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Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer

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Title: Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer
Authors: Santoso, Heri Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Segah, Hendrik Browse this author
Keywords: macronutrient
micronutrient
oil palm
spectral
reflectance
spectroradiometer
Issue Date: 2-Oct-2019
Publisher: Taylor & Francis
Journal Title: International journal of remote sensing
Volume: 40
Issue: 19
Start Page: 7581
End Page: 7602
Publisher DOI: 10.1080/01431161.2018.1516323
Abstract: Leaf nutrients are needed for oil palm growth and production, and the nutrient contents of oil palm leaves can be determined by the chemical analyses of the number 9 and 17 leaves for young and adult palms, respectively. However, the accurate selection of the proper leaf for sampling is problematic. Remote sensing techniques based on the reflectance values of leaves may easily monitor leaf nutrients in oil palm plantations. We studied leaf nutrient contents using spectral reflectance data to determine suitable wavelengths for predicting the contents of the most important leaf nutrients: nitrogen, phosphorus, potassium, calcium, magnesium, boron, copper, and zinc. The samples were taken from one oil palm plantation in Pundu, Central Kalimantan, Indonesia. The proposed vegetative indices, several common vegetative indices, and a stepwise regression that continued with a principal component regression were used to build models for predicting leaf nutrient contents. The proposed vegetative indices performed better than the common vegetative indices. For each of the leaf nutrients, models that included all of the significant variables from the stepwise regression and continued with principal component regression from the ultraviolet A and green to far red wavelength groups had better performance levels than models that included individually selected variables selected from each wavelength group. For total leaf nutrient content predictions, variables from the green wavelength group were always selected and contributed more to the models than any other group. Thus, our proposed vegetative indices and multivariate model may be used to predict leaf nutrient contents in oil palm plantations.
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal International Journal of Remote Sensing on 20 Sep 2018, available online: http://www.tandfonline.com/10.1080/01431161.2018.1516323.
Type: article (author version)
URI: http://hdl.handle.net/2115/75478
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Heri Santoso

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