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Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400–2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field. [ABSTRACT FROM AUTHOR]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

This paper presents a new method for estimating nearshore wave height from a digital video sequence. The method identifies main wave breaking zones in the video records and estimates the height of breaking waves inside the detected breaking zones. A geometric rectification is applied to the resulting estimation to convert the height measurement from image pixels to meters. The validation of the algorithm was undertaken over three months at Surfers Paradise, Australia. The performance of the algorithm was demonstrated to be comparable with that of buoy-measured wave height, as well as manual estimates of the onshore wave height by a surf reporter. The results indicate that the method can be used as a cost-effective tool for long-term monitoring of nearshore wave conditions. [ABSTRACT FROM PUBLISHER]

In support of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission, a number of soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out. The radiometer used in this context is fully polarimetric and has built-in radio-frequency-interference (RFI)-detection capabilities. Thus, the instrument, in addition to supplying L-band data to the geophysicists, also gave valuable information about the RFI environment. Campaigns were carried out in Australia and in a variety of European locations, resulting in the largest and most comprehensive data set available for assessing RFI at L-band. This paper introduces the radiometer system and how it detects RFI using the kurtosis method, reports on the percentage of data that are typically flagged as being corrupted by RFI, and gives a hint about geographical distribution. Also, examples of polarimetric signatures are given, and the possibility of detecting RFI using such data is discussed. [ABSTRACT FROM AUTHOR]

Plume heights from wildfires are used in climate modeling to predict and understand trends in aerosol transport. This paper examines whether smoke from grassland fires in the desert regions of western and central Australia ever rises above the atmospheric boundary layer. Three methods for deriving plume heights from the Multi-angle Imaging SpectroRadiometer (MISR) instrument were utilized: 1) the MISR standard stereo-height algorithm; 2) the MISR enhanced stereo product; and 3) the MISR INteractive eXplorer (MINX) v.1 tool. To provide context and to search for correlative factors, stereo heights were combined with fire radiant energy flux from the Moderate Resolution Imaging Spectroradiometer instrument, atmospheric structure information from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project model, surface cover from the Australia National Vegetation Information System, and forward and backward trajectories from the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory model. Although most smoke plumes concentrate in the near-surface boundary layer as expected, some appear to rise higher. Smoke that gets above the boundary layer will travel farther, remain the atmosphere longer, and therefore have a larger environmental impact. It was previously thought unlikely for grassland fires inject smoke above the boundary layer. Our findings suggest that climate modelers should reevaluate common assumptions about the heights of smoke plumes when producing aerosol transport models involving grassland fires. A closer examination of grassland fire energetics may also be warranted. [ABSTRACT FROM AUTHOR]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]