Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Nigam, Rahul; Bhattacharya, Bimal K.; Vyas, Swapnil; Oza, M. P.

doi:10.1016/j.jag.2014.04.003

Cited by 17 publications

(20 citation statements)

References 51 publications

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“…In this study, the 10% threshold of the sorted smallest value in the LUT size of 200,000 was used to select the entries from the LUT. The 10% threshold is feasible for balancing computer resources and the accuracy of the variable estimates and is consistent with the optimal number adopted by previous studies [74][75][76][77].…”

Section: Lai Inversion By the Prosail Radiative Transfer Modelsupporting

confidence: 83%

“…To achieve an appropriate representation, a total number of 200,000 reflectance data for LUT has been generated in this study. To overcome an ill-posed inversion problem resulting from measurement errors and a similar response of radiometric signals to different variable combinations in the LUT, the solution should be the average of the parameter combinations that yield the smallest values of the cost function between the remotely-sensed reflectance and the simulated reflectance [73,74]. In this study, the 10% threshold of the sorted smallest value in the LUT size of 200,000 was used to select the entries from the LUT.…”

Section: Lai Inversion By the Prosail Radiative Transfer Modelmentioning

confidence: 99%

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Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

Chen

Liu

et al. 2017

Remote Sensing

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Abstract:To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg·ha −1 , 543 kg·ha −1 and 172 kg·ha −1 and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAIs were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and anthesis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional wheat estimates.

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Section: Lai Inversion By the Prosail Radiative Transfer Modelsupporting

confidence: 83%

Section: Lai Inversion By the Prosail Radiative Transfer Modelmentioning

confidence: 99%

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

Chen

Liu

et al. 2017

Remote Sensing

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“…Besides the presence of agricultural regions, this distribution of test sites mainly reflects logistic issues, i.e., the possibility of collecting ground data in the frame of field campaigns. Moreover, there has been an increase in the number of Asian study sites, e.g., for Uzbekistan [145], India [171], and in particular China [151,172,173].…”

Section: Geographic Locationsmentioning

confidence: 99%

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

et al. 2018

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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.

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“…This coupled modelling scheme has already been applied with success to a number of crops such as wheat, maize, rice, and soybean [28][29][30][31]. In our study, PROSAIL5B was used to simulate the canopy reflectance spectra of oilseed rape [32,33].…”

Section: Prosail Model and Simulated Datasetmentioning

confidence: 99%

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

et al. 2017

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Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014-2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R 2 > 0.954 and RMSE <0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R 2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture.

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Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Cited by 17 publications

References 51 publications

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

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

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

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