Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image

Xiaohe Gu; Yancang Wang; Xiaoyu Song; Xingang Xu

doi:10.1117/12.2241240

25 October 2016 Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image

Xiaohe Gu, Yancang Wang, Xiaoyu Song, Xingang Xu

Proceedings Volume 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII; 99981G (2016) https://doi.org/10.1117/12.2241240
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Monitoring dry biomass of crop timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin in China was chose as study area to map the spatial distribution of paddy biomass. The study derived 12 vegetation indexes from HJ-CCD image, which were closely related to crop growth. After screening sensitive vegetation index with in-situ samples by correlation analysis, the study developed the inversion model by single variable and multiple variables. The determination coefficient (R²) and root mean square error (RMSE) was used to evaluate the accuracy of models. Results showed that the accuracies of multivariable models were better than these of single-variable models, of which the average R²reached 0.647 and the average RMSE was 0.059. It indicated that the multi-variable models were input in more information than those of single-variable models, which improved the accuracies of estimating paddy biomass in to a certain degree. The average overall accuracies of multi-variable models were 92.7%, while that of singe-variable models were 87.8%. The model with multiple linear regressions could be used to map the paddy biomass in the study area by using HJ-CCD image.

Citation Download Citation

Xiaohe Gu, Yancang Wang, Xiaoyu Song, and Xingang Xu "Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981G (25 October 2016); https://doi.org/10.1117/12.2241240

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KEYWORDS

Vegetation

Near infrared

Biological research

CCD image sensors

Charge-coupled devices

Statistical modeling

Remote sensing

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