Evaluation of PolInSAR Observables for Crop-Type Mapping Using Bistatic TanDEM-X Data

The contribution of Polarimetric SAR Interferometry (PolInSAR) observables to crop-type classification is investigated in this letter. The focus is set on characteristic parameters of the Coherence Region (CoRe), i.e. the representation in the polar plot of the PolInSAR data. For this purpose, time series of dual-pol HH-VV single-pass TanDEM-X bistatic data acquired over an agricultural area in Spain are exploited. In the experiment, up to 13 different crop types are evaluated. Crop classification is performed by means of the well-known Random Forest algorithm. The retrieved accuracy metrics highlight the potential of the evaluated PolInSAR descriptors for this application. Some PolInSAR features have proven to be enough representative of the scene, such as the Trace Coherence, which yields a classification accuracy of 75% and 87% at pixel and field level, respectively, on its own. Using all the PolInSAR parameters jointly as input features, classification reaches around 90% and 94% accuracy at pixel and field level, respectively. However, there are some PolInSAR feature subsets, e.g. the coherence measured at the Pauli channels or the foci of the ellipse which represents the CoRe, which yield accuracy levels very close to these maxima. These results demonstrate the suitability of the PolInSAR parameters for crop-type classification. Results are further improved when both polarimetric and PolInSAR features are combined, reaching 94% and 96% accuracy at pixel and field level, respectively.This work was supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante [UAFPU20-08]

Combination of Time Series of L-, C- and X-Band SAR Images for Land Cover and Crop Classification

The availability of new Earth observation satellites operating radar sensors at different frequencies enables the combination of multiple dimensions of the data (time, frequency, polarimetry and interferometry) in many applications. Image classification is expected to benefit from the diversity of observation. This work illustrates classification experiments carried out with series of images acquired by ALOS-2 PALSAR (L-band), Sentinel-1 (C-band) and TanDEM-X (X-band) in two application domains: land cover classification and crop-type mapping. Their usage, both separately and in combination, serves to identify the complementarity of information. In this work we propose a new colour representation of the pair-wise class separability in the case of using three frequency bands, which help identify which bands (or combinations of them) provide the best performance. Results in terms of accuracy scores (overall and class-specific) show that the use of the three frequency bands always outperforms the individual bands and their pairs. In addition, for both land classification and crop-type mapping the accuracy of using coherence time series is lower than the one obtained with the intensity time series, but there is complementarity in terms of sensitivity when both coherence and intensity time series are used together. The classes which are most benefited at each particular case of study have been identified. Finally, a partial trade-off has been found between the use of multiple frequency bands and the length of the available time series.This work was supported in part by the European Space Agency under Contract 4000133590/20/NL/AS/hh, and in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development under Project PID2020-117303GB-C22

Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X).This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping

A set of six spotlight TerraSAR-X images acquired at HH and VV polarizations in 2009 over an agricultural site in Germany are employed to evaluate the potential contribution of polarimetric features derived from this copolar mode to crop-type mapping. Results show that the inclusion of the correlation between copolar channels in the set of input features of the classifier consistently improves the classification performance with respect to the use of only backscattering coefficients. An increase around 8%-10% in overall accuracy, depending on the experiment setup, is achieved. Both user and producer accuracies are improved for all crop types, being the most noticeable contribution for barley, oat, and sugar beet. Different sets of input features, as well as classification and evaluation strategies, are tested in order to assess the robustness of this contribution.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI), and in part by the European Funds for Regional Development (EFRD) under project TEC2017-85244-C2-1-P

Exploring TanDEM-X Interferometric Products for Crop-Type Mapping

The application of satellite single-pass interferometric data to crop-type mapping is demonstrated for the first time in this work. A set of nine TanDEM-X dual-pol pairs of images acquired during its science phase, from June to August 2015, is exploited for this purpose. An agricultural site located in Sevilla (Spain), composed of fields of 13 different crop species, is employed for validation. Sets of input features formed by polarimetric and interferometric observables are tested for crop classification, including single-pass coherence and repeat-pass coherence formed by consecutive images. The backscattering coefficient at HH and VV channels and the correlation between channels form the set of polarimetric features employed as a reference set upon which the added value of interferometric coherence is evaluated. The inclusion of single-pass coherence as feature improves by 2% the overall accuracy (OA) with respect to the reference case, reaching 92%. More importantly, in single-pol configurations OA increases by 10% for the HH channel and by 8% for the VV channel, reaching 87% and 88%, respectively. Repeat-pass coherence also improves the classification performance, but with final scores slightly worse than with single-pass coherence. However, it improves the individual performance of the backscattering coefficient by 6–7%. Furthermore, in products evaluated at field level the dual-pol repeat-pass coherence features provide the same score as single-pass coherence features (overall accuracy above 94%). Consequently, the contribution of interferometry, both single-pass and repeat-pass, to crop-type mapping is proved.This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P, and by the European Commission, H2020 Programme, under Project MOSES (Managing crOp water Saving with Enterprise Services)

Utility of CYP2D6 copy number variants as prognostic biomarker in localized anal squamous cell carcinoma

Background: Anal squamous cell carcinoma (ASCC) is an infrequent tumor whose treatment has not changed since the 1970s. The aim of this study is the identification of biomarkers allowing personalized treatments and improvement of therapeutic outcomes. Methods: Forty-six paraffin tumor samples from ASCC patients were analyzed by whole-exome sequencing. Copy number variants (CNVs) were identified and their relation to disease-free survival (DFS) was studied and validated in an independent retrospective cohort of 101 ASCC patients from the Multidisciplinary Spanish Digestive Cancer Group (GEMCAD). GEMCAD cohort proteomics allowed assessing the biological features of these tumors. Results: On the discovery cohort, the median age was 61 years old, 50% were males, stages I/II/III: 3 (7%)/16 (35%)/27 (58%), respectively, median DFS was 33 months, and overall survival was 45 months. Twenty-nine genes whose duplication was related to DFS were identified. The most representative was duplications of the CYP2D locus, including CYP2D6, CYP2D7P, and CYP2D8P genes. Patients with CYP2D6 CNV had worse DFS at 5 years than those with two CYP2D6 copies (21% vs. 84%; p <.0002, hazard ratio [HR], 5.8; 95% confidence interval [CI], 2.7–24.9). In the GEMCAD validation cohort, patients with CYP2D6 CNV also had worse DFS at 5 years (56% vs. 87%; p =.02, HR = 3.6; 95% CI, 1.1–5.7). Mitochondria and mitochondrial cell-cycle proteins were overexpressed in patients with CYP2D6 CNV. Conclusions: Tumor CYP2D6 CNV identified patients with a significantly worse DFS at 5 years among localized ASCC patients treated with 5-fluorouracil, mitomycin C, and radiotherapy. Proteomics pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets for these high-risk patients. Plain Language Summary: Anal squamous cell carcinoma is an infrequent tumor whose treatment has not been changed since the 1970s. However, disease-free survival in late staged tumors is between 40% and 70%. The presence of an alteration in the number of copies of CYP2D6 gene is a biomarker of worse disease-free survival. The analysis of the proteins in these high-risk patients pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets. Therefore, the determination of the number of copies of CYP2D6 allows the identification of anal squamous carcinoma patients with a high-risk of relapse that could be redirected to a clinical trial. Additionally, this study may be useful to suggest new treatment strategies to increase current therapy efficacyIdiPAZ, Grant/Award Number: Jesús Antolín Garciarena Fellowship; European Proteomics Infrastructure Consortium, Grant/Award Number: 823839, Horizon 2020 Programm

Contribution of New Types of Radar Data to Land Cover and Crop Classification in Remote Sensing

For some time now, there has been a growing awareness in society about climate change, pollution, energy and the use of natural resources. This thinking has permeated society, mainly because the extreme natural phenomena that we are experiencing nowadays are no longer outliers in our time series of meteorological records. In this regard, it has been proven that the actual high temperatures are not only unparalleled, but also consistent around the globe which is something that had not happened until now (Neukom et al., 2019). The XX century was a turning point when it comes to the increase of the landuse for crops. In a context where the population doubled, the crop production for food from 1960 to 2010 tripled, helping to reduce the hungry population. When the world’s population is expected to continue to grow up to 9 billion people (Goodfray et al., 2010) by middle XXI century, it is essential to provide ourselves with the necessary tools to maximise crop production by taking advantage of all the resources available under a sustainable point of view. Under this context, all farmers in the European Union (EU) have the possibility to benefit from the Common Agricultural Policy (CAP), which came into force in 1960. The CAP is responsible for the financing of aid to farmers on a cross-compliance basis, based on the declaration of crop types. Traditionally, the authorities have checked the veracity of declarations in person through field inspections, which is clearly a highly inefficient, impractical and very expensive system. However, in 2018 the European Commission drafted an amendment to the CAP (European Commission, 2018), to be implemented in 2020, recommending the establishment of newprocedures for checking declarations, including the use of satellite data from the Copernicus programme or other new technologies. Among the various satellite technologies, Synthetic Aperture Radar (SAR) (Brown,1967; Curlander and McDonough, 1992) has proven the most reliable,as the images are acquired with a constant pass period and they are not subject to cloud problems (as is the case with sensors working in the optical domain) and information can be acquired both day and night. They are based in a SAR microwave sensor installed on a satellite platform with a forward trajectory which offers side-looking imaging geometries. Working in a range between 300 MHz and 30 GHz, the SAR sensor is in charge of emitting electromagnetic pulses and receiving the resulting echoes from the imaged target, which can help retrieve information about its dielectric properties, geometry, orientation, shape, and its behaviour along time. For a given target, the SAR backscattering response σ0 is function of many parameters (Lee and Pottier, 2017; Dobson et al., 1985): wave frequency, polarisation, imaging configuration, roughness, geometrical structure and dielectric properties. This makes the information extraction a major problem, as identical radar responses from two different targets may lead to the same result. To cope with this problem, the main techniques are based on extending the observation space by working with the full diversity of data. Thus, the main axes of SAR data are: • Time • Polarimetry • Interferometry • Frequency. Time series of radar data constitutes a major source of information for the classification of crops and land cover, since it makes it possible to distinguish between classes by their temporal behaviour: some land covers show a uniform response along time (e.g. urban areas), whereas there are others subject to seasonal changes (e.g. crops). It may happen that different crop species give the same radar response at a given time, however, when the time window becomes larger, and consecutive acquisitions are taken over a shorter time span, the more one can detect abrupt changes in the target over a longer time interval. Polarimetry is sensitive to the shape, orientation and the scattering mechanisms of the scatterers (Boerner et al.,1981; Zyl, Zebker, and Elachi, 1987). In that sense, when using different polarisations it is possible to discern better the true nature of the target, as some features may be visible in one polarisation but not in the others. Regarding multi-spectral data, it also constitutes a major source of information which can be exploited for classification purposes. Working with sensors operating at different frequencies, or wavelengths, provides diversity in the size of the elements of the scene to which the radar is sensitive as the radar backscattering will come from elements the size of the wavelength used it. For all of the above, multifrequency data provide complementary information, as each frequency operates and interacts with elements of the same wavelength or longer, and being transparent to all others. In addition, different bands are also associated with different spatial resolutions, so a high-frequency sensor can complement the classification performance of a low-frequency sensor when there are sufficiently small details in the scene that cannot be appreciated with the spatial resolution available at the lower frequency. From all the 4 axes exposed above, Interferometry (Graham, 1974) is without a doubt the least exploited for classification purposes. While polarimetry is sensitive to the scattering mechanisms of the scene by means of the polarisation information, interferometry adds the third dimension by being sensitive to the spatial distribution of the scatterers (Treuhaft et al., 1996). Coherence and phase difference computed between two complex-valued SAR images are the main descriptors of interferometry (Bamler and Hartl, 1998), and together, can be used to derive topographic information, vegetation structure, and deformation (volcanoes, landslides, etc.). For this reason, interferometry is especially suited for classification of covers in which there is vertical distribution of elements, e.g. urban areas and vegetation (forests and crops). Polarimetric interferometric SAR (PolInSAR) (Cloude and Papathanassiou, 1998; Treuhaft and Cloude, 1999), constitutes the next step forward, and is based on the application of interferometry to all polarisation channels. Polarimetry can identify the different scattering mechanisms in the scene by using the polarisation information, whilst interferometry is able to locate the effective scattering phase centres, which are mainly dependent on frequency, the polarisation employed, the physical, geometrical structure and orientation of the scatterer. By using the combination of both we can retrieve the vertical structure of the scene, which shows a great potential for classification purposes, since classes characterised by similar backscattering or polarimetric responses can be separated if their heights are different (e.g. types of buildings, forests, crops, etc.), whereas classes with similar heights, and hence similar interferometric coherence values (e.g. grass, crops, bare soil, etc.) can be resolved using their polarimetric response. In summary, PolInSAR-based classification is attractive since polarimetric ambiguities are resolved by interferometric information and vice-versa. The lack of exploitation of the 4 data axes in the literature, plus the arrival of a new generation of SAR sensors in the near future such as ROSE-L, BIOMASS and NISAR among others, offers a new range of possibilities in terms of new types of features for classification whose results and impact must be analysed. In this context, there are many types of SAR data (i.e. features) that have not been used yet, acquired from different sensors (Sentinel-1, PAZ, TanDEMX, TerraSAR-X and ALOS-2), and whose diversity axes, either used individually or jointly, have not yet been explored for classification applications. Therefore, the exploration of these new types of SAR data, whose contribution to classification is unknown regarding crop-type mapping, is the main objective of this doctoral thesis, and consequently also its main novelty. Based on the current state of the art of the research topic the main objective of this PhD thesis is to explore the added value of new SAR features, and their potential, alone or used together, for crop type and land cover classification. In the end, several experiments will be carried out, in different test sites, in which the proposed new features will be evaluated and compared with the traditional observables used so far, with the aim of evaluating their internal potential in classification applications.Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante UAFPU20-08

Exploring TanDEM-X Interferometric Products for Crop-Type Mapping

The application of satellite single-pass interferometric data to crop-type mapping is demonstrated for the first time in this work. A set of nine TanDEM-X dual-pol pairs of images acquired during its science phase, from June to August 2015, is exploited for this purpose. An agricultural site located in Sevilla (Spain), composed of fields of 13 different crop species, is employed for validation. Sets of input features formed by polarimetric and interferometric observables are tested for crop classification, including single-pass coherence and repeat-pass coherence formed by consecutive images. The backscattering coefficient at HH and VV channels and the correlation between channels form the set of polarimetric features employed as a reference set upon which the added value of interferometric coherence is evaluated. The inclusion of single-pass coherence as feature improves by 2% the overall accuracy (OA) with respect to the reference case, reaching 92%. More importantly, in single-pol configurations OA increases by 10% for the HH channel and by 8% for the VV channel, reaching 87% and 88%, respectively. Repeat-pass coherence also improves the classification performance, but with final scores slightly worse than with single-pass coherence. However, it improves the individual performance of the backscattering coefficient by 6&ndash;7%. Furthermore, in products evaluated at field level the dual-pol repeat-pass coherence features provide the same score as single-pass coherence features (overall accuracy above 94%). Consequently, the contribution of interferometry, both single-pass and repeat-pass, to crop-type mapping is proved

Contribution of PolInSAR Parameters of the Coherence Region to Crop Classification with Bistatic TanDEM-X Data

This study investigates the contribution of PolInSAR features, which correspond to characteristic parameters of the coherence region, in crop-type classification based on single-pass TanDEM-X bistatic data. Crop classification is carried out by means of a random forest classifier over an agricultural area in Sevilla, Spain. Accuracy metrics demonstrate the suitability of the PolInSAR observables for this purpose. A maximum accuracy of 90% and 94% at pixel and field level is obtained, respectively, when all the evaluated PolInSAR parameters are employed jointly as input features. This demonstrates their sensitivity to the crop structure, which is relevant for classification purposes