*1.1. Overview of the Presented Papers*

The 18 papers published in the current Special Issue belong to the section "Environmental Remote Sensing" and cover a wide range of applications in terms of the RS data exploited, processing techniques used, and NH addressed.

Chen et al. [1] applied multi-source remote sensing (InSAR from ALOS PALSAR-1 and -2) and field investigation to study the activity and kinematics of two adjacent landslides along the Datong River in the Qilian Mountains of the Qinghai-Tibet Plateau (China).

Wang et al. [2] proposed a data partition strategy to solve typical limitations due to traditional multi-temporal interferometric synthetic aperture radar (MT-InSAR) methods which require a large computer memory and time when processing full-resolution data. They validated such a strategy in Changzhou City and in Chongqing City (China).

Ma et al. [3] adopted a new open-source tool named MAT.TRIGRS(V1.0) to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distributions in Longchuan County (China).

Wang et al. [4] proposed a wide-area InSAR variable-scale deformation detection strategy that combined stacking technology for fast ground-deformation rate calculations and advanced TS–InSAR technology to obtain a fine deformation time series. This new strategy was tested in the Turpan–Hami basin (China).

Xiong et al. [5] presented a new strategy based on the Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) method to overcome limitations due to an inaccurate settlement prediction using traditional methods. The Xiamen Xiang'an International Airport (China) was chosen as the test site.

Wangcai et al. [6] assessed landslide susceptibility, hazard, and risk in Yan'an City (China) using a random forest machine learning classifier and eight environmental factors influencing landslides. Additionally, Differential Synthetic Aperture Radar Interferometry (DInSAR) was used for a hazard assessment.

Hermle et al. [7], with the aim of reducing noise from decorrelation in ground motion detection by imaging, applied, for the first time, the optical flow-time series for fast landslides. The debris flows from the Sattelkar area (Austria) was selected as a benchmark site.

Li et al. [8], in order to obtain a precise casualty prediction method that could be applied globally, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) were proposed in their paper. A selection of 30 historical earthquakes that occurred in China's mainland was chosen.

Seydi et al. [9] presented a novel framework for burned area mapping based on the deep Siamese morphological neural network (DSMNN-Net) and heterogeneous datasets. Two case study areas in Australian forests were selected.

Nolde et al. [10] exploited the possibilities of a recent EO dataset published by the German Aerospace Center (DLR) by exemplarily analyzing fire severity trends on the Australian East coast for the past 20 years.

Kos et al. [11] used SAR offset tracking to reconstruct a unique record of ice surface velocities for a 3.2-year period for the Palcaraju glacier located above Laguna Palcacocha, Cordillera Blanca (Peru).

Ding et al. [12] carried out a review of the literature related to the application of RS and GIS in the study of flash floods. They analyzed more than 200 articles published in the last 20 years, performing keyword co-occurrence, time zone chart, keyword burst, and the literature co-citation analysis.

Cheng et al. [13] presented a detailed analysis to investigate the disaster conditions of the Brumadinho dam failure (Brasil) using satellite images. Their in-depth analysis revealed a hazard chain containing three stages, namely dam failure, mud-, and hyperconcentrated flow.

Pacheco et al. [14] used RS to detect, map, and monitor areas that were affected by forest fires in central Portugal. For this purpose, the study analyzed the performance of the k-nearest neighbor (kNN) and random forest (RF) classifiers.

Ranjgar et al. [15] assessed land subsidence susceptibility for Shahryar County (Iran) using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Additionally, they assessed if ensembles of ANFIS with two meta-heuristic algorithms could yield a better prediction performance.

Wang et al. [16] proposed a new approach using the relief–slope angle relationship to identify rockfall source areas controlled by rock mass strength. By using data from helicopter-based RS imagery, a 10m-DEM, and fieldwork, historical rockfalls in the Wolong study area of Tibet (China) were identified.

Yang et al. [17] developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, RS, Google Earth Engine (GEE), GIS, and the revised universal soil loss equation (RUSLE). They assessed the Sydney drinking water catchment area and the Warragamba Dam (Australia).

Lastly, Piersanti et al. [18] presented the first evidence, via observation and modeling, of changes in magnetospheric field line resonance (FLR) eigenfrequency, which was associated with the earthquake occurrence, and demonstrated a causal connection between seismic phenomena and space-based observables.

The Editors expect that these studies will lead to fruitful discussions and scientific progress, which should ultimately help to improve the overall quality and reliability of remote sensing as a now indispensable tool for approaching natural hazards.

#### *1.2. Statistics*

The total number of researchers and technologists who contributed to the papers was 104, with an average of 5.8 contributors per article. As shown in Figure 2, most of them worked in China, at least in terms of affiliation, followed by Germany, Italy, Australia, and Iran. Overall, Universities and Institutions from 16 different countries were involved in the present Special Issue. Most of the papers described work with practical applications tested around the world.

**Figure 2.** Overview of the authors' affiliation by country together with the location of case studies discussed in the present Special Issue.

The most recurring words among the keywords chosen by the authors are shown in the word cloud in Figure 3. Among them, "InSAR" was selected six times, followed by "landslide" (4 times), "burned area", "sentinel", and "wildfires" with three occurrences.

**Figure 3.** Word clouds (also known as text clouds or tag clouds) generated from the keywords of all contributions to the present Special Issue. The more a word appears as a keyword, the bigger and bolder it appears in the word cloud.
