Accurate estimation of forest aboveground biomass (AGB) is vital for informing ecosystem and carbon management. The Global Ecosystem Dynamics Investigation (GEDI) instrument—a new-generation spaceborne lidar system from NASA—provides the first global coverage of high-resolution 3D altimetry data aimed specifically for mapping Earth’s forests, but its performance is yet to be tested for large parts of the world. Here, our goal is to evaluate the accuracies of GEDI in measuring terrain, forest vertical structures, and AGB in reference to independent airborne lidar data over temperate and tropical forests in North America. We compared GEDI-derived elevations and canopy heights (e.g., relative height percentiles such as RH50 and RH100) with those from the Shuttle Radar Topography Mission (SRTM) or from two airborne lidar systems: the Laser Vegetation Imaging Sensor (LVIS) and Goddard’s Lidar, Hyperspectral and Thermal system (G-LiHT). We also estimated GEDI’s geolocation errors by matching GEDI waveforms and G-LiHT pseudo-waveforms. We assessed the predictive power of GEDI metrics in estimating AGB using Random Forests regression. Results showed that GEDI-derived ground elevations correlated strongly those from LVIS, G-LiHT, and LVIS (R² > 0.91), but with nonnegligible RMSEs of 5.7 m (G-LiHT), 3.1 m (LVIS), and 10.9 m (SRTM). GEDI canopy heights had poorer correlation with LVIS (e.g., R² = 0.44 for RH100) than with G-LiHT (e.g., R² = 0.60 for RH100). The estimated horizontal geolocation errors of GEDI footprints averaged 6.5 meters, comparable to the nominal accuracy of 9 m. Correction for the locational errors improved the correlation of GEDI vs G-LiHT canopy heights significantly, on average by 53% (e.g., R² from 0.57 to 0.82 for RH50). GEDI canopy metrics were useful for predicting AGB (R² = 0.82 and RMSE = 19.1 Mg/Ha), with the maximum canopy height RH100 being the most useful predictor. Our results highlight the importance of accommodating or correcting for GEDI geolocation errors for estimating forest characteristics and provide empirical evidence on the utility of GEDI for monitoring global biomass dynamics from space.

Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such as aboveground biomass (AGB)—a potential theoretically recognized but rarely explored. In this paper, we examined the effectiveness of the reconstructed MODIS typical-angle reflectances—reflectances observed from the hotspot, darkspot, and nadir directions—for estimating forest AGB from both theoretical and practical perspectives. To gain theoretical insights, we first tested the sensitivities of typical-angle reflectances to forest AGB through simulations using the 4-scale bidirectional reflectance distribution function (BRDF) model. We then built statistical models to fit the relationship between MODIS multi-angle observations and field-measured deciduous-broadleaf/mixed-temperate forest AGB at five sites in the eastern USA, assisted by a semivariogram analysis to determine the effect of pixel heterogeneity on the MODIS–AGB relationship. We also determined the effects of terrain and season on the predictive relationships. Our results indicated that multi-angle reflectances with fewer visible shadows yielded better AGB estimates (hotspot: R² = 0.63, RMSE = 54.28 Mg/ha; nadir: R² = 0.55, RMSE = 59.95 Mg/ha; darkspot: R² = 0.46, RMSE = 65.66 Mg/ha) after filtering out the effects of complex terrain and pixel heterogeneity; the MODIS typical-angle reflectances in the NIR band were the most sensitive to forest AGB. We also found strong sensitivities of estimated accuracies to MODIS image acquisition dates or season. Overall, our results suggest that the current practice of leveraging only single-angle MODIS data can be a suboptimal strategy for AGB estimation. We advocate the use of MODIS multi-angle reflectances for optical remote sensing of forest AGB or potentially other ecological applications requiring forest structure information.

The thermal environment problem is one of the main focuses of current urban environment research. At present, there are various methods used in urban space thermal environment (USTE) research. As a simulation method to quantify the USTE, the urban weather generator (UWG) has undergone great development and achieved many progressive results. It is necessary to establish and review its current research status by synthesizing UWG multi-scale applications. This review adopts a literature review approach, leveraging the Web of Science Core Collection to obtain previous relevant publications from 2010 to 2025 using “urban weather generator” and “thermal environment” as keywords. The literature is categorized by research themes, including model development, parameter optimization, and application cases. Through innovative analyses of spatio-temporal-scale classification, parameter optimization, the integration of anthropogenic heat emissions, and the multi-domain simulation potential of the UWG, this review synthesizes the application outcomes of the UWG model in multi-scale research, addressing gaps in current urban climate studies. The paper aims to elaborate and analyze the model’s current research status considering the following six aspects. First, the basic parameters in UWG simulation are introduced, including the data and parameter determination settings used in such simulations. Secondly, we introduce the simulation model and its basic principles, the simulation process, and the main steps of this process. Third, we classify and define UWG simulations of spatial thermal environments at different time scales and spatial scales. Fourth, regarding how to improve the accuracy of the UWG model, the deterministic parameters and uncertainty parameters settings are analyzed, respectively. Then, the impacts of anthropogenic heat during the simulation process are also discussed. Fifth, the applications of the UWG model in some major fields and its possible future development directions are addressed. Finally, the existing problems are summarized, the future development trends are prospected, and research on possible expected mitigation measures for the USTE is described.

Multi-angular remote sensing observation contains crucial information on forest structure parameters. Here, our goal is to examine the ability of multi-angular indices, which are constructed by the typical-angular reflectances in red and NIR bands from MODIS observations, for the retrieval of forest biomass based on the field-measured above-ground biomass (AGB) data. Specifically, we employed the updated version of the MCD43A1 BRDF parameter product as an input for BRDF models to reconstruct the MODIS typical-angular reflectances. Furthermore, we evaluated the effects of different configurations of BRDF models and solar zenith angles (SZA) on forest AGB estimation using our developed multi-angular indices. The semivariogram analysis strategy combined with Landsat ground-surface reflectance data was employed to determine the MODIS pixel heterogeneity; the survey data from field sites of homogeneous pixels was used in our analysis and validation. The results show that our developed multi-angular indices based on a hot-revised BRDF model, under a SZA of 45°, when combined with forest cover information, can account for up to 72% of the variation forest AGB, with an RMSE = 45 Mg/ha. We also found that different kernels for the BRDF models influenced the weight parameters of the biomass inversion equation but did not significantly affect the estimated AGB. In conclusion, our method can enable the better usage of MODIS multi-angular observations for forest AGB estimation.

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly varied terrain. This study produced the actual albedo databases by using albedo retrieval look-up tables. And then we investigated the spatiotemporal variations in land surface albedo and its corresponding radiative effects in the Yellow River Basin from 2000 to 2022 using MODIS-derived reflectance data. We employed time-series, trend, and anomaly detection analyses alongside surface downward shortwave radiation measurements to quantify the radiative forcing induced by land-cover changes. Our key findings reveal that (i) the basin’s average surface albedo was 0.171, with observed values ranging from 0.058 to 0.289; the highest variability was noted in the Loess Plateau during winter—primarily due to snowfall and low temperatures; (ii) a notable declining trend in the annual average albedo was observed in conjunction with rising temperatures, with annual values fluctuating between 0.165 and 0.184 and monthly averages spanning 0.1595 to 0.1853; (iii) land-cover transitions exerted distinct radiative forcing effects: conversions from grassland, shrubland, and wetland to water bodies produced forcings of 2.657, 2.280, and 2.007 W/m², respectively, while shifts between barren land and cropland generated forcings of 4.315 and 2.696 W/m². In contrast, transitions from cropland to shrubland and from grassland to shrubland resulted in minimal forcing, and changes from impervious surfaces and forested areas to other cover types yielded negative forcing, thereby exerting a net cooling effect. These findings not only deepen our understanding of the interplay between land-cover transitions and radiative forcing within the Yellow River Basin but also offer robust scientific support for regional climate adaptation, ecological planning, and sustainable land use management.

Research on grassland monitoring based on temperature/emissivity separation based on hyperspectral thermal infrared (HTIR) remote sensing is rare. Based on the longwave TIR instrument (Hyper-CAM), this study designed two experiments to collect HTIR datasets, separate the temperature and emissivity of different vegetation of grassland, and analyze the relationship between the emissivity of vegetation and soil moisture content. First, we collected the HTIR remotely sensed dataset of different kinds of vegetation and used the temperature/emissivity separation algorithm to separate the temperature and emissivity of seven types of vegetation. The temperature and emissivity of these types of vegetation were separated. Then, the absorption characteristics of the emissivity spectral curves of each type of grass were analyzed. The distribution and differences of the temperature and specific emissivity in different parts of these seven grassland vegetation types were quantitatively analyzed, and the relationship between their changes and vegetation leaf moisture and vegetation health status was also analyzed. Second, to monitor the drought of grassland vegetation, a second experiment was designed to measure the changes in the emissivity under different soil water contents. This observation experiment took Artemisia frigida as the research object. From the results of the separation of the temperature and emissivity, we found that the emissivity of Artemisia frigida has significantly changed with the increase in the water content, and the emissivity showed an overall increasing trend. We also quantitatively analyzed the differences in the temperature and specific emissivity between Artemisia frigida and Artemisia subulata Nakai, both belonging to the genus Artemisia, under different water content conditions. The overall waveform characteristics and their similarities and differences at 850–1280 cm⁻¹ were compared and analyzed. The experimental results shows that Hyper-CAM can effectively obtain the emissivity of various types of grassland vegetation as the absorption characteristics of grassland vegetation in the thermal infrared spectral region were quite notable, which shows the significant potential ability of identification and discrimination of different types of grassland vegetation.