Jiahui Fan

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas across four continents. The experimental results demonstrated that the ResNet-IST outperformed the VASTI by approximately 3% in terms of anomaly detection accuracy and achieved a 5–15% improvement in the detection of the normalized burn ratio (NBR). Furthermore, the accuracy of the VASTI was significantly greater than that of NBR for burn detection, indicating that the merging of spectral and textural features provides complementary advantages, leading to stronger classification performance than the use of SFs alone. Our results suggest that deep learning outperforms traditional mathematical models in burned vegetation anomaly detection tasks. Nevertheless, the scope and applicability of this study are somewhat limited, which also provides directions for future research.

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions.

To preserve ecological integrity and promote sustainable progress in the Three-River Headwaters Region (TRHR), it is vital to understand the vegetation alteration patterns and the sensitivity of these patterns to climatic and anthropogenic influences. In this study, we retrieved the fractional vegetation cover (FVC) through the dimidiate pixel model, driven by MODIS reflectance data from 2001 to 2022, and analyzed its spatiotemporal variations and responses to climate variation and human activities via partial correlation and residual analyses. The results indicated that the FVC retrieval accuracy reached 84.2%. From 2001 to 2022, the growing season FVC displayed a fluctuating yet overall increasing trend, with an average growth rate of 0.23% per year (p < 0.01). The vegetation significantly improved in 50.72% of the TRHR, with the Yellow River source area exhibiting the most notable improvement. However, 67.42% of the TRHR experienced a transition from improvement to degradation in vegetation, indicating a pessimistic outlook for future changes. Partial correlation analysis revealed that temperature had a pronounced influence on the southwestern Yellow River Basin and the southern Yangtze River Basin, whereas precipitation had a substantial effect on the southwestern and northeastern sections of the Yellow River Basin. Additionally, residual analysis revealed that climate change served as the predominant factor behind the changes in the FVC, whereas the anthropogenic intervention contributed substantially to vegetation improvements in the northeastern and western portions of the Yellow River Basin. Our study provides scientific support for the construction of ecological security barriers and the harmonious development of humans and nature in the TRHR.

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m³/m³) and the highest coefficient of determination (R², above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices.

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 RH^VPD 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.

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R², 0.71), RMSE (21.9 W/m²) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.

The acquisition of a time-series of high spatial resolution terrestrial latent heat flux (LE) is crucial for agricultural water resource management. However, the currently and frequently used spatiotemporal data fusion model fails to capture LE spatial and temporal patterns during periods of vigorous vegetation growth due to the limited availability of cloud-free fine-resolution images. In this study, we proposed a Multiphase Self-Adaptive spatiotemporal data fusion model (MSA) to address this issue. Unlike popular spatiotemporal data fusion models that rely heavily on cloud-free images, MSA utilizes all available fine- and coarse-resolution images, including those with partial cloud contamination, as inputs to the model. The proposed MSA method was tested at six sites representing five major land cover types across China. The results demonstrate that the MSA model, utilizing all available Landsat images, was more accurate than that relies solely on cloud-free Landsat images [coefficient of determination (R ² ): 0.53 vs. 0.38; Root Mean Square Error (RMSE): 8.12 vs. 9.93 W/m ² ]. We also compared the proposed method with three widely used models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), and Fit-FC. The result show MSA performed better than other models at recognizing LE spatial details. Additionally, MSA produced a high spatial resolution daily LE that was the most similar to ground-observed LE [R ² =0.34 (p2 , bias=-2.75 W/m ² ]. The proposed strategy provides an alternative approach for monitoring the high spatial resolution dynamic flux of heat and water over various land cover types.