A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations

Fractional vegetation cover (FVC) is an important measure for the conservation, restoration and maintenance of biodiverse environments, giving the spatial patterns and distributions of photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation as well as bare soil (BS) in a given region. Using hyperspectral remote sensing observations from DESIS and EnMAP (Environmental Mapping and Analysis Program), we derive FVC for Camarena, Spain, a semi-arid region southwest of Madrid and an important test site for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and compare the results from both sensors. DESIS and EnMAP are both hyperspectral remote sensing instruments with spatial resolutions of 30 m but they differ in other key aspects. DESIS has a spectral range of 400-1000 nm and a maximum spectral resolution of 2.55 nm whilst EnMAP has a range of 400-2500 nm and a resolution of 6.5-10 nm. The SWIR bands of EnMAP make it far more useful for the derivation of FVC than DESIS due to characteristic absorption features above 1500 nm which help to disentangle NPV and BS spectra. Nevertheless, abundances can still be derived from the FVC processing, accepting that the RMSEs are higher for the DESIS results (13% for PV, 18% for NPV, 9% for BS) than for the EnMAP results (12% for PV, 14% for NPV, 4% for BS). The FVC processing of the DESIS and EnMAP images consists of three steps. After some pre-processing (band removal and smoothing), pure spectra are retrieved from the image using the spatial-spectral endmember extraction method developed by Rogge. This method creates a global set of endmembers from the image after the masking of pixels which are not vegetation or soil. Secondly, the extracted endmembers are classified with a Logistic Regression (for DESIS) or a Random Forest (for EnMAP) classifier which were trained from a spectral library containing 631 samples. Three classes are used for the classification: PV, NPV amd BS. Unmixing is the final stage which uses a MESMA approach where each pixel is considered to be a linear combination of one PV spectrum, one NPV spectrum and one BS spectrum from the labelled endmember library. The class abundance are the weights found in the linear unmixing and an extra shade component is considered. In this work, we will present FVC maps derived from EnMAP and DESIS of Camarena which is a semi-arid region covering approximately 75 km2 in the Province of Toledo, Spain, where the land is mainly used for rainfed agriculture. It has an undulating topography with vegetation growing on sloping areas that were either not considered good enough for farming or later abandoned. Since June 2019, 60 cloud free images were acquired by DESIS over the region and EnMAP has so far acquired 8 cloud free images in this area since launch in April 2022. Several EnMAP images in July-August 2022 coincide closely with a DESIS observation which will enable quantifiable comparisons to be made between the two sensors and allow for an evaluation of the results considering the different wavelength ranges of each sensor

Role of Hyperspectral imaging for Precision Agriculture Monitoring

In the modern era precision agriculture has started emerging as a new revolution. Remote sensing is generally regarded as one of the most important techniques for agricultural monitoring at multiple spatiotemporal scales. This has expanded from traditional systems such as imaging systems, agricultural monitoring, atmospheric science, geology and defense to a variety of newly developing laboratory-based measurements. The development of hyperspectral imaging systems has taken precision agriculture a step further. Because of the spectral range limit of multispectral imagery, the detection of minute changes in materials is significantly lacking, this shortcoming can be overcome by hyperspectral sensors and prove useful in many agricultural applications. Recently, various emerging platforms also popularized hyperspectral remote sensing technology, however, it comes with the complexity of data storage and processing. This article provides a detailed overview of hyperspectral remote sensing that can be used for better estimation in agricultural applications

Data Validation of the DLR Earth Sensing Imaging Spectrometer DESIS

Imaging spectrometry provides densely sampled and finely structured spectral information for each image pixel over large areas, enabling the characterization of materials on the Earth's surface by measuring and analyzing quantitative parameters allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The various DESIS data products available for users are described with the focus on specific processing steps. A summary of the data quality results are given. The product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements

Space-based remote imaging spectroscopy of the Aliso Canyon CH_4 superemitter

The Aliso Canyon gas storage facility near Porter Ranch, California, produced a large accidental CH_4 release from October 2015 to February 2016. The Hyperion imaging spectrometer on board the EO-1 satellite successfully detected this event, achieving the first orbital attribution of CH_4 to a single anthropogenic superemitter. Hyperion measured shortwave infrared signatures of CH_4 near 2.3 μm at 0.01 μm spectral resolution and 30 m spatial resolution. It detected the plume on three overpasses, mapping its magnitude and morphology. These orbital observations were consistent with measurements by airborne instruments. We evaluate Hyperion instrument performance, draw implications for future orbital instruments, and extrapolate the potential for a global survey of CH_4 superemitters

Preface: the environmental mapping and analysis program (EnMAP) mission: preparing for its scientific exploitation

Open access; distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) licenseThe imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrialandaquaticecosystems,examinethemultifacetedimpactsofhumanactivities,andsupport a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.Ye

Examining Students’ Perceptions of Indonesian High School Students on the Use of TikTok in Learning English

With the availability of mobile devices and Web 2.0 technologies, various networking apps have become essential to English language learning (ELL). TikTok, a global app similar to YouTube, WhatsApp, Instagram, and Twitter, is becoming increasingly popular. TikTok is a video-sharing app that allows users to produce and share content while discovering material from other users. TikTok has yet to be thoroughly researched for educational purposes. However, some research suggests that social media can help language learners. This study evaluated Islamic school students' perspectives on using TikTok to learn English to enhance four English skills. In this study, 55 high school students from an Islamic country participated in an online quantitative research survey. The information was gathered using a Google Forms questionnaire. According to the research, students were enthusiastic about utilizing TikTok as a visual aid in enhancing their English skills. Writing was discovered to be the least likely of the four English skills to develop using TikTok Apps. Besides, students also stated a high preference for watching native English channels rather than local channels when learning English. As a result, TikTok has the potential to affect pupils' English proficiency positively. As a result, students and teachers can mix social networking apps to enhance English teaching and learning. Further research may be performed to look at students' voices, in the future teachers' views, and an experimental classroom design