Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Cloud plays a crucial role in surface downward longwave radiation (DLR). To determine optimal algorithms for cloud‐sky DLR calculation under various climates, three types of algorithms were assessed, (i) four empirical algorithms determining cloudy DLR by simple cloud correction using the cloud fraction, (ii) three parameterized algorithms determining the cloud contribution by cloud temperature, and (iii) a semiempirical algorithm, Zhou‐Cess, parameterized by the cloud water path. A sensitivity study was conducted using a Moderate Resolution Transmittance (MODTRAN) code to first determine the sensitive cloud parameters of DLR. Then, these algorithms were validated using synthetic data simulated by MODTRAN, ground‐observed data, and satellite‐observed data. When all the input parameters were accurate, the cloud‐correction algorithms showed poor performance. Cloud‐temperature‐based algorithms showed much better results but exhibited positive systematic biases. The Zhou‐Cess algorithm performed best but could not precisely describe the effect caused by cloud variations. When these algorithms were applied to ground‐ and satellite‐based data, the accuracies of DLR calculations were affected by the uncertainty in the atmospheric and cloud parameters. The simple empirical algorithms showed the poorest results. The cloud‐temperature‐based algorithms were greatly influenced by the uncertainty in cloud base temperature and cloud fraction and showed acceptable results when cloud fractions were accurate. The Zhou‐Cess algorithm revealed the best results at most sites and was less impacted by cloud parameter uncertainties; therefore, this algorithm is suggested for cloudy‐sky DLR calculation with poor‐quality cloud parameters.

Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W·m⁻²) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W·m⁻² and 47.38 W·m⁻², respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of Shannon’s diversity index (SHDI) and the surface type within the flux contribution source area.

Evapotranspiration (ET) plays an important role in surface–atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR) band. A temperature-sharpening and flux aggregation scheme (TSFA) was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling) and TRFA (temperature resampling and flux aggregation) methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research) were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R², MBE, and RMSE values of the sensible heat flux (H) were 0.61, 0.90, and 50.99 W m⁻², respectively, and those for the latent heat flux (LE) were 0.82, −20.54, and 71.24 W m⁻², respectively. IPUS yielded the largest errors in ET estimation. The RMSE of LE between the TSFA and IPUS methods was 51.30 W m⁻², and the RMSE of LE between the TSFA and TRFA methods was 16.48 W m⁻². Furthermore, additional analysis showed that the TSFA method can capture the subpixel variations of land surface temperature and the influences of various landscapes within mixed pixels.

Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m², −5.9 W/m², and 14.9 W/m² for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m² (16.8%), 127.6 W/m² (55.1%), and 99.5 W/m² (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m². The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.