遙感衛(wèi)星

·編者按·

遙感衛(wèi)星，是一種利用衛(wèi)星上所裝載的遙感器對(duì)地球表面和低層大氣進(jìn)行光學(xué)或電子探測(cè)以獲取有關(guān)信息的應(yīng)用衛(wèi)星。用衛(wèi)星作為平臺(tái)的遙感技術(shù)稱為衛(wèi)星遙感。

國(guó)際遙感衛(wèi)星正向高空間分辨率、高時(shí)間分辨率、高光譜分辨率、高機(jī)動(dòng)能力和高集成化等方向發(fā)展。其中，高分辨率遙感衛(wèi)星是空間技術(shù)發(fā)展的一個(gè)重要方向，具有十分重要的商業(yè)價(jià)值與軍事意義，對(duì)地觀測(cè)衛(wèi)星地面分辨率不斷提高，目前已發(fā)展到0.5 m以內(nèi)。

按照開(kāi)發(fā)主體的不同，遙感衛(wèi)星可以分為軍用遙感衛(wèi)星、民用遙感衛(wèi)星和商用遙感衛(wèi)星。其中民用遙感衛(wèi)星主要指不以營(yíng)利為目的，主要服務(wù)于國(guó)家政府部門(mén)、公眾業(yè)務(wù)等方面的遙感衛(wèi)星，以國(guó)家投資為主；商用遙感衛(wèi)星主要指以營(yíng)利為目的，廣泛應(yīng)用于商業(yè)市場(chǎng)的遙感衛(wèi)星，以市場(chǎng)公司投資為主。按照應(yīng)用領(lǐng)域的不同，遙感衛(wèi)星可以分為陸地衛(wèi)星、海洋衛(wèi)星和氣象衛(wèi)星。

隨著衛(wèi)星數(shù)據(jù)在各領(lǐng)域的應(yīng)用越來(lái)越廣，國(guó)內(nèi)外遙感衛(wèi)星的數(shù)量也越來(lái)越多。這些衛(wèi)星主要應(yīng)用于國(guó)土資源勘查、環(huán)境監(jiān)測(cè)與保護(hù)、城市規(guī)劃、農(nóng)作物估產(chǎn)、防災(zāi)減災(zāi)和空間科學(xué)試驗(yàn)等領(lǐng)域。

本專題得到張永軍教授（武漢大學(xué)）、黃鵬高級(jí)工程師（中國(guó)科學(xué)院遙感與數(shù)字地球研究所）、焦偉利副研究員（中國(guó)科學(xué)院遙感與數(shù)字地球研究所）的大力支持。

·熱點(diǎn)數(shù)據(jù)排行·

截至2017年10月13日，中國(guó)知網(wǎng)（CNKI）和Web of Science（WOS）的數(shù)據(jù)報(bào)告顯示，以“遙感衛(wèi)星”等為詞條可以檢索到的期刊文獻(xiàn)分別為2483、4090條，本專題將相關(guān)數(shù)據(jù)按照：研究機(jī)構(gòu)發(fā)文數(shù)、作者發(fā)文數(shù)、期刊發(fā)文數(shù)、被引用頻次進(jìn)行排行，結(jié)果如下。

研究機(jī)構(gòu)發(fā)文數(shù)量排名（CNKI）

研究機(jī)構(gòu)發(fā)文數(shù)量排名（WOS）

作者發(fā)文數(shù)量排名（CNKI）

作者發(fā)文數(shù)量排名（WOS）

期刊發(fā)文數(shù)量排名（CNKI）

期刊發(fā)文數(shù)量排名（WOS）

根據(jù)中國(guó)知網(wǎng)（CNKI）數(shù)據(jù)報(bào)告，以“遙感衛(wèi)星”等為詞條可以檢索到的高被引論文排行結(jié)果如下。

國(guó)內(nèi)數(shù)據(jù)庫(kù)高被引論文排行

（續(xù)表）

根據(jù)Web of Science統(tǒng)計(jì)數(shù)據(jù)，以“遙感衛(wèi)星”等為詞條可以檢索到的高被引論文排行結(jié)果如下。

國(guó)外數(shù)據(jù)庫(kù)高被引論文排行

·經(jīng)典文獻(xiàn)推薦·

基于 Web of Science檢索結(jié)果，利用 Histcite軟件選取 LCS（Local Citation Score，本地引用次數(shù)）TOP30文獻(xiàn)作為節(jié)點(diǎn)進(jìn)行分析，得到本領(lǐng)域推薦的經(jīng)典文獻(xiàn)如下。

本領(lǐng)域經(jīng)典文獻(xiàn)

來(lái)源出版物：Biosystems Engineering,2013,114(4):358-371

Global estimates of evapotranspiration for climate studiesusing multi-sensor remote sensing data:Evaluation of threeprocess-based approaches

Vinukollu, RK; Wood, EF; Ferguson, CR; et al.

Abstract:Three process based models are used to estimate terrestrial heat fluxes and evapotranspiration (ET)at theglobal scale: a single source energy budget model, a Penman–Monteith based approach, and a Priestley–Taylor based approach. All models adjust the surface resistances or provide ecophysiological constraints to account for changing environmental factors. Evaporation(or sublimation) over snow-covered regions is calculated consistently for all models using a modified Penman equation. Instantaneous fluxes of latent heat computed at the time of satellite overpass are linearly scaled to the equivalent daily evapotranspiration using the computed evaporative fraction and the day-time net radiation. A constant fraction (10% of daytime evaporation) is used to account for the night time evaporation. Interception losses are computed using a simple water budget model. We produce daily evapotranspiration and sensible heat flux for theglobal land surface at 5km spatial resolution for the period2003–2006. With the exception of wind and surface pressure, all model inputs and forcings are obtained from satellite remote sensing. Satellite-based inputs and model outputs were first carefully evaluated at the site scale on a monthly-mean basis, then as a four-year mean against a climatological estimate of ET over26 major basins, and finally in terms of a latitudinal profile on an annual basis.Intercomparison of the monthly model estimates of latent and sensible heat fluxes with12 eddy-covariance towers across the U.S. yielded mean correlation of 0.57 and 0.54,respectively. Satellite-based meteorological datasets of2 m temperature (0.83), humidity (0.70), incident shortwave radiation (0.64), incident longwave radiation (0.67) were found to agree well at the tower scale, while estimates of wind speed correlated poorly (0.17). Comparisons of the four year mean annual ET for26global river basins andglobal latitudinal profiles with a climatologically estimated ET resulted in a Kendall’sτ＞ 0.70. The seasonal cycle over the continents is well represented in the Hovm?eller plots and the suppression of ET during major droughts in Europe, Australia and the Amazon are well picked up. This study provides the first ever moderate resolution estimates of ET on aglobal scale using only remote sensing based inputs and forcings, and furthermore the first ever multi-model comparison of process-based remote sensing estimates using the same inputs.

來(lái)源出版物：Remote Sensing of Environment,2011,115(3):801-823

SMOS radio frequency interference scenario:Status and actions taken to improve the RFI environment in the1400–1427-MHz passive band

Oliva, R; Daganzo-Eusebio, E; Kerr, YH; et al.

Abstract:The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by radio frequency interferences (RFIs) that jeopardize part of its scientific retrieval in certain areas of the world,particularly over continental areas in Europe, Southern Asia, and the Middle East. Areas affected by RFI might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put strategies in place that,one year after launch, have already improved the RFI situation in Europe where half of the sources have been successfully localized and switched off.

來(lái)源出版物：IEEE Transactions ongeoscience and Remote Sensing,2012, 50(5):1427-1439

TROPOMI on the ESA Sentinel-5 Precursor: AgMES missionforglobal observations of the atmospheric composition forclimate, air quality and ozone layer applications

Veefkind, JP; Aben, I; McMullan, K; et al.

Abstract:The ESA (European Space Agency) Sentinel-5 Precursor (S-5 P) is a low Earth orbit polar satellite to provide information and services on air quality, climate and the ozone layer in the timeframe2015–2022. The S-5 P mission is part of theglobal Monitoring of the Environment and Security (GMES) Space Component Programme. The payload of the mission is the TROPOspheric Monitoring Instrument (TROPOMI) that will measure key atmospheric constituents including ozone, NO2, SO2, CO, CH4, CH2O and aerosol properties.TROPOMI has heritage to both the Ozone Monitoring Instrument (OMI) as well as to the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY(SCIAMACHY). The S-5 P will extend the data records of these missions as well as be a preparatory mission for the Sentinel-5 mission planned for2020 onward. The mission is pre-operational and is the link between the current scientific and the operational Sentinel-4/-5 missions. This contribution describes the science and mission objectives,the mission and the instrument, and the data products.While building on a solid foundation of the heritage instruments, the S-5P/TROPOMI mission is an exciting step forward with a strong focus on the troposphere. This is achieved by a combination of a high spatial resolution and improved signal-to-noise, as well as dedicated data product development. It is anticipated that the S-5 P mission will make a large contribution to the monitoring of theglobal atmospheric composition, as well as to the scientific knowledge of relevant atmospheric processes.

來(lái)源出版物：Remote Sensing of Environment,2012,120:70-83

Estimatingground-level PM2.5 in China using satellite remote sensing

Ma, ZW; Hu, XF; Huang, L; et al.

Abstract:Estimating ground-level PM2.5from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5exposure in a largegeographic region. However, studies outside North America have been limited due to the lack ofground PM2.5measurements to calibrate the model.Taking advantage of the newly established national monitoring network, we developed a national-scalegeographically weighted regression (GWR) model to estimate daily PM2.5concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information cangreatly improve model performance. The overall cross-validation (CV)R2is 0.64 and root mean squared prediction error (RMSE) is32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5is8.28 μg/m3. Our predicted annual PM2.5concentrations indicated that over96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend theground PM2.5monitoring network in China.

來(lái)源出版物：Environmental Science & Technology,2014,48(13):7436-7444

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledgegaps

Mulla, DJ

Precision agriculture dates back to the middle of the1980’s. Remote sensing applications in precision agriculture began with sensors for soil organic matter, and have quickly diversified to include satellite, aerial, and hand held or tractor mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands. Today, electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum, enabling advanced applications such as light detection and ranging (LiDAR),fluorescence spectroscopy, and thermal spectroscopy,along with more traditional applications in the visible and near infrared portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of specific compounds, molecular interactions, crop stress,and crop biophysical or biochemical characteristics. A variety of spectral indices now exist for various precision agriculture applications, rather than a focus on only normalised difference vegetation indices. Spatial resolution of aerial and satellite remote sensing imagery has improved from100’s of m to sub-metre accuracy,allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. Temporal frequency of remote sensing imagery has also improved dramatically.At present there is considerable interest in collecting remote sensing data at multiple times in order to conduct near real time soil, crop and pest management.