Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its sig-nificance, research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this pa-per, we present three novel elements to advance in-vehicle gaze research. Firstly, we introduce IVGaze, a pioneering dataset capturing in-vehicle gaze, collected from 125 sub-jects and covering a large range of gaze and head poses within vehicles. In this dataset, we propose a new vision-based solution for in-vehicle gaze collection, introducing a refined gaze target calibration method to tackle annotation challenges. Second, our research focuses on in-vehicle gaze estimation leveraging the IvGaze. In-vehicle face images often suffer from low resolution, prompting our in-troduction of a gaze pyramid transformer that leverages transformer-based multilevel features integration. Expanding upon this, we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transfor-mation, we rotate virtual cameras to normalize images, uti-lizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR shows state-of-the-art performance on the IVGaze dataset. Thirdly, we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images, we achieve superior performance compared to relying solely on visual features, sub-stantiating the advantage of gaze estimation. The project is available at https://yihua.zone/work/ivgaze.

Soil salt affects microwave radiometer observations in a similar way to soil moisture (SM). Without consideration of soil salt, the quality of radiometer-based SM products might be impaired. This study explores the magnitude, frequency dependence, seasonal patterns, and influence factors of soil salinity effects based on triple collocation (TC) analyses using X-, C-, and L-band radiometer-based SM products over global cropland. Multiple products corroborate that soil salinity dynamics contributes an uncertainty of 0.005–0.01 cm$^{3}~\cdot $cm$^{-3}$to the total SM retrieval error budget, proportional to soil salt concentration, higher at L-band and lower at X-/C-band. Soil salinity effects are stronger in spring and summer when rainfall drives significant soil salinity dynamics. SM retrieval uncertainty increases monotonously with the duration time of nonprecipitating weathers when salt is uplifted onto topsoil and sensed by microwave radiometers. Despite of overall weaker saline effects, X-/C-band products seem to respond earlier than the L-band SM products as soils become salinized, probably due to complicated soil-salt-water dielectric properties and shallower microwave penetration depths. The neglect of loss factor in soil moisture active passive (SMAP) SM retrieval algorithm might also enhance soil salinity effects. These uncertainties are essentially time-varying wet biases, and dynamic soil salinity maps are needed for bias-correcting current L-band and future P-band SM products.