Search

Bistatic scattering from rough surfaces is typically approached through the analysis of the scattered field in the conventional H and V polarization basis, which coincides with the zenith and azimuth unit vectors in a spherical reference frame. This study delves into the impacts of different choices of the transmit and receive linear basis on the performance and design of a synthetic aperture radar (SAR) mission receive-only companion. This article formalizes the rotation of the scattered wave orientation at the antenna axes of the companion with respect to the transmitted one and introduces a novel set of linear polarizations, named principal polarizations, in transmit and receive, deemed more suited to represent the scattering mechanisms of rough surfaces. Such a set is defined by the polarization bases that maximize the radar cross section. It is shown that the theoretical estimates from the proposed geometrical framework provide a good agreement with analytical and numerical simulations, performed considering state-of-the-art numerical solutions. In addition, this article promotes the hypothesis that a bistatic radar configuration, defined through the conventional H and V linear basis, presents a strong similarity, from a target information retrieval standpoint, to a monostatic compact $\varphi $ -pol mode, i.e., with the transmission of a linear polarization rotated by an angle $\varphi $. The rotation $\varphi $ varies over the swath and as a function of satellite separation. For baselines of 250–300 km, such as those envisioned by the European Space Agency (ESA) Harmony Earth Explorer candidate, and for steep incidence angles, an equivalent $\pi /8$ -pol can be achieved for rough surfaces. [ABSTRACT FROM AUTHOR]

In this paper, a numerical method optimizer based on covariance matching is proposed for synthetic aperture radar (SAR) polarimetric calibration. The method makes use of the information provided by a distributed target and a corner reflector in order to jointly estimate the system polarimetric distortion parameters and the Faraday rotation. A preliminary analysis is conducted to show the expected accuracy values and to identify the intrinsic ambiguities of the problem. Results from simulations are shown to assess the accuracy and convergence of the method. Finally, tests have been conducted on stack of repeated full polarimetric ALOS PALSAR images to check the stability of the retrieved distortion parameters in a realistic case. [ABSTRACT FROM PUBLISHER]

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) acquisitions are mainly deemed suitable for mapping dynamic land-cover and land-use scenarios due to their timeliness and reliability. This particularly applies to Sentinel-1 imagery. Nevertheless, the accurate mapping of regions characterized by a mixture of crops and grasses can still represent a challenge. Radar time-series have to date mainly been exploited through backscatter intensities, whereas only fewer contributions have focused on analyzing the potential of interferometric information, intuitively enhanced by the short revisit. In this paper, we evaluate, as primary objective, the added value of short-temporal baseline coherences over a complex agricultural area in the São Paulo state, cultivated with heterogeneously (asynchronously) managed annual crops, grasses for pasture and sugarcane plantations. We also investigated the sensitivity of the radar information to the classification methods as well as to the data preparation and sampling practices. Two supervised machine learning methods—namely support vector machine (SVM) and random forest (RF)—were applied to the Sentinel-1 time-series at the pixel and field levels. The results highlight that an improvement of 10 percentage points (p.p.) in the classification accuracy can be achieved by using the coherence in addition to the backscatter intensity and by combining co-polarized (VV) and cross-polarized (VH) information. It is shown that the largest contribution in class discrimination is brought during winter, when dry vegetation and bare soils can be expected. One of the added values of coherence was indeed identified in the enhanced sensitivity to harvest events in a small but significant number of cases. [ABSTRACT FROM AUTHOR]

Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency's Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies. [ABSTRACT FROM AUTHOR]

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Vegetation Characterization through the Use of Precipitation-Affected SAR Signals" which appeared in the November 21, 2018 issue.