Mapping, and monitoring crops. Cloud-computing will facilitate hyperspectral information analysis as
Mapping, and monitoring crops. Cloud-computing will facilitate hyperspectral data evaluation as new tools, algorithms, and datasets are incorporated within the cloud-computing platform. This study contributes in novel methods towards the advancement of hyperspectral data evaluation by comparing the new generation spaceborne hyperspectral DESIS data with old generation Hyperion data, through classification of agricultural crops utilizing 4 unique machine mastering algorithms on Google Earth Engine.Supplementary Supplies: The following are readily available on the net at https://www.mdpi.com/article/ 10.3390/rs13224704/s1, File S1: Supplementary Material for this Journal Article entitled “Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Understanding around the Cloud”. Author Contributions: Conceptualization, P.S.T.; Formal evaluation, I.A.; Methodology, I.A. and P.S.T.; Supervision, P.S.T.; Writing–original draft, I.A. and P.S.T. All authors have read and agreed for the published version of the manuscript. Funding: This study was funded by the USGS National Land Imaging (NLI) and Land Modify Science (LCS) applications of the Land ML-SA1 Protocol Resources Mission Area, the Core Science Systems (CSS) Mission Location, the USGS Mendenhall Postdoctoral Fellowship program, the waterSMART (Sustain and Handle America’s Sources for Tomorrow) project, the NASA MEaSUREs program (grant number NNH13AV82I) by means of Global Meals Security-support Analysis Information (GFSAD) project, plus the NASA HyspIRI (Hyperspectral Infrared Imager presently renamed as Surface Biology and Geology or SBG) mission (NNH10ZDA001N-HYSPIRI). We also appreciate hyperspectral imagery created available through USGS, NASA, and Teledyne Brown Engineering. The use of trade, product, or firm names is for descriptive purposes only and doesn’t constitute endorsement by the U.S. Government. Data Availability Statement: Numerous spectral libraries in GHISA (International Hyperspectral Imaging Spectral-libraries of Agricultural crops) are obtainable by means of the NASA and USGS LP DAAC (Land Processes Distributed Active Archive Center: https://lpdaac.usgs.gov/ (accessed on ten September 2021)). Further details on GHISA could be identified at the project web-site (www.usgs.gov/WGSC/ GHISA (accessed on ten September 2021)). For future releases of GHISA information, including those analyzed within this paper, appear for updates at www.usgs.gov/WGSC/GHISA (accessed on ten September 2021) and https://lpdaac.usgs.gov/ (accessed on 10 September 2021).Remote Sens. 2021, 13,20 ofAcknowledgments: The authors thank internal and external reviewers for their insights, which helped improve the manuscript. Conflicts of Interest: The authors declare no conflict of interest.
remote sensingArticleFactors Driving Changes in Vegetation in Mt. Qomolangma (Everest): Implications for the Management of Protected AreasBinghua Zhang 1,2 , Yili Zhang 1,2 , Zhaofeng Wang 1,two , Mingjun Ding 3 , Linshan Liu 1,2, , Lanhui Li four , Shicheng Li 5 , Qionghuan Liu 1,6 , Basanta Paudel 1,2 and Huamin Zhang 1,2Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; zhangbh.17b@PSB-603 site igsnrr.ac.cn (B.Z.); [email protected] (Y.Z.); [email protected] (Z.W.); [email protected] (Q.L.); [email protected] (B.P.); [email protected] (H.Z.) College of Sources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China Ke.