Showing papers in "Remote Sensing of Environment in 2013"

TL;DR: A review of the current status of selected remote sensing algorithms for estimating land surface temperature from thermal infrared (TIR) data is presented in this article, along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements.

TL;DR: In this article, an error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed, which can then be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model).

TL;DR: In this paper, the authors present a new comprehensive change detection method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies, which integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs.

TL;DR: In this article, the authors demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point.

TL;DR: In this paper, the authors conducted an analysis of reported biomass accuracy estimates from more than 70 refereed articles using different remote sensing platforms (airborne and spaceborne) and sensor types (optical, radar, and lidar), with a particular focus on lidar since those papers reported the lowest errors when used in a synergistic manner with other coincident multi-sensor measurements.

TL;DR: Improved global estimates of LAI, FAPAR and FCOVER variables are developed by capitalizing on the development and validation of already existing products by training neural networks to estimate these fused and scaled products from SPOT-VEGETATION top of canopy directionally normalized reflectance values.

TL;DR: Developing specific spectral disease indices for the detection of diseases in crops will improve disease detection, identification and monitoring in precision agriculture applications.

TL;DR: In this paper, the authors used interferometric synthetic aperture radar (InSAR) time-series analysis of ALOS L-band SAR data to resolve land subsidence in western Indonesia with high spatial and temporal resolution.

TL;DR: In this article, the authors used remotely sensed datasets and gridded population to estimate the magnitude of thermal differentials (urban heat islands and/or sinks), the timing of heat differential events, and the controlling variables.

TL;DR: In this paper, the authors evaluate the potential use of MODIS remote sensing data for monitoring the seasonal dynamics of different types of vegetation cover that are representative of the major terrestrial biomes, including temperate deciduous forests, evergreen forests, African savannah, and crops.

TL;DR: In this article, the authors explored the use of high-resolution thermal imagery, chlorophyll fluorescence, structural and physiological indices (xanthophyll, carotenoids and blue/green/red B/G/R indices) calculated from multispectral and hyperspectral imagery as early indicators of water stress caused by Verticillium wilt infection and severity.

TL;DR: An approach based on an Artificial Neural Network has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements of GMES Sentinel-1 characteristics.

TL;DR: In this article, the authors proposed a new multi-sensor microwave remote sensing drought index, the Microwave Integrated Drought Index (MIDI), for monitoring short-term drought, especially the meteorological drought over semi-arid regions.

TL;DR: The NLCD 2006 dataset as discussed by the authors provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data.

TL;DR: In this paper, two monthly MODIS 1-km ET products, MODIS global ET (MOD16) and Operational Simplified Surface Energy Balance (SSEBop) ET, are validated over the conterminous United States at both point and basin scales.

TL;DR: In this article, the authors used the ICESat/GLAS altimetry data to estimate the lake water level from the corresponding area data, thereby reconstructing time series of lake level data for each lake from the 1970s to 2011.

TL;DR: In this article, the authors developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimation, which is used to control crop model runs, assimilate remote sensing (RS) data and update model state variables.

TL;DR: The Vegetation Adjusted NTL Urban Index (VANUI) as mentioned in this paper is a spectral index that combines MODIS NDVI with NTL to achieve three key goals: first, the index reduces the effects of NTL saturation.

TL;DR: In this paper, a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forests using multi-decadal time series of Landsat TM/ETM+ images is presented.

TL;DR: A framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components is proposed and it is suggested that comparing Landsat and MODISatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending.

TL;DR: In this paper, the authors used the temperature-vegetation index (TVX) method to estimate the air temperature of the Xiangride River basin in the north Tibetan Plateau.

TL;DR: This critically investigate the interdisciplinary literature on DLST and identifies the terms used to denote DLST in different disciplines, and formulate definitions of TSP, TUM, and DLST, which examine how TSP and TUM are connected to related fields in remote sensing.

TL;DR: In this paper, a new method for estimating water volume changes in lakes and reservoirs from four different satellite altimetry databases in combination with satellite imagery data, without any in-situ measurements and bathymetry maps.

TL;DR: In this paper, the authors describe the scientific validation of the first version of global biophysical products (i.e., leaf area index, fraction of absorbed photosynthetically active radiation and fraction of vegetation cover), namely GEOV1, developed in the framework of the geoland-2/BioPar core mapping service at 1 km spatial resolution and 10-days temporal frequency.

TL;DR: The quantitative evaluations suggest that the proposed integrated method to recover the missing pixels can predict the missing values very accurately, and performs especially well in edges, and is able to keep the shape of ground features.

TL;DR: In this paper, a new formulation of the Photochemical Reflectance Index (PRI) was proposed, in which the standard PRI index was normalized by an index that is sensitive to canopy structure (Renormalized Difference Vegetation Index, RDVI) and by a red edge index that was sensitive to chlorophyll content (R 700 /R 670 ).

TL;DR: In this paper, the authors demonstrated how the combination of multi-sensor and multi-temporal SAR data improves the monitoring capacity of very slow landslides in the Spanish Pyrenees.

TL;DR: In this paper, a semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chilophyll a concentration, absorption by CDM and particulate backscattering.

TL;DR: In this paper, the authors used a new downscaling-calibration procedure of the freely accessible Version 7 of TRMM (Tropical Rainfall Measuring Mission) 3B43 product, in conjunction with limited rain gauge data sets, to generate improved monthly pixel-based precipitation data at higher spatial resolution (1.km).

TL;DR: In this article, the authors review the use of remotely sensed data for estimating fuel moisture content (FMC) with particular concern towards the operational use of LFMC products for fire risk assessment.