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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Preliminary Validation of Mixed-Pixel Clumping Index in the Arid and Semi-Arid Region, Western China
摘要： In this paper, the 1 km Mixed Pixel Clumping Index (MPCI) was calculated using the 30 m HJ-1A/1B CCD data in the arid and semi-arid region, Western China. To validate the result, an indirect validation method was proposed. In this method, the 1 km effective LAI was retrieved from the satellite data using the PROSAIL model first, and then corrected to the true LAI with the MPCI data. The comparison between the retrieved true LAI and the MODIS product shows a significant improvement relative to the effective LAI. The correlation R2 rise from 0.52 to 0.70 and the RMSE falls from 0.58 to 0.42. It indicates that the MPCI calculation is reasonable and valid for LAI retrieval from the satellite data.
关键词： mixed pixel,leaf area index,Clumping index,validation
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of Leaf Area Index with Various Vegetation Indices from Gaofen-5 Band Reflectances
摘要： This paper attempted to retrieve leaf area index (LAI) from Gaofen (GF)-5 satellite simulation data using 6 common used vegetation indices. The canopy reflectances from 0.4-2.5μm were simulated from the combination of vegetation leaf model PROSPECT and four-stream scattering by arbitrarily inclined leaves (4SAIL) model. GF-5 satellite spectral response functions (SRF) were used to calculate the band reflectances in visible and near infrared regions. Polynomial regression was used to establish the relationships between the vegetation indices and LAI, and coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the relationships. The results showed that DVI among those indices is the best to retrieve LAI from GF-5 data with R2 of 0.964. The results also showed that the retrieval accuracy can be as high as 0.338.
关键词： Leaf area index (LAI),vegetation index,canopy reflectances,GF-5
更新于2025-09-10 09:29:36
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Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China
摘要： Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio (α) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the α of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the α of L. gmelinii forest plots was evaluated by using the reference α of the selected forest plots. The reference α of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference α of the selected forest plots (ranging from 0% to 56%). These α were also highly correlated with the site conditions and management activities in these plots. The effective α (αe) or α estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference α in the five plots. The overestimation of α or αe was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate α estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation.
关键词： plant area index (PAI),woody area index (WAI),leaf area index (LAI),forest canopy,clumping effect,multispectral canopy imager,digital hemispherical photography,woody-to-total area ratio
更新于2025-09-09 09:28:46
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Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments
摘要： The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models’ key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
关键词： paddy rice,machine learning,remote sensing,leaf area index,hyperspectral data
更新于2025-09-09 09:28:46
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Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing
摘要： In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopy gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. These collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data (R2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 (m2/m2)).
关键词： DIRSIG,leaf area index,AVIRIS,HyspIRI,photosynthetically active radiation
更新于2025-09-04 15:30:14