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 φ-pol mode, i.e., with the transmission of a linear polarization rotated by an angle φ. The rotation φ 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 π8-pol can be achieved for rough surfaces.

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The study is aimed at understanding the value of interferometric coherence in mapping regions characterized by a mixture of crops and grasses. The results highlight that a 5% improvement in the classification accuracy can be achieved by using the coherence in addition to the backscatter intensity and by combining VV and VH. It is shown that the largest contribute in class discrimination is brought in winter, when dry vegetation and bare soils can be expected. It was also notably observed that coherence information can enhance the identification of harvesting events in a small but significant number of cases.

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The paper analyses the performances of a system concept consisting in a swarm of satellites operating in the SIMO (Single Input Multiple Output) configuration. In particular, two different data collection approaches, using constant and pseudo-random PRF, are analysed and compared for different number of satellites and various baseline knowledge uncertainties. In addition to the methods in literature, we propose a PRF optimisation algorithm based on the PSD (Power Spectral Density) of the received signal.These approaches are applied to the bi-dimensional case with a 2D simulator based on the chirp scaling algorithm used to synthesize jointly focused SAR images.

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Distributed SAR systems provide imaging capabilities that cannot be achieved by traditional monolithic satellites, thanks to the multiple angles and times of observation. In this paper the opportunities offered by a swarm of small satellite nodes operating in S-Band are discussed. The nodes fly in a close formation and operate in MIMO mode. All the $N$ satellites transmit and receive the $N$ pulses at the same time, following a Frequency Division Multiplexing (FDM) scheme. The work focuses in particular on the impact of the cross-track baselines on the resolution and on the quality of the signal reconstructed from the $N^{2}$ channels. A first iteration of a distributed target processor based on adaptive frequency and channel treatment is hence proposed with the aim of effectively accounting for the slope-induced spectral shifts.@en

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.

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Tropospheric delays are one of the main contributors to the interferometric phase in synthetic aperture radar (SAR) interferometry. When the phase contributions from surface deformation, topography, and ionospheric delays are negligible or known, the interferogram can be used to estimate the differential tropospheric delay (DTD), which can help to improve tropospheric delay predictions from weather models and in situ measurements. In conventional repeat-pass interferometric SAR (InSAR), however, the estimation of the DTD can still be significantly hindered by baseline errors. In addition, a single interferogram provides only relative DTDs, as the delays can be retrieved up to an unknown offset. To address such issues, this article presents a method for the estimation of DTDs on large scales by using repeat-pass simultaneous multi-angle SAR systems. Complementary simultaneous observations of the correlated troposphere from multiple angles are used to retrieve estimates of the absolute DTD and, at the same time, to mitigate the effect of baseline knowledge errors. Finally, a performance evaluation is presented for the Harmony Earth Explorer 10 candidate mission. A centimeter-level absolute accuracy and a submillimeter-level relative accuracy of the DTD estimation are achieved under the multistatic Harmony case when at least one companion satellite has an inter-satellite distance longer than 300 km to provide enough sensitivity.

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The paper investigates the polarimetry of bistatic Synthetic Aperture Radar (SAR) acquisitions over rough surfaces, with focus on the rotation of the scattered wave orientation at the companion antenna axes and on the optimal linear polarization in transmission. This latter is defined as the polarization achieving the maximum radar cross section and will be herewith recalled as principal polarization. The paper outlines a geometrical framework for the interpretation and the estimation of the principal polarizations. It is shown that the theoretical formulation provides a good agreement with the second-order analytical approach in [1]. The paper finally postulates that a bistatic illumination in the traditional H and V linear modes can be considered equivalent to a compact φ-pol, i.e. with the transmittion in a linear polarization rotated by φ. For long baselines, such those as those envisioned by the ESA Harmony EE10 candidate, and for steep incidence angles, an equivalent π/4-pol might be possible for rough surfaces.

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Deployment of Sentinel-1 (S1) satellite constellation carrying a $C$-band synthetic aperture radar (SAR) enables regular and timely monitoring of floods from their onset until returning to nonflooded (NF) conditions. The major constraint on using SAR for near-real-time (NRT) flood mapping has been the inability to rapidly process the obtained imagery into reliable flood maps. This study evaluates the efficacy of S1 time series for quantifying and characterizing inundation extents in vegetated environments. A novel algorithm based on statistical time-series modeling of flooded (F) and NF pixels is proposed for NRT flood monitoring. For each new available S1 image, the probability of temporarily F conditions is tested against that of NF conditions by means of likelihood ratio tests. The likelihoods for the two conditions are derived from early acquisitions in the time series. The algorithm calibration consists of adjusting two likelihood ratio thresholds to match the reference F area extent during a single flood season. The proposed algorithm is applied to the Caprivi region, the resulting maps were compared to cloud-free Landsat-8 (LS8) derived maps captured during two flood events. A good spatial agreement (85-87%) between LS8 and S1 flood maps was observed during the flood peak in both 2017 and 2018 seasons. Significant discrepancies were noted during the flood expansion and recession phases, mainly due to different sensitivities of the data sources to the emerging vegetation. Overall, the analysis shows that S1 can stand as an effective standalone or gap-filling alternative to optical imagery during a flood event.

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The paper introduces the principles and the technical elements supporting the so-called SwarmSAR concept, consisting in a close formation of simple nodes cooperating in a MIMO-like frame to boost their imaging flexibility and performance. The philosophy of the swarm consists in employing extremely basic but self-sufficient nodes, each one guaranteeing sufficient image quality even when used individually. The costs are hence diverted from the node to the formation launching and maintenance aspects. We promote in this paper the use of S-Band as a convenient frequency both for the single node and for the formation requirements and resourceful for applications. An outline of the envisioned cooperative illumination modes, including high resolution imaging and the interferometric modes, and a preliminary discussion on their expected performance and challenges is provided.

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Ambiguities in short-baseline ATI interferometry need to be treated not as noise that lowers the coherence, but as a source of bias. A mathematical formulation of the interferometric ambiguity model is given, and an approach to correct ambiguities is proposed and illustrated with simulation results.

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The work investigates staggered and random PRF (Pulse Repetition Frequency) strategies for a close formation of small Synthetic Aperture Radar (SAR) satellites operating in a multistatic configuration. The satellites are positioned within a fraction of the along-track critical baseline, hence allowing for the application of Displaced Phase Center image formation approaches. The performance of regular and random pulse sampling schemes is in particular assessed for a single-input multiple-output (SIMO) S-Band constellation, whose feasibility is further analyzed in relation to the number of satellites and their antenna size.

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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.@en

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.@en

Following publication, it was noticed that the horizontal brackets labelling the two groups of precisions present in Equation 7 are incorrectly rendered in the PDF version of this Data Descriptor. The correct Equation 7 is as follows: (Formula presented.) (Formula presented.) In addition, in the Biomass subsection of the Methods section in both the HTML and PDF versions, the term “ESUs” is incorrectly rendered as “ESU’s” and the term ESUBs is incorrectly rendered as “ESUB’s” Finally, throughout the manuscript, references to sections and subsections include the prefixes “sec:” and “subsec:”, respectively. These prefixes and any hyphen between the reference words that follow the prefixes can be ignored.

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In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.@en

In this study, we performed ground validation to support the interpretation of Sentinel-1 imagery during a full growing season of five key crop types in the Netherlands. Crop height and growth stage were monitored weekly in a total of 25 parcels of maize, potato, sugar beet maize and English rye grass in the province of Flevoland. Hydrometeorological data were collected throughout the season. Here, these results are used to interpret time series of Sentinel-1 data processed for the province of Flevoland. Results demonstrate that Sentinel-1 data follow the phenological stages and can be used to identify key moments in crop development. Combined with the guaranteed availability of observations regardless of cloud cover, this makes Sentinel-l data a valuable resource for agencies and commercial entities providing advice to farmers and agro-industrial co-operatives.@en

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.@en

Reliable and timely mapping of crop growth conditions and of their water resources is considered a prioritary application in light of the abrupt climate changes. With this view, the paper presents a novel approach that makes use of dense C-Band time-series for the timely estimation of crop growth stages and for the detection of changes in crop water conditions, especially related to precipitation and irrigation events. Aided by vegetation indexes extracted from Landsat and Sentinel-2 imagery, the proposed Sentinel-l centered method exploits both temporal patterns of crop growth and spatial patterns of water anomalies to enhance its classification robustness.@en

SAR as an active remote sensing technique is capable of providing insights into the physical features of agricultural vegetation. However, the noisy nature of SAR signals makes the direct conversion to effective productivity metrics challenging. This study sheds light on the effect of gaps present in a sugarcane field on L-band and C-band SAR signals and demonstrates the variability of this effect with changing spatial averaging windows, changing precipitation conditions and changing vegetation height.@en