Search

Evapotranspiration (ET) is a key process in the eco-hydrological cycle of a basin and a reliable indicator of climate change. However, the spatiotemporal alterations of ET in the contiguous United States (CONUS) over the recent two decades remain largely uncertain. In this study, we used the recently proposed Priestley–Taylor (PT)-SinRH model to estimate the ET of CONUS during 2001–2022 based on satellite and reanalysis data. The results showed that the PT-SinRH model yielded superior performance at eddy covariance (EC) sites, and the root-mean-square error (RMSE) ranged from 6.0 to 33.5 W/m2, the Kling–Gupta efficiency (KGE) ranged from 0.22 to 0.66. The annual mean value of ET in CONUS from 2001 to 2022, estimated by the PT-SinRH model, was 42.54 W/m2, and the spatial pattern of seasonal and annual ET variations increased from west to east. From 2001 to 2022, seasonal and annual ET of CONUS showed linear trends, with an average increase of 0.76 W/m2/da (p < 0.05). The ET in the east of CONUS exhibited a rate of increase at 1.45 W/m2/da, and the ET in the west of CONUS exhibited a rate of increase at 0.42 W/m2/da (p < 0.05). Importantly, our analysis of ET trends highlights that the change of precipitation (P) and normalized difference vegetation index (NDVI) exerts a significant impact on the change of ET over CONUS.

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land.

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.