The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of Earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image-space and ground-space. The automatically generated DEM initially represents the digital surface model (DSM) which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km². DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, specially in areas like Dehradun which is having highly undulating terrain with a large number of natural drainages. The difference image of reference i.e. edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e. DSM has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at 10m posting interval (referred to as Carto10 DEM), results in a mean difference of 8.74 m.

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and sentinel-1 data, and sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

Several studies have demonstrated the usefulness of SAR satellite images for monitoring surface parameters, in particular regular estimates of surface soil moisture (SSM) at regional (i.e., swath of several km²) or plot (i.e., area of interest of several hectares) spatial scales. These scales are rarely suitable for precision farming which requires mapping SSM at intra-plot spatial scale. In this context, the objective of this study is to analyze the capabilities of multi-temporal TerraSAR-X images to estimate the fine-scale SSM variability over bare agricultural plots (at a spatial scale ranging from 80 to 2800 m²). Time series of X-band satellite images were collected over a study site located in southwestern France, together with intra-field measurements of key soil descriptors (i.e., SSM, surface roughness, soil texture). The large dataset allows independent training and validating steps of a statistical algorithm (random forest), SSM being estimated using images acquired at low at high incidence angles. The level of performances obtained at the plot spatial scale, with R² ranging from 0.64 to 0.67 (depending on the considered incidence angle) and a RMSE close to 5.0 m^-3.m^-3, are exceeded by those obtained at a finer scale (700 m², corresponding to buffers with a 15 m radius). At this intra-plot spatial scale, the estimates based on the low incidence angles images are associated to a R² of 0.69 and a RMSE of 4.89 m^-3.m^-3, results slightly lower than performance obtained using high incidence angles images, R² of 0.72 and a RMSE of 4.55 m^-3.m^-3. Such magnitude of performance slightly increases over larger intra-plot spatial scales, the values of R² being superior to 0.75 with RMSE lower than 4.20 m^-3.m^-3 over areas of 2800 m² (corresponding to buffers with a 30 m radius).

In order to prevent serious damages from a possible earthquake and to determine the possible losses, in settlements under earthquake risk, it is very important to extract building inventory information for further determination of the performance of existing buildings. As conventional methods, such as field investigations, can be time-consuming and costly on an urban scale, approaches that are able to speed up these processes and reduce the costs are required. Determining at least some of the data required to determine the seismic performance of an existing building using alternative methods instead of conventional methods will provide a significant advantage. The study aims to investigate the potential of PlanetScope satellite imagery for extracting building inventory information. Thus, the main objective of the study are; to extract building using deep learning methods, to determine the height and the construction period of the buildings, and to extract building area. For this purpose, two 3-m PlanetScope satellite images were used over the study area located in Eskisehir, Turkey. Over forty buildings were located in the study area. The results showed that with PlanetScope Imagery detached buildings can be detected with high accuracy using deep learning methods, their height and area can be calculated, and the construction period can be determined. For future studies, the obtained information are planned to further be processed in a Geographical Information System (GIS) for building inventory and to be used for seismic vulnerability assessment studies of existing buildings.

Rapid economic development and population growth lead to fast urban expansion in both urban and rural landscapes. Accurate and updated mapping of urban expansions is curtail in urban and territorial planning for sustainable and strategic urban development. Using Earth Observation (EO) technologies, classification of urban areas in a rural landscape is more challenging than big cities. In this regard, in this paper, we aim at assessing the integration of Sentinel-1 and Sentinel-2 satellite data for classifying small urban areas in rural landscape in Google Earth Engine (GEE). Images of close dates from Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated to develop a machine learning classification through a Support Vector Classification (SVM) classifier. We have also added vegetation indices to the investigated dataset. As a study area, two rural areas in the Republic of North Macedonia has been selected. The results showed that the integration of Sentinel-1 and Sentinel-2 performed better than Sentinel-2 alone, with accuracy higher than 90%. For future studies, we recommend testing the dataset to different study areas and adding different EO data for obtaining even higher accuracy.