Improving the spatial and temporal estimating of daytime variation in maize net primary production using unmanned aerial vehicle-based remote sensing

Abstract

Crop net primary productivity (NPP) represents the carbon uptake capacity in the agroecosystem carbon cycle. The chamber method is most commonly used to measure crop NPP at the canopy scale. However, the method is highly labor intensive and inefficient, especially when many measurements are required to determine hourly NPP daytime variation. The chamber method also only measures plot-scale NPP and cannot reflect whole-field NPP with high spatial resolution. In this study, the daytime variation in maize (Zea mays L.) NPP in a semiarid area was measured using the chamber method, and the spectral reflectance of the maize field at noon was measured by using an unmanned aerial vehicle (UAV) multispectral system. The objective of this study was to establish a model of daytime NPP variation and then upscale the level of NPP observations based on a UAV multispectral system. Maize maximum NPP (NPPmax) was calculated using a rectangular hyperbola model, and important vegetation indices (VIs) were calculated on the basis of multispectral imagery data. The ratio vegetation index and the red-edge chlorophyll index accounted for 65.3% and 70.3% of the variation in NPPmax, respectively. The multiple regression model with important VIs explained more than 90.4% of the variation in NPPmax. The factor photosynthetically active radiation (PAR) × NPPmax accounted for more than 84.2% of daytime variation in NPP, and when the multiple regression NPPmax model replaced NPPmax, 82.2% of the variation was still explained. The VIs, NPPmax and the PAR × NPPmax were used to obtain a multiple regression daytime NPP variation model, and when the multiple regression NPPmax model replaced NPPmax, the multiple regression model can explain 83.0% of daytime variation in NPP.