SAILHFlood: A radiative transfer model for flooded vegetation

Beget, María Eugenia; Bettachini, Victor A.; Bella, Di; Baret, Frédéric

doi:10.1016/j.ecolmodel.2013.02.025

Cited by 14 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing models mostly use typical soil reflectances from the spectral library (S. Jacquemoud et al, ) or derive them from soil reflectance models (Hapke, ; Price, ; Walthall et al, ). Contribution from more complicated background elements, for example, water and snow and understory vegetation, should be included in new modeling studies (Beget et al, ; G. Zhou et al, ). For algorithms that use ancillary land cover type as a priori information, errors in land cover will propagate to the LAI product and should be assessed formerly and be minimized where possible (J. Hu et al, ; Pocewicz et al, ).…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

554

252

View full text Add to dashboard Cite

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

show abstract

Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

554

252

View full text Add to dashboard Cite

show abstract

“…Both Landsat-7/8 and Sentinel-2A LAI showed good accuracy in the first stages of the season. An alternative to PROSAIL would be the use of SAILHFlood [61], which was specifically designed for simulating partial submerged canopy. SAILHFlood is a modification of SAILH RTM in which the emerged vegetation layer and the submerged vegetation layer are simulated, thus accounting for both emerged and submerged LAI.…”

Section: Discussionmentioning

confidence: 99%

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

et al. 2018

View full text Add to dashboard Cite

Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetationand agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10-30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016-from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R 2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high-and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R 2 ≥ 0.93 and RMSE ≤ 0.53 m 2 /m 2 . In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R 2 ≥ 0.90) and RMSE ≤ 0.80 m 2 /m 2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services.

show abstract

“…Special model versions for specific applications were also developed, such as SAILHFlood for submerged vegetation [52]. For the representation of heterogeneous canopies like forests, the "Invertible Forest Reflectance Model" (INFORM) was established [53], which is essentially a combination of the "Forest Light Interaction Model" (FLIM) [54], SAILH, and PROSPECT models.…”

Section: Model Variantsmentioning

confidence: 99%

“…Special model versions for specific applications were also developed, such as SAILHFlood for submerged vegetation [52]. For the representation of heterogeneous canopies like forests, the …”

Section: Model Variantsmentioning

confidence: 99%

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

et al. 2018

View full text Add to dashboard Cite

Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

show abstract

SAILHFlood: A radiative transfer model for flooded vegetation

Cited by 14 publications

References 36 publications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Contact Info

Product

Resources

About