Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Spatial prediction of soil ammonia (NH<sub>3</sub>) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration...

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Bibliographic Details
Main Authors: Yingqiang Song, Mingzhu Ye, Zhao Zheng, Dexi Zhan, Wenxu Duan, Miao Lu, Zhenqi Song, Dengkuo Sun, Kaizhong Yao, Ziqi Ding
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Remote Sensing
Subjects:
soil ammonia
XGBoost
ordinary kriging
spatial prediction
hyperparameter
Online Access:https://www.mdpi.com/2072-4292/15/17/4268

Internet

https://www.mdpi.com/2072-4292/15/17/4268

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