. https://doi.org/10.3390/rs11151771

AMA Style

Dayananda S, Astor T, Wijesingha J, Chickadibburahalli Thimappa S, Dimba Chowdappa H, Mudalagiriyappa, Nidamanuri RR, Nautiyal S, Wachendorf M. Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging. Remote Sensing. 2019; 11(15):1771. https://doi.org/10.3390/rs11151771

Chicago/Turabian Style

Dayananda, Supriya, Thomas Astor, Jayan Wijesingha, Subbarayappa Chickadibburahalli Thimappa, Hanumanthappa Dimba Chowdappa, Mudalagiriyappa, Rama R. Nidamanuri, Sunil Nautiyal, and Michael Wachendorf. 2019. "Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging" Remote Sensing 11, no. 15: 1771. https://doi.org/10.3390/rs11151771

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