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Article

Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

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Key Laboratory of Crop System Analysis and Decision Making, National Engineering and Technology Center for Information Agriculture, Ministry of Agriculture and Rural Affairs, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, China
2
Department of Natural Resources and Society, College of Natural Resources, University of Idaho (UI), 875 Perimeter Drive, Moscow, ID 83843, USA
3
McCall Outdoor Science School, College of Natural Resources, University of Idaho, 1800 University Lane, McCall, ID 83638, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Conghe Song and Thomas Udelhoven
Remote Sens. 2021, 13(17), 3502; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173502
Received: 21 July 2021 / Revised: 19 August 2021 / Accepted: 31 August 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice. View Full-Text
Keywords: potassium nutrition; spectral index; machine learning; meteorological factors; optimal model potassium nutrition; spectral index; machine learning; meteorological factors; optimal model
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MDPI and ACS Style

Lu, J.; Eitel, J.U.H.; Jennewein, J.S.; Zhu, J.; Zheng, H.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.; Tian, Y. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation. Remote Sens. 2021, 13, 3502. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173502

AMA Style

Lu J, Eitel JUH, Jennewein JS, Zhu J, Zheng H, Yao X, Cheng T, Zhu Y, Cao W, Tian Y. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation. Remote Sensing. 2021; 13(17):3502. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173502

Chicago/Turabian Style

Lu, Jingshan, Jan U.H. Eitel, Jyoti S. Jennewein, Jie Zhu, Hengbiao Zheng, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, and Yongchao Tian. 2021. "Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation" Remote Sensing 13, no. 17: 3502. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173502

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