Description: The scope of the Science Plan is to describe the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. Primarily, this document addresses scientists and funding institutions, but it may also be of interest to environmental stakeholders and governmental agencies. It is designed to be a living document that will be updated throughout the entire mission lifetime. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and impact on international programs as well as major environmental and societal challenges. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation, and synergies with other missions. Chapter 3 gives an overview of the major fields of application such as vegetation and forests, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document and an extended glossary of terms and abbreviations is available on the EnMAP website.

Description: Although the Csingle bondH chains of petroleum derivatives display unique absorption features in the short-wave infrared (SWIR), it is a challenge to identify plastics on terrestrial surfaces. The diverse reflectance spectra caused by chemically varying polymer types and their different kinds of brightness and transparencies, which are, moreover, influenced further by the respective surface backgrounds. This paper investigates the capability of WorldView-3 (WV-3) satellite data, characterized by a high spatial resolution and equipped with eight distinct and relatively narrow SWIR bands suitable for global monitoring of different types of plastic materials. To meet the objective, hyperspectral measurements and simulations were conducted in the laboratory and by aircraft campaigns, based on the JPL-ECOSTRESS, USGS, and inhouse hyperspectral libraries, all of which are convolved to the spectral response functions of the WV-3 system. Experiments further supported the analyses wherein different plastic materials were placed on different backgrounds, and scaled percentages of plastics per pixel were modeled to determine the minimum detectable fractions. To determine the detectability of plastics with various chemical and physical properties and different fractions against diverse backgrounds, a knowledge-based classifier was developed, the routines of which are based on diagnostic spectral features in the SWIR range. The classifier shows outstanding results on various background scenarios for lab experimental imagery as well as for airborne data and it is further able to mask non-plastic materials. Three clusters of plastic materials can clearly be identified, based on spectra and imagery: The first cluster identifies aliphatic compounds, comprising polyethylene (PE), polyvinylchloride (PVC), ethylene vinyl acetate copolymer (EVAC), polypropylene (PP), polyoxymethylene (POM), polymethyl methacrylate (PMMA), and polyamide (PA). The second and third clusters are diagnostic for aromatic hydrocarbons, including polyethylene terephthalate (PET), polystyrene (PS), polycarbonate (PC), and styrene-acrylonitrile (SAN), respectively separated from polybutylene adipate terephthalate (PBAT), acrylonitrile butadiene styrene (ABS), and polyurethane (PU). The robustness of the classifier is examined on the basis of simulated spectra derived from our HySimCaR model, which has been developed in-house. The model simulates radiation transfer by using virtual 3D scenarios and ray tracing, hence, enables the analysis of the influence of various factors, such as material brightness, transparency, and fractional coverage as well as different background materials. We validated our results by laboratory and simulated datasets and by tests using airborne data recorded at four distinct sites with different surface characteristics. The results of the classifier were further compared to results produced by another signature-based method, the spectral angle mapper (SAM) and a commonly used technique, the maximum likelihood estimation (MLE). Finally, we applied and successfully tested the classifier on WV-3 imagery of sites known for a high abundance of plastics in Almeria (Spain), Cairo (Egypt), and Accra, (Ghana, West Africa). Both airborne and WV-3 data were atmospherically corrected and transferred to “at-surface reflectances”. The results prove the combination of WV-3 data and the newly designed classifier to be an efficient and reliable approach to globally monitor and identify three clusters of plastic materials at various fractions on different backgrounds.

Description: Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system, we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue. The resulting model, the ‘Humidity calibrated Drought Condition Index’ (HcDCI) was applied for the years 2001 to 2019 in form of a case study to Weihai County, Shandong Province in East China. Design and development are based on a linear combination of the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Rainfall Condition Index (RCI) using multi-source satellite data to create a basic Drought Condition Index (DCI). VCI and TCI were derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data, while precipitation is taken from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) data. For reasons of accuracy, the decisive coefficients were determined by the relative humidity of soils at depth of 10–20 cm of particular areas collected by an agrometeorological ground station. The correlation between DCI and soil humidity was optimized with the factors of 0.53, 0.33, and 0.14 for VCI, TCI, and RCI, respectively. The model revealed, light agricultural droughts from 2003 to 2013 and in 2018, while more severe droughts occurred in 2001 and 2002, 2014–2017, and 2019. The droughts were most severe in January, March, and December, and our findings coincide with historical records. The average temperature during 2012–2019 is 1 °C higher than that during the period 2001–2011 and the average precipitation during 2014–2019 is 192.77 mm less than that during 2008–2013. The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities. The model thus, demonstrates its capability to reveal drought periods in detail, its transferability to other regions and its usefulness to take future measures.