Abstract
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.
Highlights
The normalized RMSEV (nRMSE) of multiple LR (MLR) was evenly distributed in the range of 0–26%, and most of the nRMSE of hierarchical linear modeling (HLM) was below 15% (Figure 5a)
The nRMSE difference between the MLR and HLM models was controlled within 5%, and and wide dynamic range vegetation index (WDRVI) [53] at crucial growth stages could evaluate maize yield due to the direct nRMSE distribution was random with time and region
The best result was by the model with normalized difference vegetation index (NDVI) (R2 = 0.46, RMSEV = 2.08 t/ha, nRMSE = 21.72%)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Accurate yield predictions can help individual farmers to improve field management, achieve higher yield in time [5,6,7,8,9], and provide relevant and valuable information to private companies (e.g., crop insurers or commodity traders) [10] It is a strong guarantee for national governments and international institutions (especially in developing countries) to strengthen supply chain management, food safety, and quality assessment. The correlation between yield and remote sensing information varies in different areas and years, which forms another level to explain unexplained yield changes in weather models [35,36] This possibility assumes that HLM can be an effective method for predicting crop yields under different climatic conditions. In this study, Landsat 8 images, meteorological and measured yield data were acquired to determine the optimal vegetation index (VI) for maize-yield prediction using the LR method, and to build regional maize-yield prediction models using HLM and assess its accuracy
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