Cloud penetrating Synthetic Aperture Radar (SAR) imagery has proven effective for tropical forest monitoring at national and pan-tropical scales. Current SAR-based disturbance detection methods rely on identifying decreased post-disturbance backscatter values as an indicator of forest disturbances. However, these methods suffer from a major shortcoming, as they show omission errors and delayed detections for some disturbance types (e.g., logging or fires). Here, post-disturbance debris or tree remnants result in stable SAR backscatter values similar to those of stable forest. Despite fairly stable backscatter values we hypothesize that different orientation and arrangement of tree remnants lead to an increased heterogeneity of adjacent disturbed pixels. Increased heterogeneity can be quantified by textural features. We assessed six Gray-Level Co-Occurrence Matrix (GLCM) textural features utilizing Sentinel-1 C-band SAR time series. We used a pixel-based probabilistic change detection algorithm to detect forest disturbances based on each GLCM feature and compared them against forest disturbances detected using only backscatter data. We further developed a method to combine both backscatter and GLCM features to detect forest disturbances. GLCM Sum Average (SAVG) performed best out of the tested GLCM features. Omission errors were reduced of up to 36% and the timeliness of detections was improved of up to 30 days by applying the combination method of backscatter and GLCM SAVG. Test sites characterized by large unfragmented disturbance patches (e.g., large-scale clearings, fires and mining) showed the greatest spatial and temporal improvement. A GLCM kernel size of 5 leads to the best trade-off of improving timeliness of detections and reducing omission errors while not introducing commission errors. The robustness of the developed method was verified for a variety of natural and human-induced forest disturbance types in the Amazon Biome. Our results show that combined SAR-based textural features and backscatter can overcome omission errors caused by post-disturbance tree remnants. Combining textural features and backscatter can support law enforcement activities by improving spatial and temporal accuracy of operational SAR-based disturbance monitoring and alerting systems.

How to cite: Balling, J., Herold, M., and Reiche, J.: The benefit of textural features for SAR-based tropical forest disturbance mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3434, https://doi.org/10.5194/egusphere-egu23-3434, 2023.

Photovoltaic (PV) and wind are currently the highest growing renewable energies according to the annual World Energy Outlook 2022. However, in the case of solar PV in Europe, this growth is mainly driven by utility-scale installations. Distributed residential generation has many benefits, such as relieving the electrical grid and an increase of self-sufficiency. One challenge of this topic is to accurately estimate the rooftop PV potential of different regions, in order to best allocate economic resources and regulate accordingly. Multiple approaches have been proposed in the past, such as infering from proxy variables like population density, automatically analyzing residential 3D point clouds or automatically analyzing satellite images. The latter has gained popularity in the recent years given the increased availability of satellite imagery and the improvement of Computer Vision methods. However, in research, the analysis of satellite imagery is impeded by the lack of transparency, reproducibility, and standardization of methods. Studies are heterogeneous, target different types of potential with redundant efforts, and are mostly not open source or using private datasets for training. This makes it challenging for users of various backgrounds to find and use the existing approaches.

For these reasons, this paper proposes a conceptual framework that describes and categorizes the tasks that need to be considered when estimating PV potential, thus creating a clear framework along which the contents of this research report can be classified. Addidionally the open source workflow PASSION is introduced, which integrates the assessment of geographical, technical and economic potentials of regions under consideration along with the calculation of surface areas, orientations and slopes of individual rooftop sections. It also includes the detection of obstacles and existing PV installations. It is based on a novel two-look approach, in which three independent models are deployed in parallel for the identification of rooftops, sections and superstructures. The three models show a mean Intersection Over Union (IoU) between classes of 0.847, 0.753 and 0.462 respectively, and more importantly show consistent results in non-selected real life samples.

How to cite: Pueblas, R., Weinand, J., Kuckertz, P., and Stolten, D.: PASSION: a workflow for the estimation of rooftop photovoltaic potential from satellite imagery., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8825, https://doi.org/10.5194/egusphere-egu23-8825, 2023.

Due to climate change, understanding the changes in the water cycle has become a pressing issue. It is increasingly important to study prolonged periods of intense precipitation or dry spells to better manage water supply, infrastructure and agriculture. However, obtaining fine-scale precipitation data is challenging due to the intermittent nature of rain in time and space. Ground-based instruments could have mismatches between different regions due to spatial distribution, calibration, and complex topography. On the other hand, space-borne observations have uncertainties in their retrieval algorithms. This study proposes to deal directly with microwave images from space remote sensing, as this type of data makes it possible to study the evolution of the atmospheric water cycle on a global scale and with a temporal coverage of several decades by avoiding the uncertainties from retrieval methods. In recent years, convolutional neural networks have shown promising capabilities in identifying cyclones and weather fronts in large labelled climate datasets. However, these models required large labelled datasets for training and testing. The present study aims to test unsupervised segmentation approaches of microwave images, which are thus segmented into different classes. Instead of focusing only on one aspect, for example, precipitation, the obtained classes contain many physical properties. This is due to the fact that microwave brightness temperatures contain essential information relative to the atmospheric water cycle that can be used to derive many products such as rain intensity, water vapour, cloud fraction, and sea surface temperature. The unsupervised segmentation model consists of blocks of fully convolutional networks serving as feature extractors. Without labels, pseudo-targets from the feature extractors are used to train the model. The performance of the model in terms of intra-class and inter-class distances is compared with those of simpler models such as Kmeans. A major challenge in the unsupervised approach is validating and interpreting the resulting classes. Most of the obtained cluster patterns provide geographically coherent regions whose mode of variability of geophysical quantities can be highlighted. The presented study will then explore how the different classes computed by the unsupervised methods can be labelled and how the properties of the said classes change through time and space.

How to cite: Sambath, V., Viltard, N., Barthès, L., Martini, A., and Mallet, C.: Unsupervised Segmentation Of Microwave Brightness Temperatures To Study The Changes In The Water Cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13610, https://doi.org/10.5194/egusphere-egu23-13610, 2023.

Riparian ecosystems are biodiversity hotspots and provide crucial services to human wellbeing. Currently, the knowledge of how riparian ecosystems respond to and in turn influence the variations of the environment remains considerably limited. As a first step toward filling the gap, this research aims to characterize the dynamics of riparian vegetation during the past several decades across multiple aquatic sites operated by the National Ecological Observatory Network (NEON) of the US. Specifically, it leverages high-resolution hyperspectral and lidar data collected by NEON’s airborne observational platform (AOP) surveys, the long-term records of satellite optical and radar imagery, and advanced data fusion and classification techniques to generate a time-series record of riparian vegetation on a seasonal-to-yearly basis. The maps derived will provide a new basis for understanding how riparian vegetation has changed across continental US, and for predicting how it is likely to change in the future. This work is sponsored by NSF’s Macrosystems Biology and NEON-Enabled Science (MSB-NES) Program (2021/9–2024/8), and the overarching goal of the project is to mechanistically link riparian vegetation dynamics to hydroclimate variations and assess the functional importance of riparian ecosystems to macrosystem fluxes of carbon and water.

How to cite: Jin, H. and Tai, X.: Spatiotemporal mapping of riparian vegetation through multi-sensor data fusion and deep learning techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17425, https://doi.org/10.5194/egusphere-egu23-17425, 2023.

The presence of clouds and aerosols in satellite imagery hamper their use to monitor, observe and analyze the Earth's surface. Multisensor fusion can alleviate this problem. The HISTARFM algorithm developed by Moreno-Martinez et al. (2020) can generate monthly gap-filled reflectance data at 30 m spatial resolution by blending Landsat (30 m pixel size every 16 days) and MODIS (500 m pixel size daily) data using a bias-aware Kalman filter. 

Cloud computing platforms such as Google Earth Engine (GEE) help us to efficiently process public data archives from different remote sensing data sources. Therefore, GEE allows us to adapt the HISTARFM algorithm to obtain gap-filled data at higher spatial resolution. To reduce the massive number of images involved in the process, the bias-aware Kalman filter blends the available and preprocessed HISTARFM monthly gap-filled reflectance (30 m pixel size every month) and Sentinel-2 (10 m pixel size at five days) data. The very high spatial gap-filled images provide reflectance information at feasible scales to obtain new products that improve decision-making activities in variable territories with complex topographies. Also, new derivative products (e.g. land cover maps, biophysical parameters, or phenological indicators) will provide the scientific community better understanding and monitoring of bio-geographical and ecoclimatic characteristics of the Earth.

Additionally, a reduction of the time resolution of the temporal series is manageable with this approach by linear interpolation producing five days of gap-filled reflectance Sentinel-2 data. The proposed approach shows promising preliminary results and provides gap-free reflectance Sentinel-2 images with their associated uncertainties. These results foster the development of improved near-real-time applications for crop and natural vegetation monitoring at continental scales.

How to cite: Izquierdo-Verdiguier, E., Moreno-Martínez, Á., Muñoz-Mari, J., Clinton, N., Vuolo, F., Atzberger, C., and Camps-Valls, G.: Enhanced and gap-free Sentinel-2 reflectance data at vast scales with GEE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8451, https://doi.org/10.5194/egusphere-egu23-8451, 2023.

Mapping of Land use and land cover (LULC) changes over time requires automated processes and has been investigated using various machine learning algorithms and, more recently, deep learning models for semantic classification. New applications of these models to different satellite data and areas are regularly published. However, studies on the transfer of these models to other data and study areas are rather scarce. In a previous study [1], we used multi-modal and –temporal Sentinel data for LULC classification using traditional and novel deep learning models. The data covered parts of the Amazon basin and was comprised of a twelve-month time series of radar imagery (Sentinel-1), combined with a singular multi-spectral image (Sentinel-2). All satellite images were captured throughout the year 2018. The label map (Collection 4) of the Amazon produced by the MapBiomas project [2] was used as training and test labels. Besides state-of-the-art models, we developed five variations of a deep learning model—DeepForest—which leverages on the multi-temporal and -modal aspect of the data. The best model variation (DF1c) reached an overall accuracy of 74.4% on the test data.

Currently we are investigating the transferability of these models to more recent data of the same region. The new dataset was processed in the same way as in the previous study. It comprises a Sentinel-1 time-series and a single Sentinel-2 images from 2020, with an updated version of the label map of the MapBiomas project (Collection 6). This posed some challenges, as the classification scheme changed and is not fully backwards compatible with the one used to train the DeepForest models. A test dataset was chosen in the state of Mato Grosso, as the satellite scenes cover most classes used in the classification scheme. However, this data exhibits some class imbalance, as two of the eleven classes are dominating the scene. All five DeepForest variations reached accuracies higher than 79% and thus generalize well on the major LULC classes. For comparison and to further improve our models, we currently retrain the models on the new, larger data set (114,376 training image tiles compared to 18,074). Preliminary results will be shown during the session.

References

• Cherif, E.; Hell, M.; Brandmeier, M. DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin. Remote Sensing 2022, 14, 5000, doi:10.3390/rs14195000.
• MapBiomas Brasil; Available online: https://mapbiomas.org/en

How to cite: Hell, M., Brandmeier, M., and Nüchter, A.: Transfer Learning for LULC Classification on multi-modal data in the Amazon Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15020, https://doi.org/10.5194/egusphere-egu23-15020, 2023.

Distinguishing trees on agricultural land from forests is essential for a better understanding of the relationship between forests and human farming activities. However, it is difficult to separate them with remote sensing imagery since they share similar canopy cover, especially on the edge of the amazon rain forest, which has a much-complicated agriculture pattern. Except for annual crops and pasture, there are also lots of agroforestry applications and shifting cultivation, which integrates many tree systems. And those tree systems are not well separated from the forest in the existing land cover map. Recent techniques allow for the mapping of single trees outside of forests, now we take the next step by identifying those diverse tree-involved systems in agricultural land. Here we aim to generate a robust, cost-efficient method to distinguish trees within agricultural land from the forest. We started our exploration from Peruvian Amazon, where the competition for land has increased in the last decades, causing possible adverse effects on livelihoods and ecosystem services. Deep learning models, data sampling, and fine-tuning strategies are tested and optimized with PlanetScope satellite imagery. Our research target is to provide a tool for separating tree systems in farmland from the forest. It can also be used as a base map to explore the dynamic of agriculture transition and its impact on livelihoods and ecosystem services. 

How to cite: Yang, W., Ortiz Gonzalo, D., Tong, X., Pierre Johannes Gominski, D., Brandt, M., Kariryaa, A., Reiner, F., and Fensholt, R.: Separating tree systems in agricultural lands from forests using Deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8916, https://doi.org/10.5194/egusphere-egu23-8916, 2023.

Obtaining Soil Moisture Content (SMC) over large scales is of key importance in several environmental and agricultural applications especially in the context of climate change and transition to digital farming. Remote sensing (RS) has a demonstrated capability in retrieving SMC over large areas with several operational products already available at different spatiotemporal resolutions. At the same time, cosmic-ray neutron sensing is a recently emerged approach in retrieving high temporal resolution SMC at intermediate spatial scales. The present study conducts an intercomparison between different RS-based soil moisture products, daily SMC retrievals from a cosmic-ray neutron sensor (CRNS) station and a network of in situ SoilNet wireless sensors installed at the Pinios Hydrologic Observatory ILTER site in central Greece for a time period of 2018-2019. The RS-based soil moisture products included herein are from NASA’s Soil Moisture Active Passive (SMAP) and Metop-A/B Advanced Scatterometer (ASCAT) satellite missions. The methodological workflow adopted includes standardized validation procedures employing a series of statistical measures to quantify the agreement between the different RS-based soil moisture products, CRNS-based SMC and the SoilNet ground truth data. Our study results contribute towards global efforts aiming at exploiting CRNS data in the context of soil moisture retrievals and their potential synergies with RS-based products. Furthermore, our findings provide valuable insights into assessing the capability of CRNS at retrieving more accurate SMC estimates at arid and semi-arid environments such as those found in the Mediterranean basin, while supporting also ongoing global validation efforts.

Keywords: Cosmic Ray Neutron Sensors; SMAP; ASCAT; SoilNet; Soil Moisture Content

How to cite: Detsikas, S. E., Petropoulos, G. P., Koutsias, N., Gasparatos, D., Pisinaras, V., Bogena, H., Wendland, F., Herrmann, F., and Panagopoulos, A.: A comparative study of SMAP and ASCAT satellite soil moisture products with cosmic-ray neutron sensing and in-situ data in a Mediterranean setting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5175, https://doi.org/10.5194/egusphere-egu23-5175, 2023.