Abstract
Abstract. Commercially available proximal sensors are being used in precision agriculture to provide non-destructive, real-time spatial information on 'green biomass' that may be of interest to the remote sensing community. The sensors are described as biomass sensors, but questions remain on which canopy characteristics can be best estimated by the sensor measurements. In this study Normalized Difference Vegetation Index (NDVI) measurements from active optical sensors were examined across multiple datasets, representing different active optical sensors, different years, and different sites for wheat and barley. The NDVI values were compared with spatially and temporally coincident measurements of fractional green cover and above ground biomass, expressed as dry matter in kg ha-1. Direct comparison of NDVI measurements from the different sensors for the same plots over a range of canopy cover demonstrates differences for plot means of NDVI. Canopy fractional green cover values were well described by NDVI using a linear model. Using all of the datasets, the linear regression of fractional green cover on NDVI yielded an r2 of 0.71 and a standard error of 0.12. NDVI measured by the sensors did not explain as much of the variance in dry matter as for fractional cover. Dry matter was related to NDVI using a non-linear model. For all sensors, sites and dates with green biomass, the model was fitted with r2 value of 0.27, and standard error of 2133 kg ha-1. The relationship between NDVI and dry matter was nearly linear at levels of biomass less than 1000 kg ha-1. Results for these datasets indicate that the active optical sensors may be a useful surrogate for fractional cover, but not for above ground biomass.
Highlights
Available proximal sensors are being used in precision agriculture to provide non-destructive, real-time spatial information on ‘green biomass’
In this study Normalized Difference Vegetation Index (NDVI; Rouse et al 1973) measurements from active optical sensors were examined across multiple datasets, representing different active optical sensors, different years, and different sites for wheat and barley grown under rainfed conditions
Given that the measurements were made over the growing season, these results would indicate systematic differences in the NDVI values generated by the sensors
Summary
Available proximal sensors are being used in precision agriculture to provide non-destructive, real-time spatial information on ‘green biomass’. These active optical sensors utilise built-in light sources, pulsed to differentiate the artificial signal from sunlight. This gives these sensors a tremendous advantage over passive optical sensors (such as ground-based spectrometers) in that they can be used under any sky conditions, or even in complete darkness. There is a relevance to the remote sensing community in that these sensors could potentially provide ground truth on fractional ground cover and/or plant biomass. In this study Normalized Difference Vegetation Index (NDVI; Rouse et al 1973) measurements from active optical sensors were examined across multiple datasets, representing different active optical sensors, different years, and different sites for wheat and barley grown under rainfed conditions
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