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      Spectral Characterization of Avocado Persea Americana Mill. Cv. Hass Using Spectrometry and Imagery from the Visible to Near-Infrared Range Translated title: Caracterización espectral de aguacate Persea americana Mill cv. Hass empleando espectrometría e imágenes en el rango visible a infrarrojo cercano

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          Abstract

          Abstract Remote sensing technologies, such as spectral imaging, have great potential for crop monitoring. Spectral systems measure the energy reflected and emitted by a surface, typically between the visible and near-infrared regions of the electromagnetic spectrum. This paper presents a spectral characterization of avocado (Persea americana Mill. cv. Hass) using spectrophotometry and spectral imaging. The study uses data from four avocado farms, which were collected in situ using spectrometers and GreenSeeker sensors and remotely using satellites such as Landsat 8 and Sentinel 2. The spectral signatures captured by the in situ and remote sensors were compared and subsequently related to vegetation indices. Spectrometry revealed differences between young and mature leaves, particularly in the 480 nm to 650 nm region of the spectrum, which showed color changes in young avocado leaves. The analysis of satellite data highlighted significant differences between Sentinel 2 and Landsat 8 spectral signatures. These differences are likely due to several factors, including collection date, preprocessing, and spatial resolution of the data. Finally, the vegetation indices derived from in situ and satellite measurements displayed different scales. For in situ data, the Normalized Difference Vegetation Index (NDVI) values were around 0.9 for the spectrometers and 0.7 for the GreenSeeker sensors. However, the NDVI values derived from satellite data were around 0.4 for Sentinel 2 and 0.3 for Landsat 8.

          Translated abstract

          Resumen Las tecnologías de la percepción remota, como las imágenes espectrales, tienen un gran potencial para el monitoreo de los cultivos. Los sistemas espectrales miden la energía reflejada y emitida de una superficie, usualmente entre los rangos visible e infrarrojo cercano del espectro electromagnético. Este artículo tuvo como objetivo presentar una caracterización espectral del aguacate Persea americana Mill cv. Hass utilizando espectrofotometría e imágenes espectrales. El estudio usó datos in situ capturados con espectrómetros y GreenSeeker, y datos remotos capturados por sensores en satélites como Landsat 8 y Sentinel 2. Lo anterior se hizo sobre cuatro unidades productivas de aguacate. En primer lugar, se compararon la forma de las firmas espectrales captadas por los sensores in situ y remotos, y después se relacionaron con los índices de vegetación. A partir de la espectrometría, se establecieron diferencias entre las hojas jóvenes y las hojas desarrolladas o maduras, principalmente entre 480 nm y 650 nm. Esta región del espectro muestra los cambios de color presentes en las hojas jóvenes del aguacate. A partir de los datos de satélite, la firma espectral presenta diferencias significativas entre Sentinel 2 y Landsat 8. Los resultados mostraron que estas diferencias se derivan de varios factores, como la fecha de adquisición, el preprocesamiento y la resolución espacial. Por último, los índices de vegetación procedentes de mediciones in situ y por satélite evidenciaron escalas diferentes. El índice de vegetación de diferencia normalizada (NDVI, por sus siglas en inglés) para los datos in situ tiene valores alrededor de 0.9 y 0.7 para el espectrómetro y el GreenSeeker, respectivamente. Sin embargo, el NDVI derivado de los datos satelitales está alrededor de 0.4 para Sentinel 2 y 0.3 para Landsat 8.

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          Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

          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. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). 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. 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. 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. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.
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            PROSPECT+SAIL models: A review of use for vegetation characterization

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              Retrieval of foliar information about plant pigment systems from high resolution spectroscopy

                Author and article information

                Journal
                teclo
                TecnoLógicas
                TecnoL.
                Instituto Tecnológico Metropolitano - ITM (Medellín, Antioquia, Colombia )
                0123-7799
                2256-5337
                April 2023
                : 26
                : 56
                : e208
                Affiliations
                [1] Medellín Antioquía orgnameInstituto Tecnológico Metropolitano Colombia mariatorres@ 123456itm.edu.co
                [2] Rionegro orgnameCorporación Colombiana de Investigación Agropecuaria - AGROSAVIA Colombia trondon@ 123456agrosavia.co
                [3] Medellín Antioquía orgnameInstituto Tecnológico Metropolitano Colombia ricardofranco162765@ 123456correo.itm.edu.co
                [4] Rionegro orgnameCorporación Colombiana de Investigación Agropecuaria - AGROSAVIA Colombia mcasamitjana@ 123456agrosavia.co
                [5] Medellín Antioquía orgnameInstituto Tecnológico Metropolitano Colombia johanatrochez@ 123456itm.edu.co
                Author information
                https://orcid.org/0000-0002-9795-2459
                https://orcid.org/0000-0001-5578-3290
                https://orcid.org/0000-0003-1607-404X
                https://orcid.org/0000-0003-3257-0991
                https://orcid.org/0000-0002-2137-3143
                Article
                S0123-77992023000100208 S0123-7799(23)02605600208
                10.22430/22565337.2567
                48d10d4c-c5da-49e1-9c5f-0485a479462e

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

                History
                : 10 November 2022
                : 05 May 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 31, Pages: 0
                Product

                SciELO Colombia

                Categories
                Scientific and technological research paper

                spectrometry,percepción remota,índices de vegetación,imágenes multiespectrales,espectrometría,Aguacate,vegetation indices,remote sensing,multispectral imagery,Avocado

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