Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Cloud cover assessment for operational crop monitoring systems in tropical areas.

Abstract: The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no signi?cant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles(UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information

Self-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil.

Abstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map

Using satellite, airborne laser scanning and socio-economic data in a machine learning framework for improved fire danger modelling in the Alps

The frequency and severity of wildfires in the Alpine region will likely increase due to climate change. Most fire danger forecasts currently adopted in this region are based on meteorological data, such as the Canadian Fire Weather Index (FWI). They are typically only available at relatively coarse spatial resolutions (up to ca. 1 km) and, therefore, are of limited use in mountain regions with complex topography. Other factors, such as vegetation type and structure and the role of humans causing ignitions, are typically not considered. We address this gap by presenting a novel, high-resolution, satellite-supported integrated forest fire danger system (IFDS) for Austria. For this purpose, we use radar and optical satellite data from the Copernicus Sentinel-1 and Sentinel-2 missions, airborne laser scanning (ALS), socio-economic data, and topographic properties next to meteorological data. Two independent methods were investigated: (i) an expert-based approach that allows combining various data layers with different weightings assigned by experts and (ii) a machine-learning approach. Here, we focus on the results of the machine learning approach for a study area covering the federal state of Styria in Austria (ca. 16 400 km²). We use several data layers computed within our study as predictors in random forest models. Moisture indicators and tree species maps were derived from satellite data from the Copernicus Earth observation programme. Vegetation structure parameters, solar potential and a digital surface model (DSM) were derived from ALS data. In addition to the remote sensing data, we used meteorological variables, fire weather indices (FWI) and socio-economic data. We trained the model using forest fire events from the Austrian fire database. The cross-validation showed that the best-performing model predicts high fire danger for most fire events (87%). By integrating all the information layers compared to a baseline model using only FWI, the overall accuracy improved from 68% to 87%. The feature importance showed that the vegetation structure parameters, tree species, socio-economic parameters and DSM are essential for the model in addition to the meteorological predictors. Using this data-driven approach allowed us to learn from past fire occurrences and improved the spatial representation of fire ignition drivers, their importance and interactions. Also, this method permitted the identification of areas with higher danger risk, typically located in the vicinity of densely populated settlements

Understanding central European forest practitioners' requirements for remote sensing-based information products: a questionnaire survey

Despite decades of development, the adoption of remote sensing-based information products in the forestry sector remains limited in central and southern Europe. This may partly be due to a mismatch between the developed remote sensing products and the needs of potential users. In this study, we present the results of a survey conducted with 355 forest practitioners from eight central and southern European countries. The survey aimed to identify practitioners’ technical requirements for four types of remote sensing-based information products: tree species, canopy height, wood volume/biomass, and forest disturbances. Respondents were asked about their preferences regarding thematic and spatial detail, the maximum acceptable error, and the temporal frequency of the information layers and for the main application fields of the information layers. The study also examined whether demographic variables of the participants including education, age, and professional background influenced these requirements. Preferences for spatial and thematic detail were found to be relatively diverse, whereas more consistent patterns emerged regarding error tolerances and temporal frequency demands. For example, the maximal acceptable error for detailed tree species maps was fluctuating between 5 and 15%, the maximal error of canopy height maps deemed acceptable by interviewees was between 1 and 3 m and the maximal acceptable error for biomass maps was between 5 and 20%. When comparing the demands of the practitioners with the current state-of-the-art in remote sensing, our results suggest that for some products, such as canopy height maps, existing remote sensing technologies and workflows can meet all practitioner demands. However, focusing on other information products, this is only partly the case: In our view, remotely sensed information on forest disturbances partially meets practitioner needs, while products related to tree species and wood volume/biomass currently fall short in terms of thematic detail and accuracy required by practitioners in central and southern Europe. The application fields for the information layers included well-expected tasks but the participants also named some quite unexpected “out-of-the-box” ideas of how to make use of remote sensing based information layers. These have so far been mostly ignored by the remote sensing community. With respect to the demographic groups, we found no statistically significant differences. While our results suggest that further technical innovation is still needed with respect to some information products to match the demands of the practitioners, it may also be questioned whether the demands are fully realistic. For example, it is well known that many traditional information products on which current forestry practices base on, do not reach the accuracy demanded by the practitioners. So, providing information products that are better than what practitioners are used to work with at the moment, may still create added value, even if the defined accuracy requirements are not matched. Our study contributes to our understanding of the alignment and misalignment between the technical requirements of forest practitioners and the capabilities of remote sensing-based information product

Remote sensing for improved forest fire danger estimation in the Alpine region

Wildfires increasingly threaten human health and infrastructure with consequences for forestry, agriculture, and biodiversity. Predictions show that climate change will likely increase the wildfire frequency and severity in the Alpine region. Providing high-quality data to estimate fire danger can improve resource planning of decision-makers and the timing and quality of early warnings for society. Forest fire danger forecasts are based on empirical or physical models which estimate the moisture levels of fuels as a function of weather conditions. These forecasts often use indices based on meteorological data, such as the Canadian Fire Weather Index (FWI). However, meteorological forecasts are typically only available at relatively coarse spatial resolutions (up to ca. 1 km), and therefore, of limited use in mountain regions with complex topography. Also, other factors, such as vegetation type and structural elements and the role of humans causing ignitions, are often not considered. Therefore, there is a need for an integrated wildfire danger assessment for Austria. The CONFIRM project, which started in December 2019 with funding from the Austrian Research Promotion Agency (FFG), tries to address this gap and develops a novel, high-resolution, and satellite-supported integrated forest fire danger system (IFDS) for Austria. For that purpose, radar and optical satellite data from the Copernicus Sentinel-1 and Sentinel-2 missions, airborne Laserscanning (ALS), socio-economic data, and topographic properties are used next to meteorological data. The project uses two independent methods: (i) an expert-based approach that allows a combination of various data layers with different weightings and (ii) a machine learning approach. Key stakeholders from national weather services, fire brigades, state forest administrations, and infrastructure providers are providing feedback on the prototype of the IFDS according to their needs and requirements. Here, we present the results of the machine learning approach for a study site covering the state of Styria (ca. 16 400 km ²). Several machine learning techniques have already proven suitable in similar studies (e.g. Random Forest and Maxent) are employed. We used satellite-derived moisture indicators and tree species classifications, ALS-derived vegetation structure parameters and irradiance, topographic and socio-economic data, and meteorological variables as input features to estimate fire danger. The predictors were trained using forest fire events from the Austrian forest fire database, which occurred between 2016 and 2021. The precision metrics used in the course of spatial cross-validation show that the best performing model manages to predict high fire danger for the majority of fire events