Shengbiao Wu

The University of Hong Kong | HKU · Faculty of Architecture

Special Issue Information https://www.mdpi.com/journal/remotesensing/special_issues/Surface_Biophysical_Parameter_Retrieval Dear Colleagues, Surface biophysical parameters across leave (e.g., leaf chlorophyll nitrogen and water contents, leaf mass per area, and leaf inclination angle), canopy (e.g., canopy leaf area index, height and biomass, and tree crown area, and diameter at breast height), and landscape (e.g., surface albedo, temperature, and radiative budget) scales are crucial for modeling terrestrial processes, monitoring agricultural ecosystems, and quantifying many human–environment interactions. Remote sensing has become the mainstream technology for retrieving and mapping large-scale and long-term biophysical surface parameters. Their successful retrievals using remote sensing rely on accurate physical models, robust retrieval approaches, high-quality data observations, and reliable validation strategies. Therefore, advances are needed to better monitor the biophysical surface parameters, and to also improve our understanding and modeling of terrestrial ecosystems processes and human–nature relationships. This Special Issue aims to report on the state-of-the-art in the monitoring of biophysical surface parameters with remote sensing. Related articles are welcome, including, but not limited to, the following topics: Physical models (e.g., 1-D and 3-D) across multi-scales (e.g., leaf, canopy, individual tree-crown, ecosystem, landscape, and rugged terrain) for modeling remote sensing signals integrated with surface biophysical parameters. Experimental sources of inaccuracy when retrieving surface biophysical parameters from remote sensing observations: atmosphere conditions, geometric configuration of observation, directional and neighborhood effects, 3-D architecture of the studied landscape, etc. Retrieval approaches (e.g., empirically based, physically based, and machine learning and deep learning approaches) across multi-scale observation platforms (e.g., smartphone, wireless sensor network (WSN), tower-based cameras, unmanned aerial vehicle (UAV), airborne, and satellite) for retrieving surface biophysical parameters. New proposed or improved operational algorithms for recent satellite missions (e.g., Himawari-8, DSCOVR EPIC, Landsat-8, Sentinel-2, Worldview, PlanetScope, and PRISMA) to large-scale map surface biophysical parameters. Rapid estimation approaches using the cloud-computing platforms (e.g., Google Earth Engine (GEE), Amazon Web Services (AWS), and Microsoft Azure). Multi-source data fusion techniques (e.g., optical and LIDAR) for improving satellite data quality by minimizing artifact effects (e.g., clouds, topography, reflectance anisotropy, and satellite orbit drift effects), filling data gaps, and reconstructing time-series observations. Calibration and validation strategies for assessing the accuracy and uncertainty of remote sensing biophysical parameter products. Long-time spatial and temporal analysis of surface biophysical products, with the underlying drivers and implications for terrestrial ecosystems process and human–nature interactions. Original research and review articles are welcome. Review articles are suggested to cover one or more of the above topics. Deadline for manuscript submissions: 31 July 2022. Dr. Shengbiao Wu Dr. Baodong Xu Prof. Dr. Gaofei Yin Prof. Dr. Jean-Philippe Gastellu-Etchegorry