Multi-label classification in remote sensing is very significant which plays an important role in extracting valuable information from satellite imagery. Ignoring the distinct information provided by labels in each image or transforming images into content-aware category representations without considering inherent correlation of labels within the dataset can result in the establishment of improper relationships between images and labels, ultimately leading to a significant degradation in accuracy. To address this problem, this paper proposes a Label-Driven Graph Convolutional Network (LD-GCN) to excavate substantial information using the inherent correlation of labels from datasets and build a strong relationship between labels and images. The framework consists of two modules, i.e. label recognition GCN (LRGCN) and semantic enrichment module (SEM). The LRGCN module yields rich and valuable information from the inherent correlation of labels and builds a strong relationship between images and labels. The SEM further enriches the semantics obtained from LRGCN. Experiments conducted on UCM, AID, and DFC15 multi-label remote sensing datasets illustrate that LD-GCN outperforms the state-of-the-art methods on key evaluation metrics.

Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of Agriculture (USDA). The CDL has been widely used for training deep learning (DL) segmentation models. However, it contains various errors, such as salt-and-pepper noise, and must be refined before being used in DL training. In this study, we used two approaches to refine the CDL for DL segmentation of major crops from a time series of Sentinel-2 monthly composite images. Firstly, different confidence intervals of the confidence layer were used to refine the CDL. Secondly, several image filters were employed to improve data quality. The refined CDLs were then used as the ground-truth in DL segmentation training and evaluation. The results demonstrate that the CDL with +45% and +55% confidence intervals produced the best results, improving the accuracy of DL segmentation by approximately 1% compared to non-refined data. Additionally, filtering the CDL using the majority and expand–shrink filters yielded the best performance, enhancing the evaluation metrics by about 1.5%. The findings suggest that pre-filtering the CDL and selecting an effective confidence interval can significantly improve DL segmentation performance, contributing to more accurate and reliable agricultural monitoring.

This paper reports a test of gravitational redshift, which is a consequence of the Einstein equivalence principle, using the Rb clocks of GPS Block IIF satellites. The fractional deviation in the gravitational redshift was measured using 6,640 days of data from three Rb clocks onboard GPS Block IIF satellites. The systematic effects related to orbital uncertainty, temperature, and magnetic field were modeled conservatively. The fractional deviation in the gravitational redshift from the general relativity prediction was measured with ( 0.23 ± 1.34 ) × 10 - 3 at one sigma.

Measurements of climatic processes such as cold air drainage flows are problematic over mountainous areas. Observation of cold air drainage is not available in the existing observation network and it requires a special methodology. The main objective of this study was to characterize the cold air drainage over regions with a slope. A high resolution infrared camera, a meteorological station and Digital Elevation Model (DEM) were used. The specific objective was to derive nocturnal cold air drainage velocity over the slope. To address these objectives, a number of infrared measurement campaigns were conducted during calm and clear sky conditions over an agricultural zone (blackcurrant farm) in Canada. Using thermal infrared images, the nocturnal surface temperature gradient were computed in hourly basis. The largest gradient magnitudes were found between 17h -20h. The cooling rates at basin area were two times higher in comparison to the magnitudes observed within slope area. The image analysis illustrated this considerable temperature gradient of the basin may be partly due to transport of cold air drainage into the basin from the slope. The results show that thermal imagery can be used to characterize and understand the microclimate related to the occurrence of radiation frost in the agricultural field. This study provided the opportunity to track the cold air drainage flow and pooling of cold air in low lying areas. The infrared analysis demonstrated that nocturnal drainage flow displayed continuous variation in terms of space and time in response to microscale slope heterogeneities. In addition, the analysis highlighted the periodic aspect for cold air drainage flow.