Showing papers in "Remote Sensing of Environment in 2022"

TL;DR: Wang et al. as mentioned in this paper used an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory, to estimate ground-level ozone from solar radiation intensity and surface temperature.

TL;DR: In this article , the authors present the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection.

TL;DR: In this article , a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions is proposed, and the results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.

TL;DR: The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs as mentioned in this paper .

TL;DR: Li et al. as mentioned in this paper performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe.

TL;DR: Li et al. as discussed by the authors performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe.

TL;DR: In this paper, a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types.

TL;DR: In this article , a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types.

TL;DR: The World Settlement Footprint 3D dataset as discussed by the authors was developed to map key characteristics of the world's building stock in a so far unprecedented level of spatial detail for every single settlement on our planet.

TL;DR: In this paper, a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) is proposed to regress canopy top height globally, and the model learns to extract robust features that generalize to unseen geographical regions and yields reliable estimates of predictive uncertainty.

TL;DR: In this paper , a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) was proposed to regress canopy top height globally, with an expected RMSE of 2.7 m with low bias.

TL;DR: In this article, the authors demonstrate that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is a robust proxy of far-red SIF across a wide range of spatial and temporal scales.

TL;DR: In this paper , a machine-learning model was developed for retrieval of water quality indicators from multi-source satellite imagery, including chlorophyll-a (Chl a ), Total Suspended Solids (TSS), and absorption by Colored Dissolved Organic Matter at 440 nm ( a cdom (440)), across a wide array of aquatic ecosystems.

TL;DR: In this article , the authors investigated the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations and found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRVP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales.

TL;DR: Wang et al. as discussed by the authors presented a deep learning model to generate a new version of the LAI product (V6) at 250m resolution from MODIS data from 2000 onward.

TL;DR: In this paper , the authors used a continental-scale soil laboratory spectral library (37,540 full-pedon 350-2500 nm reflectance spectra with surface organic carbon (SOC) concentration of 0-780 g·kg−1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), CNN, and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration.

TL;DR: In this paper , a deep learning-based approach using the landscape pattern from Sentinel-1 data was proposed to produce monthly maps of forest harvesting in two deforestation hotspots - California, USA and Rondônia, Brazil.

TL;DR: Wang et al. as mentioned in this paper developed a novel neural network guided interpolation (NNGI) method to map forest canopy height by fusing GEDI, ICESat-2 ATLAS, and Sentinel-2 images.

TL;DR: In this article , the authors demonstrate a novel deep learning and big data analytics approach to fuse freely available global radar and multi-spectral satellite data, acquired by the Sentinel-1 and Sentinel-2 satellites.

TL;DR: In this article, the authors demonstrate a novel deep learning and big data analytics approach to fuse freely available global radar and multi-spectral satellite data, acquired by the Sentinel-1 and Sentinel-2 satellites, and create the first-ever global and quality controlled urban local climate zones classification covering all cities across the globe with a population greater than 300,000.

TL;DR: In this article, a permafrost-tailored InSAR approach was developed by incorporating a MODIS-land-surface-temperature-integrated ground deformation model to reconstruct the seasonal and long-term deformation.

TL;DR: In this article , triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD).

TL;DR: In this article , a permafrost-tailored InSAR approach was developed by incorporating a MODIS-land-surface-temperature-integrated ground deformation model to reconstruct the seasonal and long-term deformation.

TL;DR: In this paper , the authors explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation for land cover mapping, and implemented a regionalized approach for optimizing training data selection and model-building.

TL;DR: In this article, the authors explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation for land cover mapping, and implemented a regionalized approach for optimizing training data selection and model-building.

TL;DR: In this article , the authors developed an approach to integrate satellite altimetry (ICESat, CryoSat-2, and ICESat-2) with DEM differencing and satellite gravity to resolve high spatiotemporally resolved glacier surface elevation change and mass balance across the SETP for the past two decades.

TL;DR: In this paper , a new mono-angle retrieval algorithm, SMAP-INRAE-BORDEAUX (SMAP-IB), was proposed to estimate global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD), which are essential to monitor the Earth water and carbon cycles.

TL;DR: In this paper , a cross-sensor Land-cOVEr framework, called LoveCS, is introduced to address the difficulties of the spatial resolution inconsistency and spectral differences, and a dense multi-scale decoder is proposed to effectively fuse the multiscale features from different sensors.

TL;DR: Wang et al. as discussed by the authors proposed a watershed-spectral-texture-controlled normalized cut (WST-Ncut) algorithm, and applied it to delineate individual trees in a subtropical broadleaf forest situated in Shenzhen City of southern China.

TL;DR: The first Cloud Masking Intercomparison Exercise (CMIX) as discussed by the authors was conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV).