Abstract
A promising low-cost solution for monitoring spectral information, e.g., on agricultural fields, is that of wireless sensor networks. In contrast to remote sensing, these can achieve more continuous monitoring due to their long-term deployment and are less impacted by the atmosphere, making them a promising solution for the calibration of satellite data. In this paper, we explore an alternative approach for processing data from such a network. Hyperspectral sensors were found to be too complex for such a network. While previous work considered fusing the data from different multispectral sensors in order to derive hyperspectral data, we shift the assessment of the hyperspectral modeling in a separate preprocessing step based on machine learning. We then use the learned data as additional input while using identical multispectral sensors, further reducing the complexity of the sensors. Despite requiring careful parametrization, the approach delivers hyperspectral data of similar and in some cases even better quality.
Highlights
The continuous monitoring of plants at high spatial and temporal resolution is a crucial component of making agriculture more efficient and thereby preparing it for an increasing world population
In the remote sensing community, this was expressed more formally with vegetation indices such as the VisibleBand Vegetation Index (VDVI), Normalized Green-Red Difference Index (NGRDI), and the Normalized Green-Blue Difference Index (NGBDI) [5] which are based on a red, a green and a blue band or a subset thereof
Distributed Compressive Sensing (DCS), or Joint Sparsity Models (JSMs)-1, is mostly used as described in [2], i.e., with a firstdegree differential matrix for both the common part as well as the innovation signals which represent the difference between the common part and the individual nodes.Using such a differential matrix means assuming sparsity in the derivative of the spectrum which is a good assumption because the spectra are relatively smooth
Summary
The continuous monitoring of plants at high spatial and temporal resolution is a crucial component of making agriculture more efficient and thereby preparing it for an increasing world population. Such information is mainly collected by remote sensing or when agricultural machines drive on the field. The steep increase in reflection at approximately 700 nm is known as the red edge [7] It has been used in the calculation of another vegetation index, the Normalized Difference Red Edge Index (NDRE) [8]
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