Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire

2021 ◽  
Vol 255 ◽  
pp. 112304
Author(s):  
José Manuel Fernández-Guisuraga ◽  
Jochem Verrelst ◽  
Leonor Calvo ◽  
Susana Suárez-Seoane
Keyword(s):  
Radiative Transfer ◽  
Spatial Resolution ◽  
Vegetation Cover ◽  
High Spatial Resolution ◽  
Ecological Systems ◽  
Radiative Transfer Models ◽  
Fractional Vegetation Cover ◽  
Reflectance Data ◽  
Transfer Models

scholarly journals Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

2021 ◽  
Vol 13 (19) ◽  
pp. 3874
Author(s):  
Xu Ma ◽  
Lei Lu ◽  
Jianli Ding ◽  
Fei Zhang ◽  
Baozhong He
Keyword(s):  
Spatial Resolution ◽  
Precision Agriculture ◽  
Vegetation Cover ◽  
High Spatial Resolution ◽  
World View ◽  
Canopy Reflectance ◽  
Row Crops ◽  
High Spatial Resolution Image ◽  
Fractional Vegetation Cover ◽  
Reflectance Model

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Inference over radiative transfer models using variational and expectation maximization methods

2021 ◽  
Author(s):  
Daniel Heestermans Svendsen ◽  
Daniel Hernández-Lobato ◽  
Luca Martino ◽  
Valero Laparra ◽  
Álvaro Moreno-Martínez ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Expectation Maximization ◽  
Radiative Transfer Models ◽  
Transfer Models

scholarly journals Comparisons of Ocean Radiative Transfer Models With SMAP and AMSR2 Observations

2019 ◽  
Vol 124 (11) ◽  
pp. 7683-7699 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Jacqueline Boutin ◽  
Thomas Meissner ◽  
Stephen English ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Models ◽  
Transfer Models
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