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Remote Sens., Volume 2, Issue 4 (April 2010), Pages 908-1196

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Research

Jump to: Review

Open AccessArticle Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment
Remote Sens. 2010, 2(4), 908-925; doi:10.3390/rs2040908
Received: 26 January 2010 / Revised: 10 March 2010 / Accepted: 22 March 2010 / Published: 26 March 2010
Cited by 8 | PDF Full-text (590 KB) | HTML Full-text | XML Full-text
Abstract
Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been [...] Read more.
Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and “smart applications” of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated “rain event”. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm−1 and 763 cm−1). The integrated intensities of these two peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (“sill”, “range”, and “nugget”) were examined as possible “indicators” of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Non-Lambertian Corrected Albedo and Vegetation Index for Estimating Land Evapotranspiration in a Heterogeneous Semi-Arid Landscape
Remote Sens. 2010, 2(4), 926-938; doi:10.3390/rs2040926
Received: 29 December 2009 / Revised: 15 March 2010 / Accepted: 19 March 2010 / Published: 30 March 2010
Cited by 10 | PDF Full-text (677 KB) | HTML Full-text | XML Full-text
Abstract
The application of energy balance algorithms to remotely sensed imagery often fails to account for surface roughness variation with diverse land cover, resulting in poor resolution of evapotranspiration (ET) variations. Furthermore, the assumption of a horizontally homogeneous Lambertian surface reflecting energy equally [...] Read more.
The application of energy balance algorithms to remotely sensed imagery often fails to account for surface roughness variation with diverse land cover, resulting in poor resolution of evapotranspiration (ET) variations. Furthermore, the assumption of a horizontally homogeneous Lambertian surface reflecting energy equally in all directions affects the calculations of albedo and vegetation index. The primary objective of this study is to improve the accuracy of the estimation and discrimination of ET among different land cover types in Southern New Mexico from ASTER datasets, by formulating the spatial variation of non-Lambertian reflectance using a wavelength-dependent Minnaert function. Full article
Open AccessArticle Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data
Remote Sens. 2010, 2(4), 939-956; doi:10.3390/rs2040939
Received: 21 December 2009 / Revised: 22 March 2010 / Accepted: 23 March 2010 / Published: 31 March 2010
Cited by 28 | PDF Full-text (1516 KB) | HTML Full-text | XML Full-text
Abstract
This work aims to establish a relationship between volume and biomass with interferometric and radiometric SAR (Synthetic Aperture Radar) response from planted Eucalyptus saligna forest stands, using multi-variable regression techniques. X and P band SAR images from the airborne OrbiSAR-1 sensor, were [...] Read more.
This work aims to establish a relationship between volume and biomass with interferometric and radiometric SAR (Synthetic Aperture Radar) response from planted Eucalyptus saligna forest stands, using multi-variable regression techniques. X and P band SAR images from the airborne OrbiSAR-1 sensor, were acquired at the study area in the southeast region of Brazil. The interferometric height (Hint = difference between interferometric digital elevation model in X and P bands), contributed to the models developed due to fact that Eucalyptus forest is composed of individuals whose structure is predominantly cylindrical and vertically oriented, and whose tree heights have great correlation with volume and biomass. The volume model showed that the stand volume was highly correlated with the interferometric height logarithm (Log10Hint), since Eucalyptus tree volume has a linear relationship with the vegetation height. The biomass model showed that the combination of both Hint2 and Canopy Scattering Index—CSI (relation of s°VV by the sum of s°VV and s°HH, which represents to the canopy interaction) were used in this model, due to the fact that the Eucalyptus biomass and the trees height relationship is not linear. Both models showed a prediction error of around 10% to estimate the Eucalyptus biomass and volume, which represents a great potential to use this kind of technology to help establish Eucalyptus forest inventory for large areas. Full article
Open AccessArticle Towards Multidecadal Consistent Meteosat Surface Albedo Time Series
Remote Sens. 2010, 2(4), 957-967; doi:10.3390/rs2040957
Received: 3 March 2010 / Revised: 28 March 2010 / Accepted: 29 March 2010 / Published: 31 March 2010
Cited by 18 | PDF Full-text (466 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring of land surface albedo dynamics is important for the understanding of observed climate trends. Recently developed multidecadal surface albedo data products, derived from a series of geostationary satellite data, provide the opportunity to study long term surface albedo dynamics at the [...] Read more.
Monitoring of land surface albedo dynamics is important for the understanding of observed climate trends. Recently developed multidecadal surface albedo data products, derived from a series of geostationary satellite data, provide the opportunity to study long term surface albedo dynamics at the regional to global scale. Reliable estimates of temporal trends in surface albedo require carefully calibrated and homogenized long term satellite data records and derived products. The present paper investigates the long term consistency of a new surface albedo product derived from Meteosat First Generation (MFG) geostationary satellites for the time period 1982–2006. The temporal consistency of the data set is characterized. The analysis of the long term homogeneity reveals some discrepancies in the time series related to uncertainties in the characterization of the sensor spectral response of some of the MFG satellites. A method to compensate for uncertainties in the current data product is proposed and evaluated. Full article
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Open AccessArticle Comparative Analysis of Clustering-Based Approaches for 3-D Single Tree Detection Using Airborne Fullwave Lidar Data
Remote Sens. 2010, 2(4), 968-989; doi:10.3390/rs2040968
Received: 18 January 2010 / Revised: 20 February 2010 / Accepted: 27 February 2010 / Published: 1 April 2010
Cited by 19 | PDF Full-text (996 KB)
Abstract
In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data [...] Read more.
In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4–5 points/m2. Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data. Full article
Open AccessArticle Monitoring Vegetation Phenological Cycles in Two Different Semi-Arid Environmental Settings Using a Ground-Based NDVI System: A Potential Approach to Improve Satellite Data Interpretation
Remote Sens. 2010, 2(4), 990-1013; doi:10.3390/rs2040990
Received: 23 November 2009 / Revised: 29 December 2009 / Accepted: 20 February 2010 / Published: 6 April 2010
Cited by 7 | PDF Full-text (1198 KB) | HTML Full-text | XML Full-text
Abstract
In semi-arid environmental settings with sparse canopy covers, obtaining remotely sensed information on soil and vegetative growth characteristics at finer spatial and temporal scales than most satellite platforms is crucial for validating and interpreting satellite data sets. In this study, we used [...] Read more.
In semi-arid environmental settings with sparse canopy covers, obtaining remotely sensed information on soil and vegetative growth characteristics at finer spatial and temporal scales than most satellite platforms is crucial for validating and interpreting satellite data sets. In this study, we used a ground-based NDVI system to provide continuous time series analysis of individual shrub species and soil surface characteristics in two different semi-arid environmental settings located in the Great Basin (NV, USA). The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover. NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. NDVI time series values were significantly different between sagebrush (Artemisia tridentata) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites. Greasewood NDVI from the SNK1 site produced significant correlations with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas greasewood NDVI from the SV1 site produced lower correlations (r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1. Results from this study indicated the potential capabilities of using this ground-based NDVI system to extract spatial and temporal details of soil and vegetation optical properties not possible with satellite derived NDVI. Full article
Open AccessArticle Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study
Remote Sens. 2010, 2(4), 1014-1034; doi:10.3390/rs2041014
Received: 8 February 2010 / Revised: 27 March 2010 / Accepted: 30 March 2010 / Published: 7 April 2010
Cited by 8 | PDF Full-text (1384 KB) | HTML Full-text | XML Full-text
Abstract
In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana’a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where [...] Read more.
In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana’a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana’a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate changes in population density per built-up area. Population densities increased in all districts, with the greatest density change in the peripheral areas towards the North. The results reflect the pressure on the city’s infrastructure and natural resources and could contribute to sustainable urban planning in the city of Sana’a. Full article
(This article belongs to the Special Issue Multi-Temporal Remote Sensing)
Open AccessArticle Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data
Remote Sens. 2010, 2(4), 1035-1056; doi:10.3390/rs2041035
Received: 17 February 2010 / Revised: 22 March 2010 / Accepted: 23 March 2010 / Published: 8 April 2010
Cited by 58 | PDF Full-text (1289 KB) | HTML Full-text | XML Full-text
Abstract
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of [...] Read more.
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm. Full article
(This article belongs to the Special Issue Global Croplands)
Open AccessArticle Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data
Remote Sens. 2010, 2(4), 1057-1076; doi:10.3390/rs2041057
Received: 16 January 2010 / Revised: 24 March 2010 / Accepted: 25 March 2010 / Published: 9 April 2010
Cited by 97 | PDF Full-text (1686 KB) | HTML Full-text | XML Full-text
Abstract
This study’s overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed [...] Read more.
This study’s overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed data. In 2003 flex fuel automobiles started to enter the Brazilian consumer market causing a dramatic expansion of sugarcane areas from 2.57 million ha in 2003 to 4.45 million ha in 2008. Almost all the land use change, for the sugarcane expansion of crop year 2008/09, occurred on pasture and annual crop land, being equally distributed on each. It was also observed that during the 2008 harvest season, the burned sugarcane area was reduced to 50% of the total harvested area in response to a protocol that aims to cease sugarcane straw burning practice by 2014 for mechanized areas. This study indicates that remote sensing images have efficiently evaluated important characteristics of the sugarcane cultivation dynamic providing quantitative results that are relevant to the debate of sustainable ethanol production from sugarcane in Brazil. Full article
(This article belongs to the Special Issue Global Croplands)
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Open AccessArticle Forest Roads Mapped Using LiDAR in Steep Forested Terrain
Remote Sens. 2010, 2(4), 1120-1141; doi:10.3390/rs2041120
Received: 25 January 2010 / Revised: 9 March 2010 / Accepted: 13 April 2010 / Published: 15 April 2010
Cited by 17 | PDF Full-text (2088 KB) | HTML Full-text | XML Full-text
Abstract
LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road [...] Read more.
LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM. In comparison to a field-surveyed centerline, the LiDAR-derived road exhibited a positional accuracy of 1.5 m, road grade measurements within 0.53% mean absolute difference, and total road length within 0.2% of the field-surveyed length. Airborne LiDAR can provide thorough and accurate road inventory data to support forest management and watershed assessment activities. Full article
(This article belongs to the Special Issue LiDAR)
Open AccessArticle Remote Sensing of Vegetation Structure Using Computer Vision
Remote Sens. 2010, 2(4), 1157-1176; doi:10.3390/rs2041157
Received: 2 March 2010 / Revised: 1 April 2010 / Accepted: 18 April 2010 / Published: 21 April 2010
Cited by 42 | PDF Full-text (6930 KB) | HTML Full-text | XML Full-text
Abstract
High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft. Here we demonstrate that high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using inexpensive hobbyist aerial platforms. Digital photographs were acquired using a kite aerial platform across two 2.25 ha test sites in Baltimore, MD, USA. An open-source computer vision algorithm generated 3D point cloud datasets with RGB spectral attributes from the photographs and these were geocorrected to a horizontal precision of <1.5 m (root mean square error; RMSE) using ground control points (GCPs) obtained from local orthophotographs and public domain digital terrain models (DTM). Point cloud vertical precisions ranged from 0.6 to 4.3 m RMSE depending on the precision of GCP elevations used for geocorrection. Tree canopy height models (CHMs) generated from both computer vision and LiDAR point clouds across sites adequately predicted field-measured tree heights, though LiDAR showed greater precision (R2 > 0.82) than computer vision (R2 > 0.64), primarily because of difficulties observing terrain under closed canopy forest. Results confirm that computer vision can support ultra-low-cost, user-deployed high spatial resolution 3D remote sensing of vegetation structure. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Open AccessArticle Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops
Remote Sens. 2010, 2(4), 1177-1196; doi:10.3390/rs2041177
Received: 10 February 2010 / Revised: 20 April 2010 / Accepted: 21 April 2010 / Published: 26 April 2010
Cited by 18 | PDF Full-text (398 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. [...] Read more.
Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use. Full article
(This article belongs to the Special Issue Global Croplands)

Review

Jump to: Research

Open AccessReview Monitoring Automotive Particulate Matter Emissions with LiDAR: A Review
Remote Sens. 2010, 2(4), 1077-1119; doi:10.3390/rs2041077
Received: 5 January 2010 / Revised: 15 March 2010 / Accepted: 30 March 2010 / Published: 9 April 2010
Cited by 4 | PDF Full-text (914 KB) | HTML Full-text | XML Full-text
Abstract
Automotive particulate matter (PM) causes deleterious effects on health and visibility. Physical and chemical properties of PM also influence climate change. Roadside remote sensing of automotive emissions is a valuable option for assessing the contribution of individual vehicles to the total PM [...] Read more.
Automotive particulate matter (PM) causes deleterious effects on health and visibility. Physical and chemical properties of PM also influence climate change. Roadside remote sensing of automotive emissions is a valuable option for assessing the contribution of individual vehicles to the total PM burden. LiDAR represents a unique approach that allows measuring PM emissions from in-use vehicles with high sensitivity. This publication reviews vehicle emission remote sensing measurements using ultraviolet LiDAR and transmissometer systems. The paper discusses the measurement theory and documents examples of how these techniques provide a unique perspective for exhaust emissions of individual and groups of vehicles. Full article
(This article belongs to the Special Issue LiDAR)
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Open AccessReview Ten Years of SeaWinds on QuikSCAT for Snow Applications
Remote Sens. 2010, 2(4), 1142-1156; doi:10.3390/rs2041142
Received: 2 March 2010 / Revised: 24 March 2010 / Accepted: 30 March 2010 / Published: 16 April 2010
Cited by 18 | PDF Full-text (771 KB) | HTML Full-text | XML Full-text
Abstract
The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap [...] Read more.
The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)

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