Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

Biostimulants in dissolved organic matters recovered from anaerobic digestion sludge with alkali-hydrothermal treatment: Nontarget identification by ultrahigh-resolution mass spectrometry

Recovering high-value biomaterials from anaerobic digestion sludge (ADS) has attracted considerable attention. However, the molecular features and biological effects of abundant dissolved organic matters (DOMs) in ADS are still unclear, which limits the efficient recycling and application of these bioproducts. This study investigated the molecular composition and transformation of DOMs recovered from ADS through a mild-temperature alkali-hydrothermal treatment (AHT) with ultrahigh-resolution mass spectrometry and energy spectroscopy, and the fertilizing effects of DOMs were evaluated by rice hydroponics. The results indicated that AHT processes significantly promoted the solubilization and release of DOMs from ADS, where most of DOMs molecules remained unchanged and mainly consisted of N-containing compounds with 1–3 N atoms, featuring aromatic or N-heterocyclic rings. Furthermore, AHT processes at pH of 9–10 induced the hydrolysis of partial protein-like substances in DOMs, which was accompanied by formation of heterocyclic-N compounds. Under AHT at pH of 11–12, protein-like and heterocyclic-N substances were increasingly decomposed into amino-N compounds containing 1 or 5 N atoms, while numerous oxygenated aromatic substances with phytotoxicity were degraded and removed from DOMs. Rice hydroponic test verified that ADS-derived DOMs recovered by AHT process at pH of 12 exhibited the highest bioactivity for rice growth, which was attributed to the abundance of amino compounds and humic substances. This study proposed a novel process for the recovery of high-quality liquid organic fertilizer from ADS through AHT process, which can further enrich the technical options available for the safe utilization of sludge resources