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Remote Sens. 2015, 7(1), 808-835; doi:10.3390/rs70100808

Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing

1
Climate Research Group, World Agroforestry Centre, United Nations Ave, Gigiri, P.O. Box 30677-00100, Nairobi, Kenya
2
Southwestern Geographic Center, United States Geological Survey, 2255 N. Gemini Dr, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Clement Atzberger
Received: 11 November 2014 / Accepted: 7 January 2015 / Published: 14 January 2015
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Abstract

Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands. View Full-Text
Keywords: field measurement; hyperspectral; satellite calibration; radiometer; California; HyspIRI field measurement; hyperspectral; satellite calibration; radiometer; California; HyspIRI
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Marshall, M.; Thenkabail, P. Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing. Remote Sens. 2015, 7, 808-835.

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