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The next World Radio Conference (WRC) will be held in November 2019 in Geneva, Switzerland. This paper discusses WRC-19 agenda items that could impact scientific uses in Earth satellite remote sensing and radio astronomy.

The objectives of this research are to (1) address remote sensing strategies and requirements for estimating snow depth and snow water equivalent (SWE) using existing L-Band interferometric data sets in coordination with field-based observations and modeling frameworks and, with this information, (2) inform the Next Generation Cold Land Processes Experiment (SnowEx) toward articulating the appropriate science and research questions for a single motivating science plan. As proposed, SnowEx is a multi-year airborne snow campaign with a primary goal of exploring multimodal sensor observations in coordination with field campaigns to inform the next generation snow remote sensing satellite platform. Based on limitations of satellite-based optical and LiDAR instruments operating in regions of the globe with consistent cloud-cover, the fact that many snow-dominated regions are at more northerly latitudes (limited solar illumination in the middle of winter), and these snow-dominated regions often experience periods of prolonged cloud cover (due to synoptic precipitation events), a microwave remote sensing platform may be the most viable path to space for a dedicated snow remote sensing mission. Specifically, L-Band radar interferometry has shown some unique promise with an archive of historical and contemporary satellite collections from JAXA's PALSAR-1 and PALSAR-2 instruments, respectively. Moreover, with the expected NISAR (NASA-ISRO Synthetic Aperture Radar) mission launch in 2020 and the unprecedented availability of dedicated global interferometric L-Band products every 12-days, as well as what is in essence a NISAR airborne simulator in JPL's UAVSAR platform, the L-Band interferometric approach to estimating snow depth and snow water equivalent (SWE) requires further investigation within the context of in-situ observations and modeling frameworks.

Spaceborne SAR interferometry (InSAR) has the potential of mapping the forest height on a global scale and a monthly/weekly basis, which can improve our understanding of the global carbon dynamics. In previous work, repeat-pass SAR interferometry from spaceborne sensors is utilized to create large-scale forest height maps based on a newly developed approach. This paper thus serves as a summary paper and also sheds light on the future directions with improved results. In particular, it will be shown that repeat-pass SAR interferometry is able to create a large-scale (11.6 million hectares) forest height mosaic product with RMSE ≤ 4 m for forest stands on the order of 6 hectares over both the flat and mountainous areas in New England, US through using the past and current spaceborne repeat-pass InSAR observations (i.e. JAXA's ALOS-1 and ALOS-2) combined with sparse airborne lidar training samples (44,000 hectares). Moreover, the results and performance of this approach can be remarkably improved with several enhancement techniques that can be easily satisfied with the use of future spaceborne repeat-pass InSAR and lidar missions (e.g. NASA-ISRO's NISAR and NASA's GEDI). The methodology described in this paper can be considered as a complimentary tool to the existing PolInSAR technique when single-pass full/dual-pol data are not available and/or the underlying topography is complicated.

Spaceborne SAR interferometry (InSAR) has the potential of detecting forest change on a global scale with fine (meter-level) spatial resolution as well as on a monthly/weekly basis regardless of day or night. This is significant to characterize the land-use change and its impact on climate change. In this paper, both single-pass and repeat-pass SAR interferometry from spaceborne sensors are combined in order to detect and quantify (with Normalized RMSE ≤ 30%) forest disturbance at a large scale (dozens of kilometers) however with a fine spatial resolution (< 1 hectare) based on two newly developed approaches. The single-pass InSAR approach is not only able to detect forest disturbance but also capable of characterizing meter (or even sub-meter) level change of forest phase-center (mean) height due to forest growth and/or degradation. These methods are extensively validated with the past and current spaceborne single-pass and repeat-pass InSAR missions (i.e. JAXA's ALOS-1, ALOS-2 and DLR's TanDEM-X) over subtropical forests in Australia as well as tropical forests in Brazil. Such techniques also serve as observing prototypes for the fusion of the future spaceborne InSAR missions (such as NASA-ISRO's NISAR and DLR's TanDEM-L).

In this paper, an agricultural land cover classification algorithm is presented that uses L-band SAR observations over time to create a crop/non-crop classification. A statistical measurement known as the coefficient of variation (CV) is introduced, which as a measurement of change is able to differentiate between changing agricultural fields and relatively static non-crop areas. Images from the airborne AgriSAR 2006 campaign in northern Germany and from the ALOS PALSAR satellite over Minnesota, USA are used to demonstrate the algorithm. The small region covered by the AgriSAR campaign is used to highlight the differences in behavior of L- and C-band, and in this case the better results of L-band data when using the CV algorithm. Results in both regions show promise for future use of the CV algorithm.

This paper describes of a ground-based synthetic radar (SAR) which can take short- and long-term radar cross-section measurements that can be used for the monitoring of dynamic forest characteristics through the instrument's sensitivity to the dielectric constants for the soil and woody structures. The SAR image is generated through the successive transmission/reception of radar echoes from a tram-mounted sensor that sits 20 meters above the forest floor. Processing of the data into range and azimuthally resolved imagery is achieved through the backprojection algorithm.