ORCID Profile
0000-0001-8407-7125
Current Organisations
Université Laval
,
Kansas State University
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Publisher: MDPI AG
Date: 04-2021
DOI: 10.3390/RS13071348
Abstract: The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over erse agro-ecosystems to increase confidence in model performance.
Publisher: Mary Ann Liebert Inc
Date: 02-2016
Publisher: Wiley
Date: 11-02-2011
DOI: 10.1111/J.1469-8749.2010.03899.X
Abstract: Evidence-based recommendations regarding which exercise tests to use in children and adolescents with cerebral palsy (CP) are lacking. This makes it very difficult for therapists and researchers to choose the appropriate exercise-related outcome measures for this group. This study aimed to identify a core set of exercise tests for children and adolescents with CP. Fifteen experts (10 physical therapists/researchers and five exercise physiologists three from the Netherlands, two from the USA, one from the UK, five from Canada, and four from Australia) participated in a Delphi survey which took four stages to achieve a consensus. Based on the information that was collected during the survey, a core set of measures was identified for levels I to IV of the Gross Motor Function Classification System (GMFCS). For children with CP classified at GMFCS levels I and II, tests were identified for two motor skills (walking and cycling). For the subgroup of children with CP classified at GMFCS level III, the tests that were identified related to walking, cycling, and arm cranking. For children with CP classified at GMFCS level IV, the tests included in the core set were related to cycling and arm cranking. The core set will help physical therapists, exercise physiologists, and other health professionals who work with children and adolescents with CP to decide which test(s) to use in clinical practice or research. This will facilitate comparability of results across studies and clinical programmes.
Publisher: Elsevier BV
Date: 10-2019
No related grants have been discovered for Dipankar Mandal.