scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

scholarly journals ANÁLISE DO IMPACTO DA CORREÇÃO ATMOSFÉRICA NO CÁLCULO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA A PARTIR DE IMAGEM LANDSAT 8/OLI

2020 ◽  
Vol 13 (1) ◽  
pp. 076
Author(s):  
Cristiane Nunes Francisco ◽  
Paulo Roberto da Silva Ruiz ◽  
Cláudia Maria de Almeida ◽  
Nina Cardoso Gruber ◽  
Camila Souza dos Anjos
Keyword(s):  
Rio De Janeiro ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat 8 Oli ◽  
Arithmetic Operations

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação.  Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

2019 ◽  
Vol 11 (11) ◽  
pp. 1350 ◽  
Author(s):  
José Raúl Román ◽  
Emilio Rodríguez-Caballero ◽  
Borja Rodríguez-Lozano ◽  
Beatriz Roncero-Ramos ◽  
Sonia Chamizo ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Soil Surface ◽  
Response Analysis ◽  
Surface Reflectance ◽  
Wide Range ◽  
Reflectance Analysis ◽  
Non Destructive

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms’ status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine

2020 ◽  
Vol 12 (2) ◽  
pp. 211 ◽  
Author(s):  
Pablo Martín-Ortega ◽  
Luis Gonzaga García-Montero ◽  
Nicole Sibelet
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Temporal Patterns ◽  
Google Earth ◽  
Surface Reflectance ◽  
Enhanced Vegetation Index ◽  
Google Earth Engine ◽  
Level 1 ◽  
Irregular Topography

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP

Irriga ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 330-342
Author(s):  
Renata Teixeira de Almeida Minhoni ◽  
Mírian Paula Medeiros André Pinheiro ◽  
Roberto Filgueiras ◽  
Celia Regina Lopes Zimback
Keyword(s):  
Supervised Classification ◽  
Aquatic Macrophytes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Boundary Definition ◽  
E Mail

SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP  RENATA TEIXEIRA DE ALMEIDA MINHONI1; MÍRIAN PAULA MEDEIROS ANDRÉ PINHEIRO2; ROBERTO FILGUEIRAS3 E CÉLIA REGINA LOPES ZIMBACK4 1 Eng. Ambiental, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrônoma, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrícola e Ambiental, Doutorando em Engenharia Agrícola – UFV. Avenida Peter Henry Rolfs, s/n - Campus Universitário, CEP 36570-900, Viçosa - MG, e-mail: [email protected] Eng. Agrônoma, Professora. Doutora do Departamento de Solos e Recursos Ambientais - FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected]  1 RESUMO Macrófitas aquáticas são organismos fotossintéticos, com tamanho suficiente para serem vistos a olho nu, que crescem submersas, flutuando ou sobre a superfície da água. A ação antrópica no represamento de corpos hídricos tem ocasionado a eutrofização dos recursos hídricos, e dentre os desequilíbrios que esta ação gera no meio aquático está à elevada proliferação de macrófitas. Devido a esse fato, essa pesquisa foi desenvolvida com o objetivo de realizar uma estimativa da área ocupada por macrófitas aquáticas no reservatório da Usina Hidrelétrica de Barra Bonita (SP), nos anos de 2013, 2014 e 2015. O estudo foi realizado na estação seca (mês de agosto), por meio do uso do NDVI (Normalized Difference Vegetation Index) e classificação supervisionada MAXVER (Máxima Verossimilhança). Para obtenção dos mapas e gráficos, foram realizadas as seguintes ações: seleção das imagens do satélite LANDSAT-8/OLI, calibração radiométrica, correção atmosférica, reprojeção, definição do limite, recorte da área, NDVI e classificação supervisionada. Os mapas obtidos por meio da classificação supervisionada, auxiliada pelos mapas de NDVI, apontaram para um aumento de aproximadamente 50% na área ocupada por macrófitas aquáticas de 2013 a 2015. Palavras-chave: classificação supervisionada, eutrofização, índice NDVI, landsat-8.  MINHONI, R. T. A.; PINHEIRO, M. P. M. A.; FILGUEIRAS, R.; ZIMBACK, C. R. L.REMOTE SENSING APPLIED TO THE MONITORING OF AQUATIC MACROPHYTES AT BARRA BONITA RESERVOIR, SP  2 ABSTRACT Aquatic macrophytes are photosynthetic organisms, large enough to be seen with naked eye, which grow submerged, floating or on the surface of the water. The anthropic action in the damming of water bodies has caused eutrophication of water resources, and among the imbalances that this action generates in the aquatic environment is the high proliferation of macrophytes. Due to this fact, this research was developed with the aim of estimating the area occupied by aquatic macrophytes in the reservoir of Barra Bonita Hydroelectric Power Plant (SP), in the years of 2013, 2014 and 2015. The study was carried out in the dry season (August), through the use of NDVI (Normalized Difference Vegetation Index) and supervised classification MAXVER (Maximum Likelihood). To obtain the maps and graphs, the following actions were taken: selection of LANDSAT-8 / OLI satellite images, radiometric calibration, atmospheric correction, reprojection, boundary definition, NDVI and supervised classification. The maps obtained through supervised classification, aided by NDVI maps, pointed to an increase of approximately 50% in the area occupied by aquatic macrophytes from 2013 to 2015. Keywords: supervised classification, eutrophication, NDVI index, landsat-8.

scholarly journals Uso e ocupação do solo do município de novo progresso no Estado do Pará-Brasil

2021 ◽  
Vol 10 (1) ◽  
pp. e51210112060
Author(s):  
Raimara Reis do Rosário ◽  
Mateus Trindade Barbosa ◽  
Francimary da Silva Carneiro ◽  
Merilene do Socorro Silva Costa
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
De Novo ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Google Earth Engine ◽  
Para Determinar

O objetivo foi analisar o processo de uso e ocupação do solo do município de Novo Progresso no Estado do Pará, interligando-o com as atividades de maior importância econômica desenvolvidas nesta região. Utilizou-se o shapefile de limite do município de Novo Progresso na plataforma online Google Earth Engine (GEE), que disponibilizou um mosaico de imagens orbitais, do satélite Landsat-8/OLI-TIRS, referentes ao ano de 2019. O processo de classificação foi feito a partir do Code Editor do GEE, utilizando um Índice espectral de vegetação para auxiliar a classificação (Normalized Difference Vegetation Index – NDVI). Foi utilizado o Software QGis 3.10.6 para elaborar os mapas de localização do município e o de classificação de uso e cobertura do solo. Os dados foram tabulados em planilhas para determinar as taxas de crescimento do período analisado. Para realizar a avaliação da confiabilidade da classificação foi utilizado o método de Exatidão Global e o Índice Kappa. Foi possível identificar que no ano de 2019, houve a incidência de 3.064.396,65 ha (80,3%) de floresta densa, uma área de 496.104,07 ha (13,0%) com solo exposto, 248.052,03 ha (6,5%) de floresta secundária, e apenas 7.632,37 ha (0,2%) com predominância de hidrografia, totalizando uma área de 3.816.185,13 ha.  As áreas que encontram-se com o solo exposto não estão diretamente relacionadas com o crescimento populacional, mas sim a forma como é estabelecido o uso do solo, com base nas principais atividades desenvolvidas na região considerando que a lógica produtiva ocorre de forma desordenada, não respeitando os critérios de desenvolvimento sustentável.

scholarly journals Science impact of MODIS C5 calibration degradation and C6+ improvements

2014 ◽  
Vol 7 (7) ◽  
pp. 7281-7319 ◽  
Author(s):  
A. Lyapustin ◽  
Y. Wang ◽  
X. Xiong ◽  
G. Meister ◽  
S. Platnick ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Temporal Trends ◽  
Polarization Sensitivity ◽  
Surface Reflectance ◽  
Blue Band ◽  
Cross Calibration ◽  
Calibration Approach

Abstract. The Collection 6 (C6) MODIS land and atmosphere datasets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra, and to lesser extent, in MODIS Aqua geophysical datasets. Sensor degradation is largest in the Blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström Exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS dataset which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as de-trending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over deserts, we have also developed a de-trending and cross-calibration method which removes residual decadal trends on the order of several tenths of one percent of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

scholarly journals Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

2021 ◽  
Vol 13 (16) ◽  
pp. 3072
Author(s):  
Dominique Carrer ◽  
Catherine Meurey ◽  
Olivier Hagolle ◽  
Guillaume Bigeard ◽  
Alexandre Paci ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Atmospheric Correction ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
The Sun ◽  
Cloud Detection ◽  
Ground Station ◽  
Vegetation Health

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers.

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