Deep learning models trained in a fully supervised way have shown encouraging capabilities for mapping forests with TanDEM-X interferometric data, being able to generate time-tagged forest maps at large-scale over tropical forests. These maps have been generated at 50 m resolution to reduce the computation burden. In this work, we now aim to exploit the high-resolution capabilities of the TanDEM-X interferometric dataset, processed at only 6 m resolution. In order to cope with the lack of reliable reference data at such high resolution, we focus on the investigation of self-supervised learning approaches. The availability of a reference map over Pennsylvania, USA, based on Lidar acquisitions at 1 m resolution, allows us to compare different deep learning approaches. First promising results show the possibility to extend the proposed self-supervised learning approach over areas where the lack of reference data prevent us from using fully supervised deep learning methods.

Highly accurate estimates of canopy height (CH) and above ground biomass (AGB) are key parameters for forest disturbance analysis, resource monitoring, and carbon flux analyses. In this work we present a deep learning-based approach for mapping CH and AGB on country-scales from single-baseline, single-polarization, single-pass TanDEM-X InSAR data. The proposed approach consists in a convolutional neural network (CNN), trained and validated on the five test-sites covered by the 2016 AfriSAR campaign. The resulting performance is in line or better than those of current state-of-the-art approaches. The framework is subsequently deployed on a large-scale to map the entire country of Gabon (in West Central Africa), showcasing the flexibility, scalability, and accuracy of our proposed approach for forest parameter estimation.

The TanDEM-X Forest/Non-Forest map, derived from the volume decorrelation factor using a supervised fuzzy clustering algorithm, represents the baseline approach for forest mapping with TanDEM-X data at global scale. Deep learning (DL) methods have been demonstrated to be also suitable for mapping forests at large scale with TanDEM-X interferometric data. In this work, we investigate the capabilities of using a U-Net-like architecture with TanDEM-X interferometric data for forest mapping at 6 m resolution. With such high-resolution data, we aim at improving the forest mapping accuracy and to be able to detect forest degradation over the Amazon rainforest caused e.g. by selective logging, fires and natural hazards. The classification improvements already observed applying DL methods on TanDEM-X data allow for the generation of large scale time-tagged mosaics. The explotation of such mosaics over extended areas is a key aspect for the detection and monitoring of forest dynamics worldwide.

Large-scale and up-to-date canopy height model (CHM) estimates are key to forest resources assessment and disturbance analysis. In this work we present an investigation of the potential of Deep Learning (DL) for the regression of forest height from TanDEM-X bistatic InSAR data. We propose a novel fully convolutional neural network (CNN) framework, trained and tested on four tropical sites in Gabon, Africa, together with a series of experiments for assessing the impact of different input features with specific focus on bistatic InSAR. The obtained results are extremely promising and already in line with state-of-the-art methods based on theoretical modelling, with the remarkable advantage of requiring only one single TanDEM-X acquisition at inference time.

In this work, we investigate the potential of SAR interferometry (InSAR) for mapping forests worldwide and retrieve important biophysical parameters, such as land cover and canopy height. We compare single-pass (bistatic) versus repeat-pass InSAR, discussing their main peculiarities and limitations. In particular, we concentrate on the analysis of the interferometric coherence and on the relationship between volume and temporal decorrelation with respect to forest parameters estimation. We present the work done at DLR for mapping forests worldwide at high spatial resolution using the TanDEM-X bistatic coherence, together with the potential of Sentinel-1 InSAR time-series for a regular monitoring of vegetated areas. We discuss the algorithms which are currently under development based on the latest advances in the field of artificial intelligence and, in particular, of deep learning, presenting the first promising results for a more effective exploitation of current EO datasets.

The TanDEM-X Forest/Non-Forest Map, derived from the volume decorrelation factor using a supervised fuzzy clustering algorithm, represents the baseline approach for forest mapping with TanDEM-X data at large/global-scale. Deep learning methods have been demonstrated to be also suitable for mapping forests with TanDEM-X interferometric data, e.g. by utilizing a U-Net convolutional neural network (CNN) on full-resolution images. In this work, we investigate the capabilities of using a U-Net-like architecture with TanDEM-X interferometric data for forest and water mapping on a large scale. An ad-hoc training strategy has been developed to detect forest and water on TanDEM-X images acquired with different acquisition geometries over the Amazon rainforest. In this case, a significant performance improvement with respect to the clustering approach, with a mean f1-score increase of 0.13 on test images has been measured with respect to the baseline clustering technique. The trained U-Net over the Amazon rainforest has been used to extend the forest and water mapping to other tropical forests over Africa and Asia. The classification improvements applying CNN methods on TanDEM-X data allow for the generation of time-tagged mosaics over the tropical forests by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017, skipping the weighted mosaicking of overlapping images used in the clustering approach for achieving a good final accuracy, as well as avoiding the use of external layers to filter out water surfaces. The explotation of such mosaics over extended areas is a key aspect for the detection and monitoring of deforested areas worldwide.

The estimation of forest parameters, such as canopy height model (CHM) and above ground biomass (AGB), is of ut-most importance for forest monitoring, carbon-cycle modelling, disturbance analysis, resource inventorying and natural disaster prevention. In this work, we profit from the most recent advancements in deep learning research to propose a convolutional neural network (CNN) architecture for frequent forest parameter estimation at large scale. Our technique consists of a fully convolutional, multi-modal framework, which works on a single set of complementary multi-spectral and interferometric SAR data, acquired by ESA's Sentinel-2 and DLR's TanDEM-X missions, respectively. The regression performance of our framework has been tested over four tropical forest test sites in Gabon, Africa. The estimation of CHM shows promising early results when compared to state-of-the-art methods and has the advantage of requiring only a single input image pair instead of a longer time-series, as commonly done for state-of-the-art model-based techniques.

This paper presents an approach for large-scale precise mapping of agricultural fields based on the analysis of Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project. The goal is to design a flexible and automatic processing chain able to perform mapping in massive data. Here we focus on precision agriculture products generation at country level. In particular, the Country of study is Italy and the application goal is precision agriculture of single crop fields. To achieve this goal, two macro challenges are considered: (i) download and pre-processing of S2 SITS, and (ii) multi-temporal (MT) fine characterization of agricultural fields. Both challenges are addressed in an automatic way by exploiting and/or updating state-of-the-art methodologies. Promising results have been obtained over years 2017 and 2018 for Italy.