ALBERT

Description: Over the past 25 years, research groups worldwide have been actively studying soil reflectance across the visible, near-infrared, shortwave infrared, and thermal regions for chemometric purposes. These groups have generated numerous soil spectral libraries (SSLs) with varying coverage and using different sensors and protocols. However, merging or comparing SSLs is challenging due to factors related to instrumentation and measurement protocols. With the emergence of hyperspectral technology, there is a growing interest in utilizing SSLs for the quantitative assessment of soil properties on a global scale. P4005 "Standards and Protocols for Soil Spectroscopy" is a working group established under the IEEE-Standards Association that aims to harmonize SSLs by developing an agreed protocol. The final protocol release is expected in early 2024, enabling intercompatible, reliable, and reproducible soil spectral measurements and facilitating the development of a global SSL.

Description: Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.

Description: This study introduces the development of Spatially Upscaled Soil Spectral Libraries (SUSSL) approach to assess spectral disturbances caused by variations in surface conditions in remote sensing-based soil property prediction. The SUSSL incorporates realistic cropland reflectance scenarios using spectral modelling and aggregation techniques. By convoluting the spectral database to multispectral and hyperspectral satellite sensors, the sensitivity of spectral indices in retrieving undisturbed surface reflectance is evaluated. Preliminary findings indicate that the spectral disturbance effects significantly impact the accuracy of soil organic carbon (SOC) estimations, resulting in a noticeable loss compared to bare soil spectra. However, strict filtering criteria using spectral indices exhibit promise in enhancing SOC modelling performance, particularly for multispectral sensors. Hyperspectral sensors demonstrate higher baseline accuracies even in disturbed soil cases. This research highlights the importance of accounting for surface condition variations for reliable soil property mapping. Future work involves leveraging machine learning techniques on SUSSL data to improve prediction accuracy and spatial coverage of soil properties using Earth Observation data.

Description: The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

Description: The Environmental Mapping and Analysis Program (EnMAP) is a new spaceborne German hyperspectral satellite mission, whose primary goal is to generate accurate information on the state and evolution of the Earth´s ecosystems. The core themes of EnMAP are monitoring environmental changes, ecosystem responses to human activities, and management of natural resources such as soils and minerals. EnMAP started on 1st April 2022 and is now in operational phase since over six months, with strong expectations regarding data quality and impact on soil research. In this paper, we aim to demonstrate in a few case studies the observed current capabilities for EnMAP with regard to soil mapping based on different test sites and methodologies. Key soil properties could be derived and spatially mapped in agricultural test sites in semi-arid and temperate zones such as Soil Organic Carbon (SOC) content important for soil health and carbon sequestration, texture (clay content) important for soil fertility, and carbonate content. Additionally, we test different standard and state-of-the art methodologies, including new scenarios for time-series of hyperspectral remote sensing data for improved soil products.

Description: Oil productions can result in pore pressure drop in the reservoir, generating an increase in effective stress and leading to reservoir compaction. The compaction in the subsurface reservoir translates to the earth's surface, which is manifested as a loss of elevation (land subsidence), causing damages to oil production facilities and surface infrastructures. The Liaohe oilfield, located in Liaohe River Delta (LRD), northeast of China, is one of the most significant subsidence areas in China as a direct consequence of oil extraction from the reservoir. Previous studies carried out in this area assumed the oil production-induced displacement retrieved from Interferometric Synthetic Aperture Radar (InSAR) corresponds only to vertical deformation. In this work, for the first time, we proposed a method to retrieve the full three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west displacement components by combining the multiple-geometry InSAR line-of-sight (LOS) observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement. Two ascending and two descending datasets from Sentinel-1 satellite covering the area were processed by an InSAR time series analysis over the 2017 to 2021 period, providing consistent displacement rate maps and displacement time series in the LOS direction. Spatial local-scale land subsidence was found in several producing fields over the deltaic region, including Shuguang, Huanxiling, and Jinzhou oilfields. The 3D displacement decomposition was then conducted in Shuguang oilfield. The derived 3D displacement field exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50–60 mm/year moving towards the subsidence center. The retrieved 3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir.

Description: In this study, we investigated four different ferroan dolomite samples using in situ Raman spectroscopy and powder X-ray diffraction (XRD) at high pressures up to 48 GPa and at room temperature. Our results show that the transition from Dolomite-I (Dol-I) to Dolomite-II (Dol-II) occurs above 13-16 GPa, and the transition pressure depends on the composition of the solid solution. Compression above 32-35 GPa results in the appearance of the Dolomite-IIIc (Dol-IIIc) or Dolomite-IIIb (Dol-IIIb). In the high-pressure XRD study, we found that the XRD patterns of the Ca0.97(Mg0.77,Fe0.23Mn0.03)(CO3)2 (xFe = 0.23, Ank23) can be indexed as Dol-IIIc at 44 GPa, while the rhombohedral Dol-IIIb structure matches better with the XRD patterns of the xFe = 0.40 (Ank40) and 0.64 (Ank64) solid solutions. Additionally, in the Raman spectra of the Fe-richest sample (Ca0.99(Mg0.33Fe0.64Mn0.05) (CO3)2 (Ank64), we observed an abrupt frequency downshift of the CO3-stretching vibrations between 40 – 42 GPa, which may reflect a pressure-induced Fe2+ spin transition. We further investigated two samples with xFe= 0.19 (Ank19) and 0.23 (Ank23) at high pressure and high temperatures, up to at least 2600 K. The experiments revealed that the unquenchable Dol-IIIc structure could be a stable high-pressure/high-temperature polymorph in ferroan dolomite up to at least 2600 K.

Description: Spaceborne imaging spectroscopy is undergoing a rapid expansion with a new generation of missions in recent years. Following the Hyperion (2000) and HICO (2009) missions, new spaceborne imaging spectroscopy missions have recently started operating: DESIS (2018), PRISMA (2019), HISUI (2019) and more recently EnMAP (2022) and EMIT (2022). These missions face the common challenge of providing accurate spectral and radiometric results over a wide spectral range. This requires accurate instrument calibration and the validation of the results obtained. In this contribution, we provide an overview of the calibration and validation (CalVal) activities in the EnMAP mission, and we present the CalVal results that were obtained as part of the commissioning phase (April - October 2022).

Description: The purpose of the presented study is to evaluate the comprehensive impact of different land cover types on the temperature development in the entire Shandong Province by a 20-year-long (MODIS) LST time series from 2003 to 2022. To find out the primary influencing factors, methods such as the Pearson correlation, stepwise analysis, and best-subset selection were applied. The results revealed that the average temperatures had been rising in summer during day- and night-time by 2.32 °C and 1.22 °C, respectively and in winter during day- and night-time by 3.25 °C and 1.33 °C, whereby a significant contribution can be attributed to the period 2014-2022. Substantial variations in LSTs emerge between built-up and vegetated areas and landlocked and coastal regions. Moreover, we could identify a contribution of 0.089 °C, caused merely by the extension of built-up areas of 1.65% in the entire Shandong Province. Modeling the combined effects of further relevant variables/factors, the percentage of cropland area (crop-per) and the number of landscape patches (NPl) indicate considerable influence on the daytime temperature in the temporal domain.

Description: Transparent, accurate, comparable, and complete estimates of greenhouse gas emissions and removals are needed to support mitigation goals and performance assessments under the Paris Agreement. Here, we present a comparative analysis of the agriculture forestry and other land use (AFOLU) emission estimates from different datasets, including National Greenhouse Gas Inventories (NGHGIs), FAOSTAT, the BLUE, OSCAR, and Houghton (here after updated H&N2017) bookkeeping models; Emissions Database for Global Atmospheric Research (EDGAR); and the US Environmental Protection Agency (EPA). We disaggregate the fluxes for the forestry and other land use (FOLU) sector into forest land, deforestation, and other land uses (including non-forest land uses), while agricultural emissions are disaggregated according to the sources (i.e., livestock, croplands, rice cultivation, and agricultural fires). Considering different time periods (1990–1999, 2000–2010, and 2011–2018), we analyse the trend of the fluxes with a key focus on the tropical regions (i.e., Latin America, sub-Saharan Africa, and South and Southeast Asia). Three of the five data sources indicated a decline in the net emissions over the tropics over the period 1990–2018. The net FOLU emissions for the tropics varied with values of 5.47, 5.22, 4.28, 3.21, and 1.17 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs, respectively) over the recent period (2011–2018). Gross deforestation emissions over the same period were 5.87, 7.16, 5.48, 3.96, and 3.74 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). The net forestland sink was −1.97, −3.08, −2.09, −0.53, and −3.00 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). Continental analysis indicated that the differences between the data sources are much large in sub-Saharan Africa and South and Southeast Asia than in Latin America. Disagreements in the FOLU emission estimates are mainly explained by differences in the managed land areas and the processes considered (i.e., direct vs indirect effects of land use change, and gross vs net accounting for deforestation). Net agricultural emissions from cropland, livestock, and rice cultivation were more homogenous across the FAOSTAT, EDGAR, and EPA datasets, with all the data sources indicating an increase in the emissions over the tropics. However, there were notable differences in the emission from agricultural fires. This study highlights the importance of investing and improving data sources for key fluxes to achieve a more robust and transparent global stocktake.