More recently, pixel resolution has been increased by newer satellites, such as WorldView-2 and -3 (DigitalGlobe, Longmont, CO, USA). However, there are two main problems with these platforms for precision agriculture applications, which are related to the per pixel resolution (30 m 2 per pixel for Landsat and 500 m 2 for MODIS) and the orbit period (16 d for Landsat and 26 d for SPOT).
#NIR COLOR INDEX OF THE SUN FREE#
Furthermore, some satellite platforms have free access to visible and multispectral data, such as Landsat 7-8.
#NIR COLOR INDEX OF THE SUN SERIES#
In terms of platforms, the advantages of satellite based remote sensing include high spatial resolution, which makes possible the extraction of long time data series of consistent and comparable data, which can be cost effective. However, the applicability of remote sensing and its different VIs extracted from these techniques usually relies heavily on the instruments and platforms to determine which solution is best to get a particular issue. These latter applications have been developed to be a well-known discipline category, precision agriculture, which could be tracked back to three decades ago. Specifically, these types of information applied to agriculture provide not only an objective basis (depending on resolution) for the macro- and micromanagement of agricultural production but also in many occasions the necessary information for yield estimation of crops. Remote sensed information of growth, vigor, and their dynamics from terrestrial vegetation can provide extremely useful insights for applications in environmental monitoring, biodiversity conservation, agriculture, forestry, urban green infrastructures, and other related fields. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications.
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