Developing an integrated indicator for monitoring maize growth condition using remotely sensed vegetation temperature condition index and leaf area index

•The developed index is very reliable and promising for estimating the growth conditions of maize.•Integrating more growth-correlated variables improves the maize growth monitoring accuracy.•GRA and AHP as useful weighting methods to acquire priorities of indicators at different stages.•Classification standards established to improve precision and quantification of regional monitoring results. Early and accurate assessment of maize growth is important for national food security. To improve the accuracy of maize growth monitoring in the central plain of Hebei Province, PR China, multiple growth-related factors, including water stress and vegetation coverage, should be comprehensively considered. This study derived the ten-day vegetation temperature condition index (VTCI) and leaf area index (LAI) from the first ten days of July to the third ten-day of September during 2010–2017 from MODIS data. Then, the grey relational analysis (GRA) method and analytic hierarchy process (AHP) were used to determine the weight coefficients of the VTCI and LAI at four maize growth stages (the emergence-jointing, jointing-booting, booting-filling and filling-mature stages). Thus, an integrated maize growth monitoring index (G) was formulated for maize growth estimation at the main growth stage. Linear regression models between maize yields and G values for counties in five cities of the Hebei Plain from 2010 to 2015 were constructed to verify and analyze the precision of maize growth monitoring. The weight coefficients of the VTCI and LAI varied at the four growth stages. In Cangzhou City, the LAI weight coefficient at the jointing-booting stage was the highest, followed by the VTCI at the booting-filling stage, indicating that maize growth conditions and yields were highly correlated with vegetation coverage at the jointing-booting stage and that maize growth was most sensitive to water stress at the booting-filling stage. Linear regression models between G values and maize yields for the counties in the five cities all passed the significance test at the 0.01 level. Moreover, the correlation between G values and maize yields was closer than that between maize yields and VTCI or LAI alone and illustrated a high accuracy of the integrated maize monitoring results derived from the synthetic approach combining the two indices. According to the maize growth monitoring results, from 2010 to 2017, the best year regarding maize growth conditions was 2011, and the worst year was 20