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Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Specifically, we included MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We used Partial Least Square Regression (PLSR), an effective statistical model for dimension reduction, to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. In the PLSR model that includes all of the satellite data and climate variables from 2007 to 2009, we assessed the first two major PLSR components and found that the first component (an integrated proxy of crop aboveground biomass) explained 82% variability of modelled crop yield, and the second component (dominated by environmental stresses) explained 15% variability of modelled crop yield. We found that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass (i.e. the first component). For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we found that EVI and SIF-GPP do not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide unique information on environmental stresses that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications. [ABSTRACT FROM AUTHOR]

The Geminid meteor shower was observed in 2015 using the Western Meteor Physics Group's Canadian Meteor Orbit Radar (CMOR), Marshall Space Flight Center's (MSFC) eight wide-field optical cameras, and telescopes of MSFC's lunar impact monitoring program. These observations allowed Geminid fluxes to be calculated in three unique mass-ranges, from 1.8×10 −4 g to 30 g. From these fluxes, a mass index of 1.68±0.04 was measured. The quantities derived here, along with a profile of the Geminid meteor shower activity in 2015 from CMOR, were used to find a total Geminid mass of 3.5×10 7 g the Earth encountered in 2015, along with a minimum total mass for the Geminid meteoroid stream of 1.6×10 16 g. Attempts have been made in the past to measure the mass of meteoroid streams using ZHR profiles, but this new and improved treatment uses empirically derived fluxes and measured mass indices for the 2015 encounter with the meteoroid stream. These results are compared to meteoroid stream mass estimate measured for the Perseids of 3.3×10 17 g. Instead of three flux values, a single flux value and an assumed mass index of 1.78 was used in Perseid analysis. [ABSTRACT FROM AUTHOR]

The initial results of a two year simultaneous optical-radar meteor campaign are described. Analysis of 105 double-station optical meteors having plane of sky intersection angles greater than 5° and trail lengths in excess of 2 km also detected by the Middle Atmosphere Alomar Radar System (MAARSY) as head echoes was performed. These events show a median deviation in radiants between radar and optical determinations of 1.5°, with 1/3 of events having radiant agreement to less than one degree. MAARSY tends to record average speeds roughly 0.5 km/s and 1.3 km higher than optical records, in part due to the higher sensitivity of MAARSY as compared to the optical instruments. More than 98% of all head echoes are not detected with the optical system. Using this non-detection ratio and the known limiting sensitivity of the cameras, we estimate that the limiting meteoroid detection mass of MAARSY is in the 10 −9 –10 −10 kg (astronomical limiting meteor magnitudes of +11 to +12) appropriate to speeds from 30 to 60 km/s. There is a clear trend of higher peak RCS for brighter meteors between 35 and −30 dBsm. For meteors with similar magnitudes, the MAARSY head echo radar cross-section is larger at higher speeds. Brighter meteors at fixed heights and similar speeds have consistently, on average, larger RCS values, in accordance with established scattering theory. However, our data show RCS ∝ v/2, much weaker than the normally assumed RCS ∝ v 3 , a consequence of our requiring head echoes to also be detectable optically. Most events show a smooth variation of RCS with height broadly following the light production behavior. A significant minority of meteors show large variations in RCS relative to the optical light curve over common height intervals, reflecting fragmentation or possibly differential ablation. No optically detected meteor occurring in the main radar beam and at times when the radar was collecting head echo data went unrecorded by MAARSY. Thus there does not appear to be any large scale bias in MAARSY head echo detections for the (comparatively) larger optical events in our dataset, even at very low speeds. [ABSTRACT FROM AUTHOR]

Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is challenging, but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics Investigation) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina's Second Native Forest Inventory (2015–2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 × 3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 × 3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r = 0.89 and 0.85) outperformed those from GEDI (r = 0.81) and the GFCH (r = 0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina's native forest areas in support of forest management and conservation planning across the country. • Nationwide mapping of multiple forest structure attributes at 30-m resolution. • Uncertainty maps of each forest structure attribute at 30-m resolution. • Best predictors were a combination of Sentinel-1 and 2 data plus coordinates. • Forest height predictions outperformed those from GEDI and the GFCH. • Reliable predictions to support sustainable forest management in Argentina. [ABSTRACT FROM AUTHOR]

Abstract: The Alligator Rivers region is located in the wet–dry tropics along the coastal zone of northern Australia and contains Kakadu National Park, which is recognized under the Ramsar Convention on Wetlands and is a World Heritage listed site. Multiple anthropogenic stressors increasingly affect the floodplains of this region, and baseline information on floodplain inundation dynamics is necessary to manage these threats and develop adaption strategies for sea level rise. This study uses classification tree modeling to combine microwave (ALOS L-band Synthetic Aperture Radar) and optical (Landsat Thematic Mapper, TM 5) satellite data with field-sampled aquatic vegetation and depth logger data to predict the seasonal and inter-annual dynamics of aquatic plant cover and extent of inundation in the region. The USGS Landsat TM 5 image archive was sampled between 1985 and 2011 using three seasonal samples per year to create a comprehensive long-term time series of seasonal and inter-annual floodplain inundation extents. Classification accuracy for the inundation mapping was estimated at 86% based on seasonal depth logger data. The mean extent of inundation at the end of the wet season (March/April) was 1784km2 (range 2283–1309km2), receding on average to approximately 25% of its extent by August/September. Seasonal inundation patterns exhibit an exponential recession of inundation into ‘backswamp’ areas on the fringes of the floodplains with hydro-periods on the order of 5months. The findings of this work significantly improve our understanding of dynamics in this environmentally and culturally unique area and provide a basis for application in other seasonally flooded environments. [Copyright &y& Elsevier]

Abstract: Simulation of future satellite images can be applied in order to validate the general mission concept and to test the performance of advanced multi-sensor algorithms for the retrieval of surface parameters. This paper describes the radiative transfer modeling part of a so-called Land Scene Generator (LSG) that was developed to simulate images of the sensors OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) on board of the Sentinel-3 mission. Features of this mission are its wide spectral coverage (optical and thermal domains) and its wide imaging swath, which imposes particular requirements on the simulator in dealing with atmospheric effects over both spectral domains and with angular effects caused by variations in surface bi-directional reflectance distribution function (BRDF) and atmospheric scattering. In the simulator, radiative transfer models for the combination vegetation-soil and for water are coupled to atmospheric parameters derived from MODTRAN runs in order to calculate top-of-atmosphere radiances. For this, four-stream radiative transfer theory is applied to allow simulation of BRDF effects, topography effects, adjacency effects, as well as its uniform application over the optical and thermal spectral domains. [Copyright &y& Elsevier]

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional-to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km2 in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service–Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r =0.88) was achieved for the prototype mapping zone. [Copyright &y& Elsevier]

We present a statistical analysis of meteors observed simultaneously with optical imaging and the Poker Flat Incoherent Scatter Radar (PFISR). The observations analyzed were from 3 nights (3, 4 and October 5, 2010), totaling 20.5 ​h of observation time. Optical observations alone revealed 493 meteors, while the PFISR data contained 9324 meteors over that same time period. There were 224 meteors that were detected by both PFISR and the optical imaging. The focus of this paper is on these 224 meteors that were detected by both methods. For these meteors, the masses were calculated from the optical light curves combined with the total velocities and were found to range between 0.4 and 204 ​mg. Using these meteor observations as a baseline, the minimum meteoroid mass detectable by the PFISR radar was estimated to be around 0.14 ± 0.01 ​mg for a 70 ​km/s meteor. The origination radiants of these 224 meteors were calculated in Heliocentric ecliptic coordinates and they were found to originate from many different areas in the northern hemisphere and not particularly concentrated to any of the sporadic meteor complex sources. • The minimum meteoroid mass detectable by PFISR is estimated to be around 0.14 ​mg. • There is no apparent correlation observed between the raw radar SNR and the optically derived meteoroid mass. • Milligram sized meteors observed at PFISR do not appear to originate from any preferential radiant points. [ABSTRACT FROM AUTHOR]

• Contributions and complementarities of the different frequencies for detecting 4 types of land use (and two states of bare soils. • The use of one mono-frequency images provides limited overall results. • A significant improvement in the classifications by fusing images delivered by optical and radar sensors, for most of the classes studied. • The complementary nature of the contributions made by L-band and optical waves. The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010. The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used. [ABSTRACT FROM AUTHOR]

The quantification of vegetation height for the circumpolar Arctic tundra biome is of interest for a wide range of applications, including biomass and habitat studies as well as permafrost modelling in the context of climate change. To date, only indices from multispectral data have been used in these environments to address biomass and vegetation changes over time. The retrieval of vegetation height itself has not been attempted so far over larger areas. Synthetic Aperture Radar (SAR) holds promise for canopy modeling over large extents, but the high variability of near-surface soil moisture during the snow-free season is a major challenge for application of SAR in tundra for such a purpose. We hypothesized that tundra vegetation height can be derived from multispectral indices as well as from C-band SAR data acquired in winter (close to zero liquid water content). To test our hypothesis, we used C-band SAR data from Sentinel-1 and multi-spectral data from Sentinel-2. Results show that vegetation height can be derived with an RMSE of 44 cm from Normalized Difference Vegetation Index (NDVI) and 54 cm from Tasseled Cap Wetness index (TC). Retrieval from C-band SAR shows similar performance, but C-VV is more suitable than C-HH to derive vegetation height (RMSEs of 48 and 56 cm respectively). An exponential relationship with in situ height was evident for all tested parameters (NDVI, TC, C-VV and C-HH) suggesting that the C-band SAR and multi-spectral approaches possess similar capabilities including tundra biomass retrieval. Errors might occur in specific settings as a result of high surface roughness, high photosynthetic activity in wetlands or high snow density. We therefore introduce a method for combined use of Sentinel-1 and Sentinel-2 to address the ambiguities related to Arctic wetlands and barren rockfields. Snow-related deviations occur within tundra fire scars in permafrost areas in the case of C-VV use. The impact decreases with age of the fire scar, following permafrost and vegetation recovery. The evaluation of masked C-VV retrievals across different regions, tundra types and sources (in situ and circumpolar vegetation community classification from satellite data) suggests pan-Arctic applicability to map current conditions for heights up to 160 cm. The presented methodology will allow for new applications and provide advanced insight into changing environmental conditions in the Arctic. • Use of frozen condition backscatter allows vegetation height retrieval in tundra. • C-band SAR shows similar capabilities like multi-spectral indices. • The Tasseled Cap Wetness Index is specifically applicable in northern tundra. • The evaluation across different regions suggests circumpolar applicability of C-VV. • Combination of Sentinel-1 and Sentinel-2 allows treatment of ambiguities. [ABSTRACT FROM AUTHOR]

Following the developments in pulse compression techniques for increased range resolution and higher signal to noise ratio of radio wave radar systems, the concept of thermal wave radar (TWR) was introduced for enhanced depth resolvability in optical infrared thermography. However, considering the highly dispersive and overly damped behavior of heat wave, it is essential to systematically address both the opportunities and the limitations of the approach. In this regard, this paper is dedicated to a detailed analysis of the performance of TWR in inspection of carbon fiber reinforced polymers (CFRPs) through frequency and/or phase modulation of the excitation waveform. In addition to analogue frequency modulated (sweep) and discrete phase modulated (Barker binary coded) waveforms, a new discrete frequency-phase modulated (FPM) excitation waveform is introduced. All waveforms are formulated based on a central frequency so that their performance can be fairly compared to each other and to lock-in thermography at the same frequency. Depth resolvability of the waveforms, in terms of phase and lag of TWR, is firstly analyzed by an analytical solution to the 1D heat wave problem, and further by 3D finite element analysis which takes into account the anisotropic heat diffusivity of CFRPs, the non-uniform heating induced by the optical source and the measurement noise. The spectrum of the defect-induced phase contrast is calculated and, in view of that, the critical influence of the chosen central frequency and the laminate's thickness on the performance of TWR is discussed. Various central frequencies are examined and the outstanding performance of TWR at relatively high excitation frequencies is highlighted, particularly when approaching the so-called blind frequency of a defect. [ABSTRACT FROM AUTHOR]