Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach

Darvishzadeh, Roshanak; Matkan, A A; Dashti, Hamid

doi:10.1109/jstars.2012.2186118

Cited by 109 publications

(92 citation statements)

References 43 publications

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“…The decametric maps produced from Landsat-7/8 data through the developed inversion of RTM with GPR provided RMSEs values (0.75, 0.50 and 0.61 m 2 /m 2 for 2014, 2015 and 2016, respectively) comparable to results previously obtained with real Sentinel-2A data [15], and outperformed previous LAI retrieval studies over rice areas using PROSAIL LUT inversion (RMSE = 2.26 m 2 /m 2 ) [58]. Similar accuracy in LAI retrievals has been reported using LUT inversion of PROSAIL over other cereal crops, e.g., RMSE = 0.9 m 2 /m 2 over maize [59] and RMSE of 0.42 and 0.64 m 2 /m 2 over maize and wheat respectively [60].…”

Section: Discussionsupporting

confidence: 72%

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

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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.

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Section: Discussionsupporting

confidence: 72%

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

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“…The PROSAIL radiative transfer model was ran in forward mode for building a database, which was used for training the retrieval model and for mimicking canopy reflectance using the turbid medium assumption, which is particularly well suited for homogeneous canopies like rice [18,35]. It assumes the canopy as a turbid medium for which leaves are randomly distributed.…”

Section: Retrieval Methodologymentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

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“…The inversion consists of adjusting the input biophysical variables to reduce the error between the simulated and measured reflectance [36,37]. While these techniques have been applied with success [38,39], they can be computationally demanding. In addition, they suffer from the so-called ill-posed problem [40,41] due to model and measurements uncertainties; that is, different model parameters might result in very similar spectra [42].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R 2 > 0.5). Indices with spectral bands around the red edge (705-753 nm) were more OPEN ACCESSRemote Sens. 2015, 7 14531 sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R 2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 μg·cm −2 , and R 2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

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Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach

Cited by 109 publications

References 43 publications

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

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

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

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