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Open AccessArticle

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

1
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for the Technology and Application of Internet of Things, Nanjing Agricultural University, Nanjing 210095, China
2
Department of Geography, Minnesota State University, Mankato, MN 56001, USA
3
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1763; https://doi.org/10.3390/rs11151763
Received: 20 June 2019 / Revised: 20 July 2019 / Accepted: 23 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications. View Full-Text
Keywords: leaf area index (LAI); UAV RGB imagery; color index; texture; rice leaf area index (LAI); UAV RGB imagery; color index; texture; rice
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MDPI and ACS Style

Li, S.; Yuan, F.; Ata-UI-Karim, S.T.; Zheng, H.; Cheng, T.; Liu, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cao, Q. Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sens. 2019, 11, 1763. https://doi.org/10.3390/rs11151763

AMA Style

Li S, Yuan F, Ata-UI-Karim ST, Zheng H, Cheng T, Liu X, Tian Y, Zhu Y, Cao W, Cao Q. Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sensing. 2019; 11(15):1763. https://doi.org/10.3390/rs11151763

Chicago/Turabian Style

Li, Songyang; Yuan, Fei; Ata-UI-Karim, Syed T.; Zheng, Hengbiao; Cheng, Tao; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang. 2019. "Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation" Remote Sens. 11, no. 15: 1763. https://doi.org/10.3390/rs11151763

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