Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau

Abstract: Taiyuan basin in Chinese Loess Plateau was characterized as the variety of landform, land use fragmentation, low soil organic matter (SOM) and soil total nitrogen (STN) content; therefore, the predictions of soil nutrients in the area were rather difficult. In this study, three soil sampli...

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Autores principales:	Zhu,Hongfen, Xu,Zhanjunm, Jing,Yaodong, Bi,Rutian, Yang,Wude
Lenguaje:	English
Publicado:	Chilean Society of Soil Science / Sociedad Chilena de la Ciencia del Suelo 2018
Materias:	Partial least square regression (PLSR) soil organic matter (SOM) soil total nitrogen (STN) spatial variation scale
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000401126
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spelling	oai:scielo:S0718-951620180004011262019-02-05Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess PlateauZhu,HongfenXu,ZhanjunmJing,YaodongBi,RutianYang,Wude Partial least square regression (PLSR) soil organic matter (SOM) soil total nitrogen (STN) spatial variation scale Abstract: Taiyuan basin in Chinese Loess Plateau was characterized as the variety of landform, land use fragmentation, low soil organic matter (SOM) and soil total nitrogen (STN) content; therefore, the predictions of soil nutrients in the area were rather difficult. In this study, three soil sampling transects of cropland soil from northwest to southeast in Taiyuan basin were established and the visible-near infrared reflectance (VNIR) of soil samples were measured. The predicting models for SOM and STN based on VNIR were established, and the predicting accuracies were assessed by traditional evaluating index, wavelet transform, and semivariance structure. The traditional evaluating index showed that the partial least square regression (PLSR) and optimum number of latent variables were suitable for SOM prediction. The accuracies were “good” (RPD ranges from 2.30 to 2.40) for calibration and “moderate” (RPD ranges from 1.80 to 1.95) for validation, whereas the model and parameters of STN were “moderate” (RPD ranges from 1.83 to 1.87) for calibration and “acceptable” (RPD ranges from 1.41 to 1.48) for validation procedure. Based on the wavelet transform, the patterns of global wavelet power spectrum for predicted and measured SOM were closer than that of STN, and their difference in local wavelet spectra could present the predicting errors in the scale and location domain. The nugget effect indicated that the stochastic variability weakened, and the spatial structure of predicted SOM and STN enhanced. The range of predicted SOM and STN were greater than those of measured. Therefore, the predicting models based on independent dataset using PLSR could be used for the prediction of SOM or STN in the un-sampled areas. Wavelet transform and semivariance parameters could be used to guide the utilization of predicted values.info:eu-repo/semantics/openAccessChilean Society of Soil Science / Sociedad Chilena de la Ciencia del SueloJournal of soil science and plant nutrition v.18 n.4 20182018-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000401126en10.4067/S0718-95162018005003103
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Partial least square regression (PLSR) soil organic matter (SOM) soil total nitrogen (STN) spatial variation scale
spellingShingle	Partial least square regression (PLSR) soil organic matter (SOM) soil total nitrogen (STN) spatial variation scale Zhu,Hongfen Xu,Zhanjunm Jing,Yaodong Bi,Rutian Yang,Wude Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
description	Abstract: Taiyuan basin in Chinese Loess Plateau was characterized as the variety of landform, land use fragmentation, low soil organic matter (SOM) and soil total nitrogen (STN) content; therefore, the predictions of soil nutrients in the area were rather difficult. In this study, three soil sampling transects of cropland soil from northwest to southeast in Taiyuan basin were established and the visible-near infrared reflectance (VNIR) of soil samples were measured. The predicting models for SOM and STN based on VNIR were established, and the predicting accuracies were assessed by traditional evaluating index, wavelet transform, and semivariance structure. The traditional evaluating index showed that the partial least square regression (PLSR) and optimum number of latent variables were suitable for SOM prediction. The accuracies were “good” (RPD ranges from 2.30 to 2.40) for calibration and “moderate” (RPD ranges from 1.80 to 1.95) for validation, whereas the model and parameters of STN were “moderate” (RPD ranges from 1.83 to 1.87) for calibration and “acceptable” (RPD ranges from 1.41 to 1.48) for validation procedure. Based on the wavelet transform, the patterns of global wavelet power spectrum for predicted and measured SOM were closer than that of STN, and their difference in local wavelet spectra could present the predicting errors in the scale and location domain. The nugget effect indicated that the stochastic variability weakened, and the spatial structure of predicted SOM and STN enhanced. The range of predicted SOM and STN were greater than those of measured. Therefore, the predicting models based on independent dataset using PLSR could be used for the prediction of SOM or STN in the un-sampled areas. Wavelet transform and semivariance parameters could be used to guide the utilization of predicted values.
author	Zhu,Hongfen Xu,Zhanjunm Jing,Yaodong Bi,Rutian Yang,Wude
author_facet	Zhu,Hongfen Xu,Zhanjunm Jing,Yaodong Bi,Rutian Yang,Wude
author_sort	Zhu,Hongfen
title	Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
title_short	Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
title_full	Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
title_fullStr	Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
title_full_unstemmed	Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau
title_sort	spatial variation and predictions of soil organic matter and total nitrogen based on vnir reflectance in a basin of chinese loess plateau
publisher	Chilean Society of Soil Science / Sociedad Chilena de la Ciencia del Suelo
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000401126
work_keys_str_mv	AT zhuhongfen spatialvariationandpredictionsofsoilorganicmatterandtotalnitrogenbasedonvnirreflectanceinabasinofchineseloessplateau AT xuzhanjunm spatialvariationandpredictionsofsoilorganicmatterandtotalnitrogenbasedonvnirreflectanceinabasinofchineseloessplateau AT jingyaodong spatialvariationandpredictionsofsoilorganicmatterandtotalnitrogenbasedonvnirreflectanceinabasinofchineseloessplateau AT birutian spatialvariationandpredictionsofsoilorganicmatterandtotalnitrogenbasedonvnirreflectanceinabasinofchineseloessplateau AT yangwude spatialvariationandpredictionsofsoilorganicmatterandtotalnitrogenbasedonvnirreflectanceinabasinofchineseloessplateau
_version_	1714206590106599424

Spatial variation and predictions of soil organic matter and total nitrogen based on VNIR reflectance in a basin of Chinese Loess Plateau

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