Inversion of paddy leaf area index using Beer-Lambert law and HJ-1/2 CCD image.

Monitoring crop leaf area index (LAI) timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin was chose as study area to carry out field survey. By using decision tree classification and HJ-1/2 CCD image, the spatial distribution of paddy was identified. The extinction coefficient of paddy surface was confirmed with in-situ samples. The Beer-Lambert law was introduced to develop the inversion model of paddy LAI. The accuracy of inversion model was evaluated with in-situ samples, including coefficient of determination (R2), RMSE and overall accuracy, while contrasting with the model of single-variable and multi-variables. Results showed that the inversion model based on Beer-Lambert law reached highest accuracy with the average R2 of 0.684 and the average RMSE of 0.592. The average R2 of multi-variables was 0.636, while the average RMSE was 0.661. The model of single-variable has lowest accuracy with average R2 of 0.595 and average RMSE of 0.732. It indicated that the retrieval accuracy of LAI was improved with more variables inputted. The model based on Beer-Lambert law simulated the physical process of radiative transfer of paddy that differed from the two other models. The overall accuracy of Beer-Lambert law model exceeded 95 percent, while those of the two other models were 91.0 percent and 88.2 percent respectively. So the inversion model of paddy LAI based on Beer-Lambert law could eliminate the influence of water background and improve the accuracy of paddy LAI by remote sensing. [ABSTRACT FROM PUBLISHER]