Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013–2015 in North Plain China. Results showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CIgreen), red edge normalized ratio (NRred edge), and red-edge chlorophyll index (CIred edge) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. Compared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.

Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level (\({\overline{EWT}}\)) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CI_green), red edge chlorophyll index (CI_{red edge}), and the red edge normalized ratio (NR_{red edge}) were most sensitive to the variations of CWC and \({\overline{EWT}}\). Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and \({\overline{EWT}}\). However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, \({\overline{EWT}}\) and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI).

We estimated the light use efficiency (LUE) via vegetation canopy chlorophyll content (CCC_canopy) based on in situ measurements of spectral reflectance, biophysical characteristics, ecosystem CO₂ fluxes and micrometeorological factors over a maize canopy in Northeast China. The results showed that among the common chlorophyll‐related vegetation indices (VIs), CCC_canopy had the most obviously exponential relationships with the red edge position (REP) (R² = .97, p < .001) and normalized difference vegetation index (NDVI) (R² = .91, p < .001). In a comparison of the indicating performances of NDVI, ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), and 2‐band enhanced vegetation index (EVI2) when estimating CCC_canopy using all of the possible combinations of two separate wavelengths in the range 400−1300 nm, EVI2 [1214, 1259] and EVI2 [726, 1248] were better indicators, with R² values of .92 and .90 (p < .001). Remotely monitoring LUE through estimating CCC_canopy derived from field spectrometry data provided accurate prediction of midday gross primary productivity (GPP) in a rainfed maize agro‐ecosystem (R² = .95, p < .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties.