Remote Sensing of Environment
Công bố khoa học tiêu biểu
* Dữ liệu chỉ mang tính chất tham khảo
Sắp xếp:
Bayesian methodology for inverting satellite ocean-color data
Remote Sensing of Environment - Tập 159 - Trang 332-360 - 2015
Seasonal dynamics of suspended-sediment plumes from the Tana and Sabaki Rivers, Kenya: Analysis of landsat imagery
Remote Sensing of Environment - Tập 16 - Trang 165-173 - 1984
Remote sensing of water quality in the Singapore-Johor-Riau growth triangle
Remote Sensing of Environment - Tập 43 - Trang 139-148 - 1993
Monitoring spring phenology in Mediterranean beech populations through in situ observation and Synthetic Aperture Radar methods
Remote Sensing of Environment - Tập 248 - Trang 111978 - 2020
Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner
Remote Sensing of Environment - Tập 114 - Trang 2229-2237 - 2010
A Semianalytical Approach to the Calibration of AVIRIS Data to Reflectance over Water
Remote Sensing of Environment - Tập 75 - Trang 335-349 - 2001
Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method
Remote Sensing of Environment - Tập 263 - Trang 112566 - 2021
A multi-angular invariant spectral index for the estimation of leaf water content across a wide range of plant species in different growth stages
Remote Sensing of Environment - Tập 253 - Trang 112230 - 2021
Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra
Remote Sensing of Environment - Tập 192 - Trang 58 - 2017
In this study, we investigated the utility of hyperspectral remote sensing data for estimating green percent vegetation cover (PVC) for a study site in the Canadian High Arctic. A field experiment was conducted on Sabine Peninsula (76°27′ N, 108°33′ W), Melville Island, Nunavut, Canada to collect field spectra and PVC for five vegetation types, i.e., polar semi-desert (PD), dry mesic tundra (DMT), mesic tundra (MT), wet mesic tundra (WMT) and wet sedge/moss (WSM). Based on field spectra, two types of 2-band hyperspectral (i.e., Hyperion) and multispectral (i.e., WorldView-3) vegetation indices (VIs) were derived using all possible band combinations. Optimal spectral bands were identified based on their correlations with green PVC. In addition, VIs designed for other landscapes were examined for their ability to estimate green PVC in an Arctic environment. The results indicate that PVC and spectral features for Arctic vegetation types were related to moisture content: (1) vegetation types with dry to intermediate soil moisture (e.g., PD, DMT and MT) possessed large amounts of bare soil and exhibited spectral properties similar to bare soil; and (2) vegetation types with high moisture content (e.g., WMT and WSM) exhibited spectra similar to senescent vegetation given the substantial proportion of senescent vegetation in these vegetation types. The optimal Hyperion spectral bands for estimating green PVC were located at the absorption features observed in Arctic vegetation spectra, including 681.20nm (leaf pigment absorption); 721.90nm and 732.07nm (along the red-edge slope); 1174.77nm and 1184.87nm (leaf water absorption); and 1447.14nm, 1457.23nm, 2072.65nm and 2102.94nm (leaf cellulose and lignin absorption). Narrowband VIs exhibited a stronger correlation with green PVC than broadband VIs due to the finer spectral features sampled by hyperspectral data. Further, VIs designed to estimate leaf pigment and dry matter content (e.g., lignin and cellulose) showed strong correlations with green PVC.
#Biophysical remote sensing #Vegetation indices #Spectral indices #NDVI #Field spectroscopy #Hyperion #WorldView-3
Detecting hotspots of interactions between vegetation greenness and terrestrial water storage using satellite observations
Remote Sensing of Environment - Tập 231 - Trang 111259 - 2019
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