We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented.
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http://dx.doi.org/10.1038/s41597-020-00657-1 | DOI Listing |
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119 China. Electronic address:
Non-optically active water quality parameters (NAWQPs) are essential for surface water quality assessments, although automated monitoring methods are time-consuming, include labor-intensive chemical pretreatment, and pose challenges for high spatiotemporal resolution monitoring. Advancements in spectroscopic techniques and machine learning may address these issues. We integrated ultraviolet-visible-near infrared absorption spectroscopy with physical-chemical measurements to predict total nitrogen (TN), dissolved oxygen (DO), and total phosphorus (TP) in the Yangtze River Basin, China.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 101407, China.
Rainwater harvesting systems (RHS) are extensively executed to manage stormwater control and water shortage issues in cities. However, the influences of rainfall characteristics on the performances of RHS are still not deeply explored. In this research, a methodology framework is developed to explore the influences of rainfall characteristics on stormwater control and water saving performances of RHS, by using daily precipitation data during 1968-2017 at 30 stations across the Beijing region as a testbed.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, Helsinki, FI-00014, Finland; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China.
The reliability of land surface phenology (LSP) derived from satellite remote sensing is crucial for obtaining accurate estimates of the phenological response of vegetation to future climate change in urban ecosystems. Differences in phenological definition and extraction methodology using remote sensing can generate systemic errors in estimating the phenological temperature sensitivity to predict the biological response of vegetation. Here, we evaluated the start of the season (SOS), the end of the season (EOS), and the growing season length (GSL) between the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) and the Suomi National Polar-Orbiting Partnership NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics (VNP22Q2) over 1470 urban clusters worldwide.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China.
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Faculty of Fine Arts, Design and Architecture Department of Landscape Architecture, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
Wetlands provide necessary ecosystem services, such as climate regulation and contribution to biodiversity at global and local scales, and they face spatial changes due to natural and anthropogenic factors. The degradation of the characteristic structure signals potential severe threats to biodiversity. This study aimed to monitor the long-term spatial changes of the Göksu Delta, a critical Ramsar site, using remote sensing techniques.
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