Paper Info
Title
Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing
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 R-2 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 x 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.
Year
DOI
Venue
2015
10.3390/rs70100808
REMOTE SENSING
Keywords
Field
DocType
field measurement,hyperspectral,satellite calibration,radiometer,California,HyspIRI
Biomass,Leaf area index,Transect,In situ,Crop,Remote sensing,Multispectral image,Hyperspectral imaging,Photosynthetically active radiation,Geology
Journal
Volume
Issue
Citations 
7
1
3
PageRank 
References 
Authors
0.43
2
2
Name
Order
Citations
PageRank
michael marshall192.41
Prasad S. Thenkabail221030.03