Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Siegmann, Bastian; Jarmer, Thomas

doi:10.1080/01431161.2015.1084438

Cited by 82 publications

(46 citation statements)

References 55 publications

(60 reference statements)

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“…These results are reasonable because the SVR model has proven robust and efficient with small datasets [92,93]. This result is in agreement with the findings reported in References [94][95][96][97], which concluded that SVR consistently outperformed other machine learning methods. The performance (R 2 of 0.73 and RMSE of 38.68 Mg·ha −1 , Table 7) of the SVR model with the combination data of the Sentinel-2A and the ALOS-2 PALSAR-2 indicates a satisfactory result compared to previous studies on the forest AGB, as seen in Reference [98] (R 2 = 0.46), (R 2 = 0.28-0.44) [21], and (R 2 = 0.46) [99].…”

Section: Discussionsupporting

confidence: 83%

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

et al. 2018

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Abstract:The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R 2 ), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R 2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R 2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R 2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R 2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

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Section: Discussionsupporting

confidence: 83%

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

et al. 2018

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“…Both methods are commonly used in remote sensing (e.g., Baret et al, 2009;Siegmann and Jarmer, 2015). The NN method was used for development of the LAI product of future space systems (Baret et al, 2009).…”

Section: Resultsmentioning

confidence: 99%

Informative spectral bands for remote green LAI estimation in C3 and C4 crops

Kira

Nguy-Robertson

Arkebauer

et al. 2016

Agricultural and Forest Meteorology

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Informative spectral bands for remote green LAI estimation in C3 and C4 crops" (2016). Papers in Natural Resources. 669. https://digitalcommons.unl.edu/natrespapers/669 K i r a e t a l . i n A g r i c u l t u r a l a n d F o r e s t M e t e o r o l o g y2 1 8 -2 1 9 ( 2 0 1 6 ) AbstractGreen leaf area index (LAI) provides insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast and nondestructive estimation of green LAI. A number of methods have been used for the estimation of green LAI; however, the specific spectral bands employed varied widely among the methods and data used. Our objectives were (i) to find informative spectral bands retained in three types of methods, neural network (NN), partial least squares (PLS) regression and vegetation indices (VI), for estimating green LAI in maize (a C4 species) and soybean (a C3 species); (ii) to assess the accuracy of the algorithms estimating green LAI using a minimal number of bands for each crop and generic algorithms for the two crops combined. Hyperspectral reflectance and green LAI of irrigated and rainfed maize and soybean were taken during eight years of observations (altogether 24 field-years) in very different weather conditions. The bands retained in the best NN, PLS and VI methods were in close agreement. The validity of these bands was further confirmed via the uninformative variable elimination PLS technique. The red edge and the NIR bands were selected in all models and were found the most informative. Identifying informative spectral bands across all four techniques provided insight into spectral features of reflectance specific for each species as well as those that are common to species with different leaf structures, canopy architectures and photosynthetic pathways. The analyses allowed development of algorithms for estimating green LAI in soybean and maize with no re-parameterization. These findings lay a strong foundation for the development of generic algorithms which is crucial for remote sensing of vegetation biophysical parameters.Keywords: Remote sensing, Reflectance, Neural network, Partial least squares, Vegetation index, Maize, Soybean digitalcommons.unl.edu K i r a e t a l . i n A g r i c u l t u r a l a n d F o r e s t M e t e o r o l o g y2 1 8 -2 1 9 ( 2 0 1 6 ) 2

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“…We calculated the PRESS statistic through a leaveone-out cross-validation approach, which trains the model on all but one observation and then makes a prediction for the left-out observation. The average error is then computed and used to evaluate the model (Siegmann and Jarmer, 2015). VIP scores (Wold, 1994) also were determined to compare the relative significance of each wavelength in its contribution to the final model.…”

Section: Plsr Modelsmentioning

confidence: 99%

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

et al. 2016

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High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500-2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO 2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

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Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Cited by 82 publications

References 55 publications

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

Informative spectral bands for remote green LAI estimation in C3 and C4 crops

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

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