Next Article in Journal
Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time
Previous Article in Journal
SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(4), 505;

Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands

Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Department of Physics, Geography and Environmental Science, Great Zimbabwe University, P.O. Box 1235, Masvingo 00263, Zimbabwe
Department of Geography & Environmental Science, University of Zimbabwe, P.O. Box MP 167, Mount Pleasant, Harare 00263, Zimbabwe
Author to whom correspondence should be addressed.
Received: 23 November 2017 / Revised: 13 January 2018 / Accepted: 20 January 2018 / Published: 23 March 2018
Full-Text   |   PDF [5750 KB, uploaded 23 March 2018]   |  


Remote sensing has been widely used to estimate the distribution of foliar nitrogen (N) in a cost-effective manner. Although hyperspectral remote sensing targeting the red edge and shortwave infrared regions has proved successful at estimating foliar N, research has recently shifted to include exploring the benefits of using the near-infrared (NIR) region, especially when using broadband sensing. Bootstrapped random forest regression analysis was applied on Sentinel 2 data to test the significance of using the NIR in foliar N estimation in miombo woodlands. The results revealed a low ranking for individual NIR bands, but the ranking improved when spectral indices were used. In addition, the results indicated a marginal increase in the normalised root mean square error of prediction (nRMSE) from 11.35% N when all bands were used to 11.69% N when the NIR bands were excluded from the model. Bootstrapping results show higher accuracy and better consistency in the prediction of foliar N using combined spectral indices and individual bands. This study therefore underscores the significance of spectral indices to increase the NIR region’s importance in estimating the distribution of foliar N as a key indicator of ecosystem health at the landscape scale in miombo systems. View Full-Text
Keywords: nitrogen; remote sensing; near infrared; miombo; random forest regression nitrogen; remote sensing; near infrared; miombo; random forest regression

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Mutowo, G.; Mutanga, O.; Masocha, M. Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands. Remote Sens. 2018, 10, 505.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top