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Early warning systems and emergency management for disasters, environmental pollution, and illegal development require timely and accurate Earth surface anomaly detection (ESAD). Remote sensing, which uses satellites to observe the Earth’s surface, is an emerging approach to address this need. However, current remote sensing methods for ESAD are limited by their focus on specific anomalies, reliance on high-level satellite data, and the demand for significant computational and storage resources. In this article, we present a novel framework for general and on-orbit ESAD, which combines large vision models and lightweight priors. Our method characterizes images with a large vision model that is highly generalizable, reducing the dependency on high-level data, and compressing the prior base with sampling techniques, facilitating transmission and on-orbit storage. For on-orbit detection, we use a dictionary look-up style method for efficient anomaly prediction, enabling detection on satellites with limited computation resources. We evaluate our framework on typical scenarios and compare it with popular change detection (CD)-based and anomaly detection (AD)-based methods. Our results show that our framework achieves good performance while reducing the prior size by at least 95.56 times. Moreover, our framework can handle unpaired data, providing a chance to detect anomalies in the absence of near-term and paired images. Our framework has the potential to support the development and applications of general, on-orbit ESAD. The code and dataset are available at the following site: https://github.com/YummyWaffle/ESAD.

Land surface temperature (LST) is an important parameter in many research fields. As a temperature characteristic, thermal radiation has an obvious directionality. In urban areas, the directional anisotropy (DA) in surface temperatures can reach 10 K during airborne measurements, limiting the applicability of urban surface temperature products. Notably, the intricate 3-D structures and heterogeneous temperature distributions in urban environments significantly influence this anisotropy. In recent decades, numerous models have been developed for urban thermal anisotropy (UTA) analysis; however, most of these models predominantly focus on horizontal surfaces, with little consideration of mountainous architectures. To balance the calculation efficiency and model complexity, this study introduces an analytical thermal anisotropy model applicable to urban areas. This model considers the effects of 3-D structures and slopes and is labeled as the AU3SM. The multiangle data used for the analysis are recorded by an unmanned aerial vehicle (UAV) with a multicircle observation scheme in Chongqing, China. Comparisons of the AU3SM simulated data with these measurement data yield root mean square error (RMSE) and coefficient of determination ( $R^{2}$ ) values of 0.57 K and 0.89, respectively. Furthermore, similar comparisons with the discrete anisotropic radiative transfer (DART) model yield $R^{2}$ and RMSE values of 0.98 and 0.12 K, respectively. Simulations reveal that slope influences the UTA, and the hotspot lies on the opposite side of the solar direction; ignoring slope values of 5°, 10°, 15°, and 25° results in UTA maximum biases of approximately 1.2, 3.5, 7, and 7.5 K, respectively, under certain conditions. These findings demonstrate that the proposed model can accomplish rapid UTA assessments in mountainous areas.

Land surface temperature (LST) is an essential parameter for geoscience studies, and the 1-km polar-orbiting satellite LST products are widely used due to their global coverage on a daily basis. Aimed to jointly utilize all 1-km LST products to thoroughly quantify the LST temporal tendency within one day, comprehensive evaluation of existing 1-km LST products over the same sites is meaningful. In this study, taking sparsely vegetated sites as an example, ten 1-km polar-orbiting LST products were evaluated against the in situ measurements of ten USCRN sites with high spatial representativeness (from December 2019 to November 2020 covering four seasons). The evaluated LST products included three Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (i.e., MOD11, MYD11, and MYD21), two Sea and LST Radiometer (SLSTR) LST products (i.e., Sentinel-3A and Sentinel-3B), one LST product from Advanced Very High Resolution Radiometer (AVHRR) onboard Metop-B, three Visible Infrared Imaging Radiometer Suite (VIIRS) LST products (i.e., VNP21, S-NPP EDR, and NOAA-20 EDR), and one LST product from Visible and Infra-Red Radiometer (VIRR) onboard FY-3B. The results indicate that: 1) the RMSE is much higher in the daytime (2.5–4.0 K) than in the nighttime (1.7–2.5 K); 2) the daytime MBE is negative (from −0.3 to −2.8 K) and this underestimate trend is significantly slighter for nighttime MBE (from −1.7 to 0.2 K); and 3) the daytime RMSE varies from 2.7 to 4.6 K in summer, 2.7–4.5 K in spring, 2.5–3.7 K in winter, and 2.0–3.4 K in autumn. Furthermore, we found that the severe daytime RMSE is related to the thermal anisotropy amplitude, which is around 4 K in summer, 2 K in spring and autumn, and 1 K in winter. Correcting the thermal anisotropy of daytime LST is an essential step that needs to be taken seriously before conducting high-quality joint applications, such as diurnal temperature cycle (DTC) modeling.