Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Evaluating the accuracy of nine canopy resistance models in estimating winter wheat evapotranspiration using the Penman-Monteith equation

Accurate estimation of farmland evapotranspiration (ET) is crucial for agricultural production. The accuracy of the widely used Penman-Monteith (PM) equation for estimating crop ET depends on the quality of input data and their ability to accurately model the canopy resistance (r c). In this study, we evaluated the PM equation in estimating winter wheat ET using nine r c models, with both original and recalibrated parameters, including the Farias (FA), Monteith (MT), Garcίa-Santos (GA), Idso (IS), Jarvis (JA), Katerji-Perrier (KP), Stannard (ST), Todorovic (TD), and Coupled surface resistance (CO) models. We used long-term measurements (2018 to 2023) from the Bowen ratio energy balance method at both daily and seasonal scales. Parameterization was performed using data from the 2020-2021 growing season, while the remaining 4 years were used for verification. The results showed that the FA, KP, and ST models performed better in estimating daily ET with original parameters, achieving a root mean square error (RMSE) of 1.07-1.16 mm d-1 and a mean bias error (MBE) of -0.59-0.02 mm d-1. After parameterization, the performance of acceptable r c models based on RMSE (ranging from 1.07 to 1.22 mm d-1, averaged 1.16 mm d-1) ranked as follows on the daily scale: FA > CO > KP > ST > IS > GA > JA > MT. The r c models were more accurate in simulating ET on a seasonal scale than on the daily scale. Before calibration, the acceptable FA, KP, and MT models overestimated seasonal ET with the MBE ranging from 2.83 to 75.32 mm and RMSE from 29.79 to 82.38 mm. After correction, the suitable r c models based on RMSE values decreased by FA > CO > KP > IS > ST > GA > JA on the seasonal scale, which ranged from 29.79 to 76.35 mm. The performance of the revised r c models improved on both daily and seasonal scales, with RMSE reductions of 29.03% and 68.18%, respectively. Considering both the accuracy and calculation complexity, the FA and KP models were recommended to be used in the PM equation to estimate daily and seasonal ET in semiarid regions. The CO, GA, ST, IS, and JA models can also be used as alternatives, depending on the availability of meteorological parameters.

Estimation of Landsat-like daily evapotranspiration for crop water consumption monitoring using TSEB model and data fusion

Evapotranspiration (ET) plays an essential role in agricultural water resource management. Understanding regional agricultural water consumption characteristics can be improved by predicting ET using remote sensing. However, due to the lack of high-resolution images on clear-sky days or the limitation of ET reconstruction on cloudy-sky days, it remains challenging to continuously derive ET at the field scale. In this study, the Landsat and MODIS data were initially fused to obtain the Landsat-like vegetation index and land surface temperature on clear-sky days. Then the two-source energy balance (TSEB) model was applied to calculate the daily ET during the clear-sky. A canopy resistance-based gap-filling method was involved in reconstructing regional ET on cloudy days while considering different environmental factors. The estimations were validated by automatic weather system data (AWS) and eddy covariance (EC) measurements in Guantao County. The results demonstrated that the proposed scheme performed well in estimating cropland ET, with an RMSE of 0.86 mm·d⁻¹ and an R² of 0.65, and the NSE and PBias were 0.61 and -0.29%, respectively. The crop water consumption analysis revealed that the daily ET of winter wheat peaked during the maturation stage. Nevertheless, summer maize water consumption peaked in the middle of the growing season in this area. The temperature during the early development stage and the soil moisture in the mid and late growth stages had the greatest impact on the ET of winter wheat. During the entire growing period, soil moisture had the largest effect on the ET of summer maize. The findings showed that the TSEB model can be effectively applied to field-scale water consumption monitoring in North China through MODIS and Landsat data fusion and ET temporal reconstruction considering environmental factors.

Incorporation of Net Radiation Model Considering Complex Terrain in Evapotranspiration Determination with Sentinel-2 Data

Evapotranspiration (ET) is the primary mechanism of water transformation between the land surface and atmosphere. Accurate ET estimation given complex terrain conditions is essential to guide water resource management in mountainous areas. This study is based on the ETWatch model driven by Sentinel-2 remote sensing data at a spatial resolution of 10 m incorporating a net radiation model considering the impact of a complex terrain. We tested our model with two years of data in two regions with a high relief near the Huairou (2020) and Baotianman (2019) weather stations. Regarding the validation results of the ET model, the coefficient of determination (R2) reached 0.84 in Huairou and 0.86 in Baotianman, while the root mean square error (RMSE) value reached 0.59 mm in Baotianman and 0.82 mm in Huairou. The validation results indicated that the model is applicable in regions with a complex terrain, and the ET results can capture topographic textures. In terms of the slope aspect, the ET value on south-facing slopes is higher than that on north-facing slopes in both study areas. Accurate ET monitoring in mountainous regions with a high relief yields a profound meaning in obtaining a better understanding of the characteristics of heat and water fluxes at different vegetation growth stages and underlying surface types, which can provide constructive suggestions for water management in mountainous areas.

A canopy conductance model with temporal physiological and environmental factors

Canopy conductance, one of the key variables in simulating evapotranspiration, is strongly influenced by the physiological status of a plant and environmental factors, including photosynthetically active radiation, vapor pressure deficit, air temperature, soil moisture and so on. However, the restrictive functions used to represent these factors rarely consider the dynamics of physiological and environmental factors. This study proposed an improved canopy conductance model by regarding radiation and vapor pressure deficit as the two main influencing factors, quantifying the temporal variation in stomatal responses to radiation that notably adjust stomatal behavior, parameterizing maximum stomatal conductance with plant type-specific functions and proposing a new restrictive function for the VPD. The improved canopy conductance model was incorporated in a surface conductance model for estimating surface conductance and evapotranspiration at 8 flux stations at the Heihe River Basin and the Haihe River Basin. The estimated results were the most accurate when comparing to two other models. Furthermore, the model performance was acceptable when most of the parameters were assumed to be constant across the sites except the reference canopy conductance G_{c, ref} and the soil evaporation parameter α_s, which suggests that the improved canopy conductance model could be used as a parsimony model for improving canopy conductance predictions and water use efficiency over typical climate zones and underlying surface types in North of China.

Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture

Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments.

Improving Meteorological Input for Surface Energy Balance System Utilizing Mesoscale Weather Research and Forecasting Model for Estimating Daily Actual Evapotranspiration

Using Surface Energy Balance System (SEBS) to estimate actual evapotranspiration (ET) on a regional scale generally uses gridded meteorological data by interpolating data from meteorological stations with mathematical interpolation. The heterogeneity of underlying surfaces cannot be effectively considered when interpolating meteorological station measurements to gridded data only by mathematical interpolation. This study aims to highlight the improvement of modeled meteorological data from the Weather Research and Forecasting (WRF) mesoscale numerical model which fully considers the heterogeneity of underlying surfaces over the data from mathematical interpolation method when providing accurate meteorological input for SEBS model. Meteorological data at 1 km resolution in the Hotan Oasis were simulated and then were put into SEBS model to estimate the daily actual ET. The accuracy of WRF simulation was evaluated through comparison with data collected at the meteorological station. Results found that the WRF-simulated wind speed, air temperature, relative humidity and surface pressure correlate well with the meteorological stations measurements (R2 are 0.628, 0.8242, 0.8089 and 0.8915, respectively). Comparison between ET calculated using the meteorological data simulated from the WRF (ETa-WRF) and meteorological data interpolated from measurements at met stations (ETa-STA) showed that ETa-WRF could better reflect the ET difference between different land cover, and capture the vegetation growing trend, especially in areas with sparse vegetation, where ETa-STA intends to overestimate. In addition, ETa-WRF has less noise in barren areas compared to ETa-STA. Our findings suggest that WRF can provide more reliable meteorological input for SEBS model than mathematical interpolation method.