Accurate estimates of the rain-fed field evapotranspiration (ET) in the Loess Plateau of Northwest China is critical for predicting hydrologic processes, crop yield formation, and climate change. We simulated the ET variation in a rain-fed spring maize field in the eastern Loess Plateau using the Penman-Monteith (PM), Shuttleworth-Wallace (SW), and modified Priestley-Taylor (PTm) models. Then we compared their results with the observed ET using the eddy covariance (EC) method. Generally, the diurnal variation in estimated ET from the three models was similar to that of observed ET by the EC method. However, the PM model significantly underestimated ET. The ET estimates obtained from the SW and PTm models were approximately equal to the observed ET by the EC method. Considering the PTm model’s simplicity, we finally recommend it for rain-fed spring maize fields in the Loess Plateau. After precipitation, the estimated ET from the three models was significantly smaller than measured ET by the EC method, especially the PM model. After a frost, all three models failed to reflect the dramatic decrease in crop transpiration, and thus significantly overestimated ET.

Canopy temperature (T_c) is used to characterize plant water physiology, and thermal infrared (TIR) remote sensing is a convenient technology for measuring T_c in forest ecosystems. However, the images produced through this method contain background pixels of forest gaps, thereby reducing the accuracy of T_c observations. Extracting T_c data from TIR images is of great significance for understanding changes in ecosystem water status. In this study, a temperature threshold method was developed to rapidly, accurately, and automatically extract forest canopy pixels for T_c data obtention. Specifically, this method takes the temperature corresponding to the point with a slope of 0.5 in the curve composed of the normalized average temperature and the normalized cumulative number of pixels as the segmentation threshold to separate the forest gap pixels from the forest canopy pixels in the TIR images and extract the separated forest canopy pixels based on the pixel coordinates for T_c data obtention. Taking the T_c values, measured using a thermocouple, as the standard, T_c extraction using the new temperature threshold method and traditional methods (the Otsu algorithm and direct extraction) was compared in cork oak plantations. The results showed that the temperature threshold method offered the highest extraction accuracy, followed by the direct extraction method and the Otsu algorithm. The temperature threshold method was determined to be the most suitable for extracting T_c data from the TIR images of cork oak plantations.

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model’s accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model’s performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.

Monitoring drought stress is crucial for estimating productivity and assessing the health status of forest ecosystems under global climate change. Solar-induced chlorophyll fluorescence (SIF) is mechanistically coupled to photosynthesis and has advantages over greenness-based vegetation indices in detecting drought. In recent years, SIF has commonly been used in monitoring drought stress in crop ecosystems. However, the response of tower-based SIF to drought stress in forest ecosystems remains unclear. In this study, we investigated the potential of tower-based SIF to monitor drought, which was quantified using the plant water stress index (PWSI) in a Chinese cork oak plantation. The results show the negative effect of drought on SIF, and afternoon depression of SIF emission under drought stress was observed. Canopy SIF (F) exhibited a nonlinear relationship with PWSI, while the quantum yield of SIF (ΦF) exhibited a significant linear relationship with PWSI at 687 nm and 760 nm (ΦF₆₈₇: R² = 0.90; ΦF₇₆₀: R² = 0.85). Incident radiation (PAR) and canopy structure affected the response of SIF to drought stress, with PAR as the main factor causing the nonlinear relationship between F and PWSI. Afternoon depression was described as the afternoon/morning ratio (AMR). AMR_F and AMR_ΦF exhibited a negative linear response to PWSI. AMR_F was less affected than F by PAR and canopy structures, and AMR_ΦF was more physiologically representative than ΦF. Moreover, AMR_ΦF was sensitive to VPD and REW, and it might be a good indicator of drought. Red SIF was more sensitive to drought than far-red SIF, as the R² of PWSI with AMR_ΦF687 (R² = 0.89) was higher than that with AMR_ΦF687 (R² = 0.84). These results highlight the potential of tower-based SIF, especially red SIF, for drought monitoring in a plantation, and consideration of the physiological diurnal variation in SIF under drought stress is crucial for improving the accuracy of drought stress monitoring.