In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and social policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal Quantile Regression (CQR) and related techniques, often yield overly conservative prediction intervals. In this work, we introduce a conformal inference approach to ITE using the conditional density of the outcome given the covariates. We leverage the reference distribution technique to efficiently estimate the conditional densities as the score functions under a two-stage conformal ITE framework. We show that our prediction intervals are not only marginally valid but are narrower than existing methods. Experimental results further validate the usefulness of our method.

In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance in the target domain, is more beneficial. There have been many transfer learning methods developed under various distributional assumptions. In this article, we study a particular type of classification problem, called conformal prediction, under a new distributional assumption for transfer learning. Classifiers under the conformal prediction framework predict a set of plausible labels instead of one single label for each data instance, affording a more cautious and safer decision. We consider a generalization of the covariate shift with posterior drift setting for transfer learning. Under this setting, we propose a weighted conformal classifier that leverages both the source and target samples, with a coverage guarantee in the target domain. Theoretical studies demonstrate favorable asymptotic properties. Numerical studies further illustrate the usefulness of the proposed method.

Nighttime light (NTL) remote sensing data has been widely used in various fields, such as human activity analysis, urbanization studies, and economic evaluation. However, Earth’s nighttime environment is very complex so that the NTL images are seriously affected by atmospheric effect and moonlight. This complexity primarily stems from the numerous atmospheric scattering and absorption, as well as the incoming moonlight, which can significantly distort and contaminate nighttime light observed by the satellite and consequently reduce the precision and stability of NTL data. In order to improve the quantitatively quality of the NTL data, this paper proposes an innovative atmospheric correction algorithm that leverages the nighttime radiative transfer model (nRTM) considering both atmospheric effect and moonlight effect to get ground radiance of artificial lights from satellite nighttime light images. This model takes into account the complex interactions between light and the atmosphere. By simulating these processes, the algorithm is able to separate the contributions of atmospheric scattering and absorption from the original NTL images. To demonstrate the effectiveness of the proposed algorithm, this paper takes the SDGSAT-1 NTL image of Beijing as a representative case study of atmospheric correction. By comparing the corrected and uncorrected images, it is evident that the atmospheric correction significantly improves the quality of the NTL data and the ground nighttime lighting information becomes clearer and more accurate, effectively removing noise interference and enhancing data reliability. Moreover, it also found that the high-pressure sodium (HPS) lamps and LED lamps in the NTL images presented different radiance values and spectral shapes that can be helpful for classifying different lamps.

The estimation of Sea Surface Temperature (SST) from ocean satellites with large observation angles must account for the angular effects on SST. This study developed an angle-dependent non-linear split-window algorithm (A-NLSW) to retrieve SST from Chinese ocean satellite HY-1D thermal infrared data. The algorithm coefficients were obtained based on the simulated dataset and grouped by initial SSTs, total atmospheric column water vapor content (TCWV), and satellite zenith angle (SZA). The A-NLSW algorithm is validated and re-calibrated using the bulk temperature collected by the iQuam in-situ dataset. After re-calibration, the accuracy of the SST was improved from 1.53 K to 0.87 K for SZA ranging from 0 to 70.5 ° . Nearly 60% of the validation points achieved an accuracy of 0.5 K and over 90% achieved an accuracy of 1.0 K. These findings highlight the robustness of the A-NLSW algorithm in reliably retrieving SST from HY-1D satellite images, even when observations are made at large SZA.

Night-time light remote sensing has been widely used in various fields, including human activity analysis, urbanization studies, and economic research. However, Earth’s nighttime environment is very complex so that nighttime light images are seriously affected by atmospheric elements and moonlight. To address this issue, this paper proposed an atmospheric correction algorithm on basis of a newly developed radiative transfer model (RTM) that aims to derive the ground radiance of artificial lights from satellite nighttime light images. The SDGSAT-1 night-time light image of Beijing is used as a case study to demonstrate the effectiveness of the proposed algorithm. Results show that the new algorithm can effectively removes the atmospheric effects and improves the data quality of nighttime light images.

Accurate estimation of the surface emission longwave radiation (SELR) has important scientific significance for understanding its spatiotemporal dynamics and surface thermal environment. High spatial resolution thermal infrared images provide better data support for studying SELR of complex surfaces such as urban surface. This paper focus on proposing a new urban-oriented hybrid method to estimate urban surface emission longwave radiation from top-of-atmosphere thermal radiance images, by taking the GF-5/VIMI thermal image as an example, and conduct the parameter sensitive analysis of the model as well as application over Beijing city. The experimental results of the simulation dataset showed that the developed method has relatively high precision, with SELR errors of less than 12.0 W/m2under low water vapor conditions and less than 17.0 W/m2under high water vapor conditions. The application of method in GF-5 image also demonstrated the rationality and effectiveness of the method.

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.