Brown and green LAI mapping through spectral indices

Delegido, Jesús; Verrelst, Jochem; Rivera, Juan Pablo; Ruiz‐Verdú, Antonio; Moreno, José

doi:10.1016/j.jag.2014.10.001

Cited by 77 publications

(65 citation statements)

References 53 publications

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“…These channels were selected to enhance the consistency with Landsat-7/8 data [18], allowing thus the creation of a robust multi-sensor retrieval. We have disregarded red-edge bands, despite their potential usefulness for the retrieval of important biophysical parameters, such as chlorophyll, nitrogen content and brown LAI [21,41].…”

Section: Sentinel-2a Processingmentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

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Section: Sentinel-2a Processingmentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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“…En esta campaña se hicieron medidas de diferentes parámetros biofísicos en distintos cultivos, entre ellas 110 muestras medidas con el Licor LAI (Delegido et al, 2015) que usaremos en este estudio. Estas son comparadas con una imagen obtenida a partir de las medidas espectrales realizadas con el sensor hiperespectral aerotransportado HyMap (Hyperspectral Mapper), el cual está configurado con 125 bandas entre 450 nm y 2490 nm con un tamaño de píxel de 5 m (Delegido et al, 2015). Esta imagen ha sido posteriormente corregida atmosférica y geométricamente .…”

Section: Datos Utilizadosunclassified

Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

et al. 2015

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Resumen: Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.Palabras clave: Hold-Out, k-fold, validación cruzada, aprendizaje automático, procesos gausianos, regresión de Kernel Ridge. Analysis of cross-validation methods for robust retrieval of biophysical parametersAbstract: Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were Kernel Ridge Regression (KRR) and Gaussian Process Regression (GPR). Cross-validation results showed that LR performed most unstable, while KRR and GPR led to more robust results. This work recommends using a nonlinear regression algorithm (e.g., KRR, GPR) in combination with a k-fold cross-validation technique with k=10 to realize robust retrievals.

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“…Retrieval models can be divided into physical models and statistical models [13][14][15][16]. Physical models are often applied to simple homogeneous crops but require several prerequisites (e.g., a homogeneous canopy), specific circumstances, and structural parameters, which limits their application [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

et al. 2017

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Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R 2 , SD R 2 , V-RMSE, and SD RMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

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Brown and green LAI mapping through spectral indices

Cited by 77 publications

References 53 publications

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

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