Extensive Ground Measurements Research Articles

Determining the accuracy of the landsat-based land continuous Variable Estimator

The inversion framework Land continuous Variable Estimator for Landsat data (LoVE)-Landsat has been recently developed, but the estimation accuracy has not been well determined. LoVE-Landsat is a data-assimilation-based inversion framework capable of estimating a series of daily 30-m spatiotemporal continuous land surface variables from Landsat top-of-atmosphere (TOA) data. This paper presents the comprehensive validation results of a land surface variables estimation conducted through LoVE-Landsat, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), surface broadband albedo, incident photosynthetically active radiation (PAR), and incident shortwave radiation (ISR). This validation involved a direct validation using extensive ground measurements and an inter-comparison with existing satellite products. In situ measurements came from a total of 196 sites covering different biome types from seven networks distributed around the world. Among these sites, more than 2000 LAI and FAPAR plot measurements representing the Landsat pixel size were collected from 40 sites of the Bigfoot, VALERI, and ImagineS networks, about 100 LAI and FAPAR reference values at 3-km scale were provided by 52 sites of DIRECT datasets, and nearly 70,000 albedo and ISR and 40,000 PAR tower-based measurements were gathered from 143 sites of Ameriflux, Euroflux, Ozflux, and SURFRAD networks. Results showed that the LoVE-Landsat 30 m LAI estimates were accurate, with R2 = 0.76 and root-mean-square-error (RMSE) = 0.89, and were slightly more accurate than the coarse resolution Global LAnd Surface Satellite (GLASS) LAI product at the kilometer scale (RMSE = 0.75 vs. 0.79). Validation at the 143 flux network sites showed that the daily snow-free LoVE-Landsat 30 m shortwave albedo had an accuracy comparable to the MODIS daily albedo product (RMSE around 0.037) and higher accuracy (RMSE = 0.031) when Landsat had clear-sky observations. Direct validation of ISR and PAR estimation showed high accuracy, with RMSE values of 102.8 and 48.7 W/m2, respectively. Spatial and temporal evaluation at six typical sites also showed that LoVE-Landsat could produce consistent spatially and temporally continuous estimation. Although a more comprehensive validation of all retrievals still needs to be conducted, both the direct validation and product comparison results of the key variables indicate that LoVE-Landsat can estimate a group of spatially and temporally continuous variables from Landsat observations accurately, which demonstrates the strong potential of LoVE-Landsat to generate global 30-m continuous land products.

Navigator Notes

Navigator Notes

Characterization of on‐orbit GPS transmit antenna patterns for space users

The GPS Antenna Characterization Experiment (GPS ACE) has made extensive observations of GPS L1 signals received at geosynchronous (GEO) altitude, with the objective of developing comprehensive models of the signal levels and signal performance in the GPS transmit antenna side lobes. The experiment was originally motivated by the fact that data on the characteristics and performance of the GPS signals available in GEO and other high Earth orbits was limited. The lack of knowledge of the power and accuracy of the side lobe signals on-orbit added risk to missions seeking to employ the side lobes to meet navigation requirements or improve performance. The GPS ACE Project lled that knowledge gap through a collaboration between The Aerospace Corporation and NASA Goddard Space Fight Center to collect and analyze observations from GPS side lobe transmissions to a satellite at GEO using a highly-sensitive GPS receiver installed at the ground station. The GPS ACE architecture has been in place collecting observations of the GPS constellation with extreme sensitivity for several years. This sensitivity combined with around-the-clock, all-in-view processing enabled full azimuthal coverage of the GPS transmit gain patterns over time to angles beyond 90 degrees o -boresight. Results discussed in this paper include the reconstructed transmit gain patterns, with comparisons to available pre-fight gain measurements from the GPS vehicle contractors. For GPS blocks with extensive ground measurements, the GPS ACE results show remarkable agreement with ground based measurements. For blocks without extensive ground measurements, the GPS ACE results provide the only existing assessments of the full transmit gain patterns. The paper also includes results of pseudorange deviation analysis to assess systematic errors associated with GPS side lobe signals.

Quantifying Western U.S. Rangelands as Fractional Components with Multi-Resolution Remote Sensing and In Situ Data

Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush (Artemisia), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30 m resolution. Extensive ground measurements, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree models were used to create component predictions. In the mapped area (2,993,655 km²), bare ground averaged 45.5%, shrub 15.2%, sagebrush 4.3%, big sagebrush 2.9%, herbaceous 23.0%, annual herbaceous 4.2%, and litter 15.8%. Component accuracies using independent validation across all components averaged R2 values of 0.46 and an root mean squared error (RMSE) of 10.37, and cross-validation averaged R2 values of 0.72 and an RMSE of 5.09. Component composition strongly varies by Environmental Protection Agency (EPA) level III ecoregions (n = 32): 17 are bare ground dominant, 11 herbaceous dominant, and four shrub dominant. Sagebrush physically covers 90,950 km², or 4.3%, of our study area, but is present in 883,449 km², or 41.5%, of the mapped portion of our study area.

Detection and Characterization of Rumble Strips in Roadway Video Logs

State departments of transportation often maintain extensive “video logs” of their roadways that include signs, lane markings, as well as non-image-based information such as grade, curvature, etc. In this work we use the Roadway Information Database (RID), developed for the Second Strategic Highway Research Program, as a surrogate for a video log to design and test algorithms to detect rumble strips in the roadway images. Rumble strips are grooved patterns at the lane extremities designed to produce an audible queue to drivers who are in danger of lane departure. The RID contains 6,203,576 images of roads in six locations across the United States with extensive ground truth information and measurements, but the rumble strip measurements (length and spacing) were not recorded. We use an image correction process along with automated feature extraction and convolutional neural networks to detect rumble strip locations and measure their length and pitch. Based on independent measurements, we estimate our true positive rate to be 93% and false positive rate to be 10% with errors in length and spacing on the order of 0.09 meters RMS and 0.04 meters RMS. Our results illustrate the feasibility of this approach to add value to video logs after initial capture as well as identify potential methods for autonomous navigation.

A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes

This study presents a comprehensive validation of the Soil Moisture Active Passive (SMAP) mission Enhanced Level-3 radiometer soil moisture (SM) product (Version 2) over 3 years from April 1, 2015 to March 31, 2018 using extensive ground measurements from sparse networks covering a range of physical and climatological regimes. Using a common spatial resolution, soil moisture retrievals from the descending (6:00 AM) and ascending (6:00 PM) overpasses were assessed based on static conditions such as the climate zone, soil property and land cover. Then validation considering performance with respect to dynamic attributes such as soil wetness, vegetation density and land surface temperature was reported. Given the above six parameters are cross-correlated, the quality of the SMAP enhanced products was further evaluated based on pairwise factors.Overall, higher accuracy was noted over zones where the soil organic carbon is low, the vegetation density is relatively sparse, locations in the temperate and arid climate zones, and the mean LST is high. Results also indicate that the descending (AM) and ascending (PM) products exhibit mean temporal correlation over the ground stations equaling 0.667 and 0.651, and mean unbiased root mean square error (ubRMSE) equaling 0.055 and 0.054 m3/m3 respectively which is close to the ubRMSE requirement of the SMAP mission, 0.04 m3/m3. While the ascending (PM) SM retrievals have been frequently excluded from applications due to its poor performances reported form validation studies, these comparable performances between the two products suggest that the ascending (PM) SM retrievals can be an additional data source. Based on the results here, it can be concluded that there is room for improving the SMAP product especially in areas of the world with very dense vegetation and average to low land surface temperatures.

Mapping global forest biomass and its changes over the first decade of the 21st century

Forests played an important role in carbon sequestration during the past two decades. Using a model tree ensemble method (MTE) to regress the seven reflectance bands of EOS-Terra-MODIS satellite data against country level forest biomass carbon density (BCD) of 2001–2005 provided by United Nations’s Forest Resource Assessment (FRA), we developed a global map of forest BCD at 1 km ×1 km resolution for both 2001–2005 and 2006–2010. For 2006–2010, the total global forest biomass carbon stock is estimated as 279.6±7.1 Pg C, and the tropical forest biomass carbon stock is estimated as 174.4±5.4 Pg C. During the first decade of the 21st century, we estimated an increase of global forest biomass of 0.28±0.75 Pg C yr−1. Tropical forest biomass carbon stock slightly decreased (−0.31±0.60 Pg C yr−1); by contrast, temperate and boreal forest biomass increased (0.58±0.28 Pg C yr−1) during the same period. Our estimation of the global forest biomass carbon stock and its changes is subject to uncertainties due to lack of extensive ground measurements in the tropics, spatial heterogeneity in large countries, and different definitions of forest. The continuously monitoring of forest biomass carbon stock with MODIS satellite data will provide useful information for detecting forest changes.

Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields

This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment

Abstract. Following the launch of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009, SMOS soil moisture products need to be rigorously validated at the satellite's approximately 45 km scale and disaggregation techniques for producing maps with finer resolutions tested. The Australian Airborne Cal/val Experiments for SMOS (AACES) provide the basis for one of the most comprehensive assessments of SMOS data world-wide by covering a range of topographic, climatic and land surface variability within an approximately 500 × 100 km2 study area, located in South-East Australia. The AACES calibration and validation activities consisted of two extensive field experiments which were undertaken across the Murrumbidgee River catchment during the Australian summer and winter season of 2010, respectively. The datasets include airborne L-band brightness temperature, thermal infrared and multi-spectral observations at 1 km resolution, as well as extensive ground measurements of near-surface soil moisture and ancillary data, such as soil temperature, soil texture, surface roughness, vegetation water content, dew amount, leaf area index and spectral characteristics of the vegetation. This paper explains the design and data collection strategy of the airborne and ground component of the two AACES campaigns and presents a preliminary analysis of the field measurements including the application and performance of the SMOS core retrieval model on the diverse land surface conditions captured by the experiments. The data described in this paper are publicly available from the website: http://www.moisturemap.monash.edu.au/aaces.

Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar

A ground-based, upward-scanning, near-infrared lidar, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stem count density (stems/area), basal area, and above-ground woody biomass with very good accuracy in six New England hardwood and conifer forest stands. Comparing forest structural parameters retrieved using EVI data with extensive ground measurements, we found excellent agreement at the site level using five EVI scans (plots) per site ( R 2 = 0.94–0.99); very good agreement at the plot level for stem count density and biomass ( R 2 = 0.90–0.85); and good agreement at the plot level for mean DBH and basal area ( R 2 = 0.48–0.66). The observed variance at site and plot levels suggest that a sample area of at least 1 ha (10 4 m 2) is required to estimate these parameters accurately at the stand level using either lidar-based or conventional methods. The algorithms and procedures used to retrieve these structural parameters are dependent on the unique ability of the Echidna® lidar to digitize the full waveform of the scattered lidar pulse as it returns to the instrument, which allows consistent separation of scattering by trunks and large branches from scattering by leaves. This successful application of ground-based lidar technology opens the door to rapid and accurate measurement of biomass and timber volume in areal sampling scenarios and as a calibration and validation tool for mapping biomass using airborne or spaceborne remotely sensed data.

EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

Abstract. EAGLE2006 – an intensive field campaign for the advances in land surface hydrometeorological processes – was carried out in the Netherlands from 8th to 18th June 2006, involving 16 institutions with in total 67 people from 16 different countries. In addition to the acquisition of multi-angle and multi-sensor satellite data, several airborne instruments – an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were deployed and extensive ground measurements were conducted over one grassland site at Cabauw and two forest sites at Loobos and Speulderbos in the central part of the Netherlands. The generated data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative land surface parameter estimation and land surface hydrometeorological process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation (ITC) and originated from the combination of a number of initiatives supported by different funding agencies. The objectives of the EAGLE2006 campaign were closely related to the objectives of other European Space Agency (ESA) campaign activities (SPARC2004, SEN2FLEX2005 and especially AGRISAR2006). However, one important objective of the EAGLE2006 campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired simultaneously over contrasting vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as the Sentinels and for validation of retrievals of land surface parameters with optical and thermal and microwave sensors onboard current and future satellite missions. This contribution describes the campaign objectives and provides an overview of the airborne and field campaign dataset. This dataset is available for scientific investigations and can be accessed on the ESA Principal Investigator Portal http://eopi.esa.int/.

Scavenging efficiency of rainfall on black carbon aerosols over an urban environment

AbstractBlack carbon (BC) aerosols are the optically absorbing part of carbonaceous aerosols that have significantly different optical and radiative properties. The present study addresses the estimation of black carbon aerosol scavenging coefficient by using ground‐based measurements over an urban environment of India, namely, Hyderabad. Extensive ground measurements of black carbon have been carried out during January to December 2004 over a tropical urban environment of Hyderabad. Seasonal variations of black carbon aerosol mass concentration showed high values during dry season and low values during monsoon season. The diurnal variations of BC suggest that the concentrations increased by a factor of ∼2 during morning and evening hours compared to afternoon hours. Drastic reduction in black carbon aerosol loading has been observed during rainy days. The statistical fit between black carbon aerosol mass concentration and rainfall suggests the reduction of ∼3.6 µg/m3 in atmospheric black carbon aerosol loading for every 1‐mm increase in rainfall intensity over the study area. The scavenging coefficient of black carbon aerosols is found to be 1.64 × 10−5 s−1. © Crown Copyright 2005. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.

Narrowband to broadband conversions of land surface albedo I: Algorithms

Land surface broadband albedo is a critical variable for many scientific applications. High-resolution narrowband satellite observations contain important information that enables us to map land surface albedo globally, and validate the coarse-resolution albedo products from the broadband sensors using ground “point/plot” measurements. However, the conversions from narrowband to broadband albedos of a general surface type have not been well established. Most studies compute total shortwave albedo based on either the empirical relations between surface total shortwave albedo measurements and satellite observations or radiative transfer simulations with the limited number of surface reflectance spectra because of the computational constraints. As a result, many conversion formulae for the same sensors are quite different. In this study, we applied an approach that decouples surface reflectance spectra from the real-time radiative transfer simulations so that many different surface reflectance spectra and the atmospheric conditions can be effectively incorporated. The conversion formulae, based on extensive radiative transfer simulations, are provided in this paper for calculating the total shortwave albedo, total-, direct-, and diffuse-visible, and near-infrared broadband albedos for several narrowband sensors, including ASTER, AVHRR, ETM+/TM, GOES, MODIS, MISR, POLDER, and VEGETATION. Some of these formulae were compared with the published formulae of the same sensors and further validations using extensive ground measurements will be discussed in the companion paper.

The potential of times series of C-Band SAR data to monitor dry and shallow snow cover

A study was conducted to assess the potential of C-band synthetic aperture radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE<20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0/spl deg/C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen.

The detection and mapping of buried waste

Determining the exact location of buried waste trenches is an important step in the characterization and remediation of certain hazardous waste sites. Remotely sensed data offers a rich source of information for accomplishing this task. This paper presents an investigation of buried waste trenches located at Oak Ridge National Laboratory (ORNL) using thermal remote sensing. A comparison of historical aerial photography and recently collected thermal imagery reveals a thermal signature which coincides with the precise locations of buried waste trenches. Statistical analysis of extensive ground measurements shows a clear thermal difference between the trench and control areas, with the trenches exhibiting cooler temperatures and greater soil moisture. By incorporating the imagery-derived information into site remediation plans, ORNL realized a cost avoidance of more than U.S. $5 000 000. Similar benefits can be anticipated at other DOE waste sites.

Analysis of Desert Plant Community Growth Patterns with High Temporal Resolution Satellite Spectra

1. The study of a desert landscape consisting of C3 shrublands and C4 grasslands demonstrated the feasibility of mapping desertification processes on a regional scale using time-series satellite images, and demonstrated the potential for monitoring desertification processes through time. 2. Fifteen meteorological satellite images of southern New Mexico, spread over the growing season of 1989, were processed to yield images of normalized difference vegetation indices. Computer classification of these images and comparison with extensive ground measurements confirmed the identification of the major classes of shrubland, grassland and mixed shrub and grass areas. 3. Our high-resolution temporal approach is shown to permit the accurate identification of vegetation types even under conditions of sparse vegetation cover. The phenologies of grassland and shrubland were revealed in the time series of satellite images. We used this feature of the data to overcome the tendency of the strong radiation signal from bare soil to mask the biomass signal. 4. Comprehensive comparisons with ground measurements confirmed the accuracy of the biomass identification for local areas within the Jornada Experimental Range, and justified extrapolation of the classification to a larger adjacent area.

Biomass Burning Airborne and Spaceborne Experiment in the Amazonas (BASE‐A)

In the Biomass Burning Airborne and Spaceborne Experiment in the Amazonas (BASE‐A), conducted in September 1989, trace gas and particulate matter emissions were measured from biomass burning due to deforestation and grassland fires in South America. This information is required for a better understanding of the environmental impacts of biomass burning in the tropics and to improve algorithms for remote sensing of biomass burning from satellite platforms. The field experiment utilized the twin‐engine Embraer Bandeirante EMB‐1Ol instrumented aircraft of the Brazilian Institute for Space Research (INPE). Concentrations of ozone, CO2, CO, CH4, and particulate matter were measured from the aircraft. Fires were observed from satellite imagery, and the smoke optical thickness, particle size, and profiles of the extinction coefficient were measured using sunphotometers in the aircraft and from the ground. Four smoke plumes were sampled, three vertical profiles were measured, and extensive ground measurements were conducted of smoke optical characteristics for different smoke types. The collected data were analyzed for determining the emission ratios and combustion efficiency (the efficiency of a fire to convert the total burned carbon to carbon dioxide) and were compared with the results from fires in North America. Combustion efficiency was found to be higher in the tropics (97% for the cerrado and 90% for the deforestation fires) with emission factors similar to those of North American fires, for a given combustion efficiency. A strong relation was found between the spatial distribution of fires (up to 9000 per day in one state) and ozone concentration (up to 80 ppbv) and between biomass burning and concentrations of trace gases, particulate matter, and ozone. These relations strongly suggest a correlation between biomass burning in the tropics and ozone formation. An optical model of the smoke aerosol was derived and applied to radiance measurements. The smoke single scattering albedo was computed from the graphitic carbon concentration (assuming external mode mixture) as 0.90 ± 0.01. The particles effective radii were 0.1 to 0.2 μm, except for 1‐day aged smoke with values up to 0.4 μm. Radiance measurements indicate that the width of the particle size distribution may be smaller in the tropics than for North American fires. The measured optical properties of smoke and the high correlation between emitted trace gases and particles form a basis for remote sensing of radiatively important trace gases and particulate matter from biomass burning using AVHRR imagery.

GRL plans issue on Arctic haze

Arctic haze, a winter‐spring air pollution phenomenon in the Arctic, has recently become the focus of accelerating research interest. In the spring of 1983 alone, at least seven atmospheric research aircraft from four nations were involved in studies related to Arctic haze. Extensive ground measurements of haze parameters were conducted by five countries with interests in the Arctic.These and earlier programs have produced new and exciting information covering a diverse range of topics. To assist in the overall study, interpretation, and dissemination of these data in a timely manner, a special issue/section of Geophysical Research Letters will be dedicated to the subject of Arctic haze and related meteorological/atmospheric studies. Publication is planned for the spring of 1984.

ESTIMATION OF SOIL MOISTURE FROM SEASAT SAR DATA1

ABSTRACT A study was made to determine if Seasat Synthetic Aperture Radar (SAR) data could be used to make practical estimates of soil moisture. Extensive ground measurements were collected at two primary sites near Guymon, Oklahoma, and Sublette, Kansas. The relative sensitivity of the SAR to differences in soil moisture, tillage roughness, and vegetation was determined. To validate the effects detected in the SAR data, an airborne scatterometer with a similar wavelength was flown repeatedly over the Guymon site.Soil moisture variations in the surface 2 cm and surface 15 cm of fields with bare soil, milo and alfalfa produce similar responses in the scattering coefficient from both systems. Roughness due to tillage in row crops produced as much as 12–15 dB increase in the scatterometer return. Most agricultural vegetation was effectively penetrated by the L‐band frequencies; however, corn produced an exceptionally high radar return either standing or after combine harvesting. When corn had ripened, there was some evidence that tillage roughness could be detected through the canopy. Moderate tillage roughness produced by grain drill furrows caused over 12 dB increase in return when row directions changed from parallel to perpendicular with respect to the SAR look direction. Dramatic increases in return occurred when vegetation surfaces were wet. Increased radar returns from tillage roughness, some vegetation and wet vegetation surfaces, all dyanmic in nature, were significant and may limit the practical estimation of soil moisture from the radar data.