Showing papers in "Remote Sensing of Environment in 2017"

TL;DR: Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection.

TL;DR: The European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CCI) program.

TL;DR: A workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 OLI/TIRS data is created, finding that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using validation data.

TL;DR: The ICESat-2 mission is a follow-on to the ICES-1 mission with three pairs of beams, each pair separated by about 3 km cross-track with a pair spacing of 90 m as discussed by the authors.

TL;DR: The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance as mentioned in this paper.

TL;DR: In this paper, the authors analyzed the temporal trajectory of remote sensing data for a variety of winter and summer crops that are widely cultivated in the world (wheat, rapeseed, maize, soybean and sunflower).

TL;DR: A new version of the widely-used PROSPECT model is presented, hereafter namedPROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids, the two plant pigments in the current version, and outperforms all the previous versions.

TL;DR: In this paper, the authors present a review of the significant progress which has been made over the last decade in this field of research with a focus on L-band, and a discussion on possible applications to the SMOS and SMAP soil moisture retrieval approaches.

TL;DR: In this article, high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) were used for vegetation classification and structure measurements.

TL;DR: In this article, the authors evaluated remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS surface reflectance imagery to improve temporal sampling over that provided by Landsat alone.

TL;DR: In this paper, the authors reviewed progress in using multitemporal DMSP-OLS and VIIRS imagery to analyze urbanization, economic, and population dynamics across a range of geographic scales.

TL;DR: In this paper, the authors investigated the relationship between the spatial configuration of trees and land surface temperature (LST) using different statistical approaches, and conducted the analyses using spatial units of different sizes, based on trees mapped from 1-m high resolution imagery.

TL;DR: Wang et al. as mentioned in this paper used the Normalized Difference Vegetation Index (NDVI) trajectory to detect major land cover dynamics in Beijing and classified the land cover types in 2015 with the Google Earth Engine (GEE) cloud calculation.

TL;DR: In this article, a high resolution image taken from a UAV at very low altitude with application to high throughput phenotyping in field conditions was used to estimate wheat plant density at the emergence stage.

TL;DR: In this article, the authors re-examined the global vegetation activity by comparing the recent MODIS Collection 6 (C6) and Collection 5 (C5) VIs including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation index (EVI) from Terra (2001-2015) and Aqua Satellites (2003-2015).

TL;DR: In this article, a comparison of Sentinel-2A (S2) MSI and Landsat 8 (L8) OLI (Operational Land Imager) data in the retrieval of forest canopy cover, effective canopy cover (ECC), and leaf area index (LAI) is presented.

TL;DR: In this article, a two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM 2.5 concentrations in 2013 and 2014 at 1-km resolution with complete coverage in space and time.

TL;DR: In this article, the authors used the Composite2Change (C2C) algorithm that leverages the extensive Landsat archive to produce annual, gap-free, surface reflectance composites to date and label disturbance types and to characterize vegetation recovery over the > 650 million ha of Canada's forested ecosystems.

TL;DR: In this paper, the current distribution of glacial lakes across the entire Himalaya and monitor the spatially-explicit evolution of the lakes over five time periods from 1990 to 2015 using a total of 348 Landsat images at 30-m resolution.

TL;DR: In this paper, the authors proposed a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance (STR ) and is parameterized based on pixel distribution within the STR - VI space.

TL;DR: In this article, the authors review their experience of TLS sampling strategies from 27 campaigns conducted over the past 5 years, across tropical and temperate forest plots, where data was captured with a RIEGL VZ-400 laser scanner.

TL;DR: The code is developed under an open-source, Apache License, Version 2.0, and is enabling other organisations, including the Committee on Earth Observing Satellites (CEOS), to explore the use of similar data cubes in developing countries.

TL;DR: In this paper, the three cornered hat method is used to quantify uncertainties in total water storage (TWS) changes from GRACE observations, land surface models, and global hydrological models, indicating that the WaterGap Global Hydrological Model (WGHM)-based TWS changes show the lowest uncertainty over sixty basins covering a range of climate settings and levels of human activities globally.

TL;DR: In this article, the authors proposed a framework for the retrieval of all-weather land surface temperature (LST) at a high spatial resolution by combining the advantages of TIR and satellite passive microwave (PMW) measurements.

TL;DR: The European Space Agency's Sentinel-2A mission with the MultiSpectral Instrument (MSI) onboard was launched in 2015, initiating a new era in high-to-moderate-resolution (i.e., 10 to 60m) imaging of Earth's resources as discussed by the authors.

TL;DR: The Multi-scale Ultra-high resolution (MUR) sea surface temperature (SST) analysis presented daily SST estimates on a global 0.01° × 0.1° grid as discussed by the authors, where only the night-time (dusk to dawn) satellite SST retrievals are used to estimate the foundation SST.

TL;DR: An efficient and reliable method that uses multi-source geospatial big data, including nighttime light imagery and social media check-in maps, to locate the main center and subcenters of a polycentric city is presented.

TL;DR: In this paper, a new multitemporal data based classification approach was developed that incorporates knowledge about the phenological changes on crop lands and identifies phenological sequence patterns (PSP) of the crop types based on a dense stack of Sentinel-1 data.

TL;DR: A new post-classification method with iterative slow feature analysis (ISFA) and Bayesian soft fusion is proposed to obtain reliable and accurate change detection maps and achieve a clearly higher change detection accuracy than the current state-of-the-art methods.

TL;DR: In this article, LiDAR-derived crown structural information was combined with hyperspectral-derived spectral vegetation indices for tree species classification in the City of Surrey, British Columbia, Canada.