Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies

Serrano, Lydia; Filella, Iolanda; Peñuelas, Josep

doi:10.2135/cropsci2000.403723x

Cited by 321 publications

(243 citation statements)

References 34 publications

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“…Reflectance values were measured by field spectroradiometry for wavelengths between 400 and 2500 nm in the 368 genotypes in different environments and at different development stages and were similar to those reported in wheat and barley grown with and without stress for different development stages [37,39]. High-yield genotypes reflected a lower amount of radiation in the visible spectrum (400-700 nm) as compared with low-yield genotypes, and this could be related to the leaf content of anthocyanins, carotenoids and chlorophyll, which are lower as a consequence of water stress [10,40].…”

Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldsupporting

confidence: 68%

“…Also, Zhao et al [42] reported higher reflectance in the 550-710 nm zone in a maize crop with an N deficit. However, other studies [37,43] revealed lower reflectance in the red zone associated with higher rates of N fertilization in wheat plants. In the NIR zone (800 to 1300 nm), high-yield genotypes showed higher reflectance in all environments, which could be associated with a greater leaf area index and green biomass [44,45].…”

Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldmentioning

confidence: 74%

“…The indices were chosen according to different biophysical criteria. The normalized difference vegetation index or NDVI (R900 − R680)/(R900 + R680) and the simple ratio or SR (R900/R680) are correlated with photosynthetic area [5,17,20,35], the photochemical reflectance index or PRI (R531 − R570)/(R531 + R570) is correlated with radiation use efficiency [36] and the water index or WI (R970/R900) is correlated with water status [37] Thereafter, a regression analysis was generated between each index and grain yield, where the coefficients of the regression were obtained to generate a predictive model for grain yield. Finally, the model fit between observed and predicted grain yields was evaluated using the coefficient of determination (R 2 ).…”

Section: Measurementsmentioning

confidence: 99%

“…This contrasts with measurements at grain filling where negative values became positive in both water stress conditions and to a lesser extent in FI. These variations in magnitude and sign of the coefficients could account for changes in the canopy because this zone has been related to differences in the canopy's biomass during measurements between anthesis and grain filling [37,43].…”

Section: Regression Coefficients For Different Spectral Zonesmentioning

confidence: 99%

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Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg·ha −1 , respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY OPEN ACCESSRemote Sens. 2015, 7 2110 variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R 2 ) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

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Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldsupporting

confidence: 68%

Section: Canopy Reflectance In Genotypes Of Contrasting Grain Yieldmentioning

confidence: 74%

Section: Measurementsmentioning

confidence: 99%

Section: Regression Coefficients For Different Spectral Zonesmentioning

confidence: 99%

See 2 more Smart Citations

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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“…Nesse caso, o estado nutricional das plantas em relação ao N pode ser estimado pela modificação da reflectância do dossel (SEMBIRING et al, 2000), basicamente por haver relação inversa entre essa variável e o teor de clorofila (SERRANO et al, 2000). O aumento da concentração de clorofila, em função dos níveis de N, resulta em incremento da área foliar (biomassa) e também da atividade fotossintética (SERRANO et al, 2000). Dessa forma, aplica-se a correlação entre índices espectrais e o acúmulo de biomassa das culturas a fim de predizer a condição nutricional ou seu estádio de desenvolvimento (LIU, 2006).…”

Section: Introductionunclassified

Modelo para estimativa do potencial produtivo em trigo e cevada por meio do sensor GreenSeeker

et al. 2009

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RESUMO:Áreas com diferentes potenciais de rendimento dentro de uma lavoura necessitam ser manejadas separadamente, para fins de aplicação da adubação nitrogenada em cobertura. O equipamento baseado em sensoriamento remoto terrestre (GreenSeeker) é um dos instrumentos utilizados para separar diferentes zonas de manejo. Para fazer isso, o sensor permite a definição de classes para estimar o potencial produtivo de forma ágil, precisa e em tempo real. Com o instrumento, foi desenvolvido um modelo para estimativa do potencial produtivo em trigo e cevada, correlacionando o Índice de Vegetação por Diferença Normalizada (NDVI) com a biomassa seca acumulada na parte aérea, por ocasião da emissão da sexta folha do colmo principal. A base do modelo foi a formação de classes de potencial produtivo correspondentes a zonas específicas de manejo da lavoura. Essas classes não necessitam ser específicas para diferentes cultivares e/ou espécies, visto que não se detectaram diferenças que justificassem a formação de grupos para elas. As superfícies de fundo (resíduos de restevas de soja e milho) tiveram efeitos significativos nas leituras do sensor. O modelo continua válido mesmo se as leituras de NDVI forem feitas antes ou após o período recomendado para tal, podendo ser ajustado com sub ou superestimação. As análises de variabilidade espacial, futuramente, podem avaliar se, as zonas de potencial produtivo estimadas pelas classes de NDVI propostas pelo modelo, correspondem à flutuação espacial da biomassa, doses de N aplicadas e rendimento de grãos. PALAVRAS-CHAVE: agricultura de precisão, adubação nitrogenada em taxa variável, NDVI. MODEL FOR YIELD POTENTIAL ESTIMATION IN WHEAT AND BARLEY USING THE GREENSEEKER SENSORABSTRACT: Areas with different yield potential within a field need to be managed separately as for nitrogen application in small grain cereals. Terrestrial remote sensing-based equipment such as the GreenSeeker sensor is one of the tools available to handle different management zones. To do this, the sensor allows the definition of classes to estimate yield potential. A model which correlated the Normalized Difference Vegetation Index (NDVI) to shoot dry biomass at the 6-leaf-stage was developed for estimating yield potential classes for wheat and barley. The model eliminated differences between species and cultivars as no correction for these factors is necessary. The effects of surface background (corn or soybean crop residues) were considered in this model. When readings are carried out before or after the recommended period, the model can be adjusted for under or overestimation. Spatial variability analysis may evaluate if yield potential zones estimated by the NDVI classes proposed in the model are related to spatial variability of shoot biomass, N rates applied and grain yield.

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Computer Processing of Remotely‐Sensed Images

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Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies

Cited by 321 publications

References 34 publications

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Modelo para estimativa do potencial produtivo em trigo e cevada por meio do sensor GreenSeeker

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