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.

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: 1) land cover classification and 2) crop-type mapping. Their usage, both separately and in combination, serves to identify the complementarity of information. In this work, we propose a new color representation of the pairwise 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 tradeoff has been found between the use of multiple frequency bands and the length of the available time series.

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.

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).