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.2018 Apr 6;18(4):1120.
doi: 10.3390/s18041120.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

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Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

He Li et al. Sensors (Basel)..

Abstract

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R²) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R² of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods.

Keywords: GF-1; PROSAIL; leaf area index; look-up table; winter wheat.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Location of the study area (GF-1 imagery displayed in this figure with false color composite: R = Near Infrared, G = red, B = Green).
Figure 2
Figure 2
Spectral responses functions of the blue, green, red and near-infrared bands for GF-1 WFV1.
Figure 3
Figure 3
Flow chart of remotely sensed winter wheat leaf area index inversion.
Figure 4
Figure 4
Performances of LAI-LUT strategies with different bands of reflectance to estimate LAI based on the GF-1 data on 14 April (a) and 25 May (b).
Figure 5
Figure 5
Performances of LAI-LUT strategies with different VIs to estimate the LAI based on the GF-1 data on 14 April (a) and at 25 May (b).
Figure 6
Figure 6
Estimated regional winter wheat LAI maps derived using the LAI-Green and LAI-GNDVI strategies on 14 April (a) and 25 May (b).
See this image and copyright information in PMC

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