scholarly journals Análise espectral e avaliação de índices de vegetação para o mapeamento da caatinga

2015 ◽  
Vol 10 (3) ◽  
pp. 01
Author(s):  
Paulo Roberto Megna Francisco ◽  
Iede De Brito Chaves ◽  
Lucia Helena Garofalo Chaves ◽  
Eduardo Rodrigues Viana de Lima ◽  
Bernado Barbosa da Silva
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Spectral Indices ◽  
Font Size ◽  
Dry Period ◽  
Analysis And Evaluation ◽  
Caatinga Vegetation

<p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-size: 12.0pt; font-family: 'Times New Roman','serif';">A caatinga é um bioma de grande diversidade que cobre a maior parte da área de clima semiárido brasileiro. Várias técnicas já foram utilizadas com o objetivo de determinar quantitativamente e qualitativamente o estado da vegetação a partir de imagens de satélite, e índices de vegetação foram desenvolvidos para auxiliar no mapeamento da vegetação, otimizando parâmetros de medidas espectrais utilizadas com esse fim. Este trabalho teve como objetivo analisar e avaliar índices espectrais (NDVI, SAVI e EVI) para mapear a vegetação de caatinga. Concluiu-se que o melhor índice que se correlaciona com a cobertura vegetal da caatinga foi o Normalized Difference Vegetation Index (NDVI), para o período seco, e o padrão de resposta espectral do período seco diminuiu os confundimentos de alvos de vegetação da caatinga. Estimou-se que 29,7% da área da bacia do rio Taperoá esteja em processo avançado de desertificação.</span></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"> </p><p align="center"><strong><em>Spectral analysis and evaluation of vegetation indices for mapping caatinga</em></strong></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"> </p><p><strong>ABSTRACT: </strong>The caatinga biome is a large diversity that covers most of the area of Brazilian semi-arid climate. Several techniques have been used in order to determine quantitatively and qualitatively the state of vegetation from satellite images and vegetation indices were developed to assist in vegetation mapping, optimizing spectral measurement parameters used for this purpose. This study aimed to analyze and evaluate spectral indices (NDVI, SAVI and EVI) to map the caatinga vegetation. It was concluded that the best index that correlates with the caatinga vegetation was the Normalized Difference Vegetation Index (NDVI) for the dry period, and the pattern of spectral response of the dry period decreased confounding targets of caatinga vegetation. It was estimated that 29.7% of the area of the river basin Taperoá is in advanced process of desertification.<strong></strong></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-size: 12.0pt; font-family: 'Times New Roman','serif';"><br /></span></p>

scholarly journals Detecção de Mudança da Vegetação de Caatinga (Change Detection of Vegetation Caatinga)

2013 ◽  
Vol 5 (6) ◽  
pp. 1473 ◽  
Author(s):  
Paulo Roberto Megna Francisco ◽  
Iêde De Brito Chaves ◽  
Lúcia Helena Garófalo Chaves ◽  
Eduardo Rodrigues Viana de Lima
Keyword(s):  
Change Detection ◽  
River Basin ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Dry Period ◽  
Caatinga Biome ◽  
Semi Arid

A caatinga é um bioma de grande diversidade que cobre a maior parte da área de clima semiárido brasileiro. Várias técnicas já foram utilizadas com o objetivo de determinar quantitativamente e qualitativamente o estado da vegetação a partir de imagens de satélite e índices de vegetação foram desenvolvidos para auxiliar no mapeamento da vegetação e otimizar os parâmetros presentes nas medidas multiespectrais utilizadas com esse fim. Este trabalho teve como objetivo mapear a vegetação da caatinga, e selecionar um índice de vegetação usando o IBVL para validação dos resultados e detectar mudanças ocorridas. Concluiu-se que o melhor índice que se correlaciona com a cobertura vegetal da caatinga foi o Normalized Difference Vegetation Index, do período seco, e que a metodologia utilizada mostrou-se eficiente para caracterização, classificação e separação em 9 classes. A maior recuperação ocorreu em áreas de drenagem e em declividade mais acentuada. A classe detectada de não mudança ocorreu em áreas de menor cobertura vegetal e de solos propensos à erosão. Estimou-se que 38,71% da área da bacia do rio Taperoá esteja em processo de desertificação.Palavras-chave: Semiárido, Geoprocessamento, Degradação. Change Detection of Vegetation Caatinga ABSTRACTThe caatinga biome is a large diversity that covers most of the area of Brazilian semi-arid climate. Several techniques have been used in order to determine quantitatively and qualitatively the state of vegetation from satellite images and vegetation indices were developed to assist in vegetation mapping and optimizing the parameters present in the multispectral measurements used for this purpose. This study aimed to map the vegetation of the caatinga, and select a vegetation index using IBVL to validate the results and detect changes. It was concluded that the best index that correlates with the vegetation of the caatinga was the Normalized Difference Vegetation Index, the dry period, and that the methodology used was efficient for characterization, classification and separation into nine classes. The best recovery occurred in areas of drainage and steeper slope. The class detected no change occurred in areas with less vegetation cover and soils prone to erosion. It was estimated that 20.21% of the area of the river basin Taperoá is in an advanced process of desertification.Keywords: Semiarid, Geoprocessing, Degradation.

Separation of Different Vegetation Types in ASTER and Landsat Satellite Images Using Satellite‐derived Vegetation Indices

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Mazlan Hashim ◽  
Sharifeh Hazini
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Vegetation Indices ◽  
Critical Issue ◽  
Vegetation Types ◽  
Enhanced Vegetation Index ◽  
Density Slicing ◽  
Landsat Satellite

Separation of different vegetation types in satellite images is a critical issue in remote sensing. This is because of the close reflectance between different vegetation types that it makes difficult segregation of them in satellite images. In this study, to facilitate this problem, different satellite derived vegetation indices including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index 2 (EVI2) were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-5 TM data. The obtained NDVI, EVI, and EVI2 images were then analyzed and interpreted in order to evaluate their effectiveness to discriminate rice and citrus fields from ASTER and Landsat data. In doing so, the Density Slicing (DS) classification technique followed by the trial and error method was implemented. The results indicated that the accuracies of ASTER NDVI and ASTER EVI2 for citrus mapping are about 75% and 65%, while the accuracies of Landsat NDVI and Landsat EVI for rice mapping are about 60% and 65%, respectively. The achieved results demonstrated higher performance of ASTER NDVI for citrus mapping and Landsat EVI for rice mapping. The study concluded that it is difficult to detect and map rice fields from satellite images using satellite-derived indices with high accuracy. However, the citrus fields can be mapped with the higher accuracy using satellite-derived indices.

scholarly journals Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1909
Author(s):  
Enrico Borgogno-Mondino ◽  
Laura de Palma ◽  
Vittorino Novello
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Table Grape ◽  
Optical Data ◽  
Strong Impact ◽  
Spectral Indices ◽  
Intrinsic Nature ◽  
Sentinel 2

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards.

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 Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1830
Author(s):  
Yongqian Ding ◽  
Yizhuo Jiang ◽  
Hongfeng Yu ◽  
Chuanlei Yang ◽  
Xueni Wu ◽  
...  
Keyword(s):  
Light Source ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Application Rate ◽  
Test Point ◽  
Central Wavelength ◽  
Nitrogen Application Rate

A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.

scholarly journals Assessment of soil contamination using remote sensing data in the Tamsag-Bulag oil field, Mongolia

2021 ◽  
Vol 895 (1) ◽  
pp. 012013
Author(s):  
S Gantumur ◽  
G V Kharitonova ◽  
A S Stepanov ◽  
K N Dubrovin
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Oil Field ◽  
Oil Contamination ◽  
Vegetation Growth

Abstract Although field surveus represent an essential method for determining oil contamination of soils and soil cover, the use of remote sensing techniques has become one of the main trends over recent years due to their economic and temporary advantages. The fundamental basis of this approach is the assessment of changes in vegetation cover by vegetation indices as indicator. In this study, the problems of assessment of the soil cover contamination during oil production are considered. It is aimed to select and evaluate objective criteria for soil cover contamination with oil in the Tamsag–Bulag field (Eastern Gobi, Mongolia). For this purpose, during the period of maximum vegetation growth, various vegetation indices were investigated at test sites (4 km2) from 2015 to 2019. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used with Sentinel-2 and MODIS of the Terra satellite images at 30 and 250 m resolution, respectively. The monitoring of the land quality with satellite images via NDVI and SAVI allows us to assess the area of oil contamination of the soils and soil cover. The significant increase in the values of the NDVI and SAVI at a distance of more than 4 km from the center of the Tamsag-Bulag oil field is shown. The obtained results indicate the possibility of assessment and monitoring the state of the oil-ed territories of the Eastern Gobi by NDVI и SAVI using satellite images.

scholarly journals Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

2019 ◽  
Vol 11 (9) ◽  
pp. 1073 ◽  
Author(s):  
Pedro C. Towers ◽  
Albert Strever ◽  
Carlos Poblete-Echeverría
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Dynamic Range ◽  
Spectral Response ◽  
Incident Light ◽  
Vegetation Indices ◽  
Area Index

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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