Huarong Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Huarong Zhao has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Dalei Han
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
1 shared publication - Hao Jiang
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
1 shared publication - Jin Wang
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
1 shared publication - Jing Liu
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, China
1 shared publication - Peiqi Yang
Nanjing Normal University
1 shared publication - Runfei Zhang
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
1 shared publication
Latest Publications
- Open Access
- 21 November 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (23) , 5433
Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies
The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).
- 21 November 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (23) , 5433
Open AccessEvaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies
The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).