Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images

The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP): Enabling Open Science for the Global Aboveground Terrestrial Carbon Dynamics Research Community

No abstract availabl

Investigations on the Effect of HD Processing in Land Cover Classification

The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

Development of an ESA Sentinel-1 Normalized Radar Backscatter product

The current family of Copernicus Sentinel-1 (European Space Agency ESA / European Commission) products primarily contains Level-1 Single Look Complex (SLC) and Ground Range Detected (GRD) products [1], which inherit their definition from the European heritage Synthetic Aperture Radar (SAR) satellite missions ERS-1, -2 and ENVISAT. These products have over the years proven to be reliable, high-quality data sources since the start of Sentinel-1A in 2014 and users largely benefit from the open and free data policy of the Copernicus program. This has led to Sentinel-1 products being routinely used in a wide range of operational applications as well as enlarging the user base of SAR data in general. However, the rapid increase of data volume is presenting a challenge to many users who want to exploit this wealth of information but lack the resources for the processing needed to convert these Level-1 products to interoperable geoinformation. Cloud exploitation offers opportunities for accelerated gain of knowledge but requires new strategies for data management and provision. The definition of Analysis Ready Data (ARD) addresses this challenge with the aim of homogenizing the data offerings from various sources whilst facilitating performant cloud access and high data quality. The Committee on Earth Observation Satellites (CEOS) defines a set of satellite ARD standards detailing higher level satellite data products [2] and thus contributes to a federated platform and data infrastructure. These standards are expected to be built upon in a new Open Geospatial Consortium (OGC) Standards Working Group on ARD, which is currently being formed. Specific to Synthetic Aperture Radar (SAR) data, three CEOS ARD product family specifications (PFS) currently exist: Normalised Radar Backscatter (NRB), Polarimetric Radar (POL), and Ocean Radar Backscatter (ORB). Of the three, NRB has been introduced first and one fully CEOS ARD certified product has already been made available [3]. Recently, ESA has defined its own prototype Sentinel-1 NRB product to pave the way for a potential contribution to the Copernicus data catalog [4]. The initial development was carried out in cooperation with the University of Jena and is now being continued at DLR. If feasible, this new NRB product might replace the existing GRD product, which would then be available only on demand. The CEOS ARD compliant NRB product consists of radiometrically terrain corrected backscatter data, several ancillary image layers referred to as per-pixel metadata in the CEOS ARD nomenclature, and extensive metadata in OGC EO XML [5] as well as STAC JSON format [6]. The latter is seen as particularly valuable for large scale cloud or High-Performance Computing (HPC) exploitation with respect to general data discovery as well as direct utilization in popular data cube related tools like xarray and dask. Here, we present the recent progress in the definition of the ESA Sentinel-1 NRB product, the evolution of the open-source prototype processor, and the extension of the existing product towards ORB. While NRB is expected to significantly increase the usability over land areas, the benefit of ORB over GRD for ocean use cases is currently still being investigated. Furthermore, it is of current interest in how far both products, NRB and ORB, can be distributed as one or whether they should be seen as two completely separate products. The results of ongoing experiments will be presented. Furthermore, a demonstration of the product content and its data cube capabilities will be given. Last but not least, the status of the systematic processing plans of DLR for this year will be presented, which will give insight into the overall data volume and processing effort to be expected for creating such a product. The experience gathered from this processing campaign will contribute to the refinement of the Sentinel-1 NRB product specification for consideration in the Copernicus data catalog

The ESA Sentinel-1 Normalized Radar Backscatter Product

The current family of Copernicus Sentinel-1 (European Space Agency / European Commission) products primarily contains Level-1 Single Look Complex (SLC) and Ground Range Detected (GRD) products [1], which inherit their definition from the European heritage Synthetic Aperture Radar (SAR) satellite missions ERS-1, -2 and ENVISAT. These products have over the years proven to be reliable high-quality data sources since the start of Sentinel-1A in 2014 and users largely benefit from the open and free data policy of the Copernicus program. This has led to Sentinel-1 products being routinely used in several operational applications as well as enlarging the user base of SAR data in general. However, the rapid increase of data volume is presenting a challenge to many users who still want to exploit this wealth of information but lack the resources for the processing needed to convert these Level-1 products to interoperable geoinformation. Cloud exploitation of data offers opportunities for accelerated gain of knowledge but requires new strategies of data management and provision. In this context, the term Analysis Ready Data (ARD) has been coined and several activities have indicated the potential of enlarging the Copernicus product family by such ARD products. However, no agreement has yet been reached on the definition of ARD as different user communities perform very different analyses and thus have different understandings of analysis readiness. With the aim to standardize different categories of ARD, the Committee on Earth Observation Satellites (CEOS) has set up the CEOS Analysis Ready Data for Land initiative (CARD4L) [2], which has recently been renamed to simply CEOS ARD (CARD) to accommodate ocean products. Several product specifications have been defined to provide guidelines on how to best process and organize data to serve as many use cases as possible with the respective products. One such product is Normalized Radar Backscatter (NRB) [3]. However, the CARD NRB specification can also be thought of as a guideline rather than a specification. It gives recommendations on what information should be included in a dataset but gives no concrete specifications on how this information should be created. For this reason, data providers are required to translate these guidelines into actual products with the software of choice to then reach out to CEOS for assessment of CARD compliance. Recently, ESA has defined its own prototype Sentinel-1 NRB product [4]. The aim is to be fully aligned with the CARD certification, offering a high-quality radiometrically terrain corrected SAR backscatter product with all relevant general metadata and per-pixel metadata (ancillary data layers). It is intended as a global and consistently processed product achieving the highest possible quality. It is designed to be complete to satisfy requirements of users with a prioritized focus on the Copernicus Services. Ultimately, it intends to significantly lower the effort for processing for the SAR user community. Core characteristics are the structuring into the Military Grid Reference System (MGRS) tile grid for interoperability with Sentinel-2 ARD products, storage in Cloud Optimized GeoTIFFs and the provision of Spatiotemporal Asset Catalogue (STAC) metadata. We present the characteristics of this S1-NRB product, give insight into current work towards extending the product to Ocean Radar Backscatter (ORB), and outline the DLR plans of globally generating the product on the terrabyte High Performance Data Analytics (HPDA) platform. These activities are expected to contribute to the further definition of a Copernicus Sentinel-1 NRB product. This type of product is now baseline in the frame of the Copernicus Expansion and Sentinel Next Generation and is hence planned for further future missions

Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images