Sun-induced Fluorescence (SIF) and Photochemical Reflectance Index (PRI) data were collected in the field over maize to study their diurnal responses to different water stresses at the canopy scale. An automated field spectroscopy system was used to obtain continuous and long-term measurements of maize canopy in four field plots with different irrigation treatments. This system collects visible to near-infrared spectra with a spectrometer, which provides a sub-nanometer spectral resolution in the spectral range of 480~850 nm. The red SIF (F_R) and far red SIF (F_FR) data were retrieved by Spectral Fitting Methods (SFM) in the $O_2$ -A band and $O_2$ -B band, respectively. In addition to PRI, $\Delta \mathrm{PRI}$ values were derived from PRI by subtracting an early morning PRI value. Photosynthetic active radiation (PAR) data, the canopy fraction of absorbed PAR (fPAR), and the air/canopy temperature and photosystem II operating efficiency (YII) at the leaf scale were collected concurrently. In this paper, the diurnal dynamics of each parameter before and after watering at the jointing stage were compared. The results showed that (i) both F_R and F_FR decreased under water stress, but F_R always peaked at noon, and the peak of F_FR advanced with the increase in stress. Leaf folding and the increase in Non-photochemical Quenching (NPQ) are the main reasons for this trend. Leaf YII gradually decreased from 8:00 to 14:00 and then recovered. In drought, leaf YII was smaller and decreased more rapidly. Therefore, the fluorescence yield at both the leaf and canopy scale responded to water stress. (ii) As good indicators of changes in NPQ, diurnal PRI and $\Delta \mathrm{PRI}$ data also showed specific decreases due to water stress. $\Delta \mathrm{PRI}$ can eliminate the impact of canopy structure. Under water stress, $\Delta \mathrm{PRI}$ decreased rapidly from 8:00 to 13:00, and the maximum range of this decrease was approximately 0.05. After 13:00, their values started to increase but could not recover to their morning level. (iii) Higher canopy-air temperature differences ( $\Delta \mathrm{T}$ ) indicate that stomatal closure leads to an increase in leaf temperature, which maintains a higher state in the afternoon. In summary, to cope with water stress, both leaf folding and changes in physiology are activated. To monitor drought, SIF performs best around midday, and PRI is better after noon.

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).