Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions

Abstract

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).

Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland

“…Despite the low number of available pairs of “EVI, fAPAR” for only one year, the high correlation observed (r 2 = 0.93) confirms the previous findings on the linear relationship between fAPAR and VI. In a comparative study, EVI was found to be the best proxy for fAPAR in drought conditions in a maize field, with r 2 = 0.69 [ 51 ]. A good quadratic relationship (r 2 = 0.88) was found between fAPAR and EVI for maize and soya fields, even though NDVI performed better in those ecosystems [ 52 ].…”

Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops

“…fAPAR depends on solar zenith angle and illumination conditions, e.g., overcast or clear sky condition. The instantaneous fAPAR is highly sensitive to variations of the solar zenith angle and presents diurnal variations under clear sky conditions (Fensholt et al, 2004;Rahman et al, 2015;Zhao et al, 2018), while it shows a much smaller diurnal variations under cloudy conditions (Nouvellon, 2000;Thomas et al, 2006). The daily integrated fAPAR, which is a variable used by many canopy functioning models, has been demonstrated to be smaller under clear sky as compared to overcast conditions (Gower et al, 1999;Thomas et al, 2006).…”

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

“…The accuracy was assessed using the user's accuracy, producer's accuracy and the kappa coefficient. Kappa coefficient (k) is mathematically expressed as [53][54][55][56]:…”