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Open AccessArticle

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
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Academic Editor: Emanuel Peres
Remote Sens. 2021, 13(3), 356; https://doi.org/10.3390/rs13030356
Received: 11 December 2020 / Revised: 14 January 2021 / Accepted: 17 January 2021 / Published: 21 January 2021
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry)
The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields. View Full-Text
Keywords: yield; maize; hierarchical linear modeling (HLM); meteorological data yield; maize; hierarchical linear modeling (HLM); meteorological data
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MDPI and ACS Style

Zhu, B.; Chen, S.; Cao, Y.; Xu, Z.; Yu, Y.; Han, C. A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province. Remote Sens. 2021, 13, 356. https://doi.org/10.3390/rs13030356

AMA Style

Zhu B, Chen S, Cao Y, Xu Z, Yu Y, Han C. A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province. Remote Sensing. 2021; 13(3):356. https://doi.org/10.3390/rs13030356

Chicago/Turabian Style

Zhu, Bingxue; Chen, Shengbo; Cao, Yijing; Xu, Zhengyuan; Yu, Yan; Han, Cheng. 2021. "A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province" Remote Sens. 13, no. 3: 356. https://doi.org/10.3390/rs13030356

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