Influence of different soil reflectance schemes on the retrieval of vegetation LAI and FVC from PROSAIL in agriculture region

Jiang, Haiying; Wei, Xiangqin; Chen, Zhulin; Zhu, Mengxun; Yao, Yunjun; Zhang, Xiaotong; Jia, Kun

doi:10.1016/j.compag.2023.108165

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…reflectance, and numerous studies have reported its significant impact on the canopy reflectance when the fCover is low [16,[28][29][30].…”

Section: Study Sitementioning

confidence: 99%

“…This conclusion aligns with a previous study that the influence of the soil background on the canopy reflectance would approach a maximum level at a low fCover, and it would significantly affect the accuracy of the inversion parameters. But when the fCover is high, the influence of the soil background on parameter inversion reduces greatly [30,45].…”

Section: Analysis Of the Impact Factors On The Accuracy Of Sail In Mo...mentioning

confidence: 99%

“…The solar angle comprises the solar azimuth angle (SAA) and the solar zenith angle (SZA), both of which markedly influence the spectral reflectance of row canopies by changing the canopy shadowing [27]. The soil background reflectance constitutes a crucial component of canopy reflectance, and numerous studies have reported its significant impact on the canopy reflectance when the fCover is low [16,[28][29][30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

Han,

Liu,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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%).

show abstract

“…reflectance, and numerous studies have reported its significant impact on the canopy reflectance when the fCover is low [16,[28][29][30].…”

Section: Study Sitementioning

confidence: 99%

Section: Analysis Of the Impact Factors On The Accuracy Of Sail In Mo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

Han,

Liu,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Leaf area index (LAI), an important vegetation structural parameter, is also a common indicator for AGB modeling [29,30]. Meanwhile, the inversion of LAI from remote sensing data has become increasingly mature, and its accuracy is guaranteed [31][32][33]. UAV technology captures vegetation spectral or structural information with high spatial and spectral resolution, enabling accurate estimation of vegetation biomass [20,23,34].…”

Section: Introductionmentioning

confidence: 99%

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe

Zhu,

Chen,

et al. 2024

Plants

View full text Add to dashboard Cite

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

show abstract

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Dong,

Liu,

Jiang

et al. 2024

Remote Sensing of Environment

View full text Add to dashboard Cite

Influence of different soil reflectance schemes on the retrieval of vegetation LAI and FVC from PROSAIL in agriculture region

Cited by 6 publications

References 27 publications

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Contact Info

Product

Resources

About