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In this mixed methods study, researchers explored students' perceptions of different types of syllabi, the course, and the instructor articulated through the syllabi. Students were randomly assigned to read one of two US History syllabi: a content-focused syllabus (CFS), characterized as a traditional, content-focused, policy-laden syllabus; or a learning-focused syllabus (LFS), characterized by strong learning objectives, authentic assessments, and a positive, motivating tone. Results show that LFS participants (n=61) had significantly more positive perceptions of the document, the course, and the instructor described by the document than CFS participants (n=66). LFS participants found, for example, more of the syllabus components to be useful, anticipated more student involvement in class, expected to learn more useful concepts and skills, and anticipated that the instructor would help them be successful. Although additional research is needed to determine generalizability of these results, we conclude that instructors have little to lose and much to gain by creating a learning-focused syllabus.

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany's Deutsches Zentrum für Luft- und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy's PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer's accuracies of 94% to 100%, and user's accuracies of 95% to 100%. [ABSTRACT FROM AUTHOR]

This article introduces a special issue on hyperspectral remote sensing, which involves advanced satellite sensors that provide data in three categories: hyperspatial, superspectral, and hyperspectral. These data types offer higher resolutions but also present challenges in handling and analysis. The special issue aims to explore the characteristics and limitations of hyperspectral data and their opportunities for advancing remote sensing science. The selected papers in the issue cover various applications and use advanced techniques to provide valuable knowledge in the field. The document discusses three research papers related to remote sensing and hyperspectral data, focusing on mapping building shadows, identifying asphalt pavement aging, and separating wheat crops from similar crops. It emphasizes the potential of hyperspectral data in advancing remote sensing science while acknowledging the challenges associated with data analysis and calibration. [Extracted from the article]

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany's Deutsches Zentrum für Luft- und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy's PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer's accuracies of 94% to 100%, and user's accuracies of 95% to 100%. [ABSTRACT FROM AUTHOR]

Essentially, the syllabus is a physical artifact outlining key structural elements of a course. It often serves contractual, record keeping, and/or communication functions. It is the place where faculty describe what content they will cover, what books and articles their students will read, the assignments they will complete, dates when things are due, and all the policies and rules that are supposed to keep everyone happy and out of trouble. The authors argue that the syllabus' primary function should be as a learning tool, one that is carefully crafted through a systematic course design process. The results of this study suggest that not only does the syllabus matter, the type of syllabus matters. When students read a learning-focused syllabus, they have significantly more positive perceptions of the document itself, the course described by the syllabus, and the instructor associated with the course.

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world's three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45 kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67 kg/m³) or 20–30° (0.94 kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75 kg/m³), medium (>0.75 to <1.10 kg/m³), and high CWP (≥1.10 kg/m³). For corn the global CWP categories were low (≤1.25 kg/m³), medium (>1.25 to ≤1.75 kg/m³), and high (>1.75 kg/m³). For rice the global CWP categories were low (≤0.70 kg/m³), medium (>0.70 to ≤1.25 kg/m³), and high (>1.25 kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India's corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100 m³ in volume that leads to many benefits such as acting as 'water banks' for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%. [ABSTRACT FROM AUTHOR]

Remote sensing can potentially be used to monitor the extent and distribution of invasive species across landscapes and regions, thus aiding conservation efforts. We collected ground-level hyperspectral data of six exotic invasive plant species in abandoned agricultural fields at the Blandy Experimental Farm in northern Virginia to determine the degree to which species could be identified using visible and near-infrared wavelengths. The spectral profile from 350 to 1025 nm was used in support vector machine analysis to determine separability of these species. We used sensitivity analyses to determine which spectral regions were most influential to identifying species by removing 50 nm regions and comparing species identification to that using the full spectral profile.Ailanthus altissima,Carduus acanthoides, andCirsium arvensehad high ability to be identified (75%, 87.5%, and 75%, respectively).Galium verumhad low ability to be identified (44.4%), perhaps due to high spectral contamination from soil.Celastrus orbiculatusandRhamnus davuricahad low ability to be identified (27.3% and 30.8%, respectively); however, they were often misclassified as each other, due to their physical overlap in the field. The sensitivity analysis revealed that the 350–399, 500–549, 700–749, and 900–949 nm regions were most useful for species identification, while 550–599 and 650–699 nm regions were detrimental, perhaps due to greater intraspecific variability than interspecific variability in these regions. These most influential regions for identification were similar to those found in other studies. Thus, it is possible to identify species using ground-level hyperspectral data. [ABSTRACT FROM PUBLISHER]