基于可见光红外成像辐射仪数据的地表温度反演

地表温度是农业旱灾和作物估产模型的重要参数。该文针对可见光红外成像辐射仪(visible infrared imager radiometer suite,VIIRS)传感器缺乏水汽通道的特点,联合Aqua卫星搭载的中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)数据提出了基于分裂窗算法的VIIRS地表温度反演方法。对地表发射率和大气透过率这2个关键参数的获取进行了详细分析,选取了处于作物生长期的2013年6月4日VIIRS数据进行实例验证分析。结果表明,与全国气象数据比较该文算法在大尺度上能够较好地获取中国地表温度;与MODIS数据温度产品在高温产粮区比较,该文算法与MODIS温度产品精度较一致,两者差值小于1 K。使用MODTRAN(moderate resolution transmission)软件对算法的精度进行了模拟评价验证,分析表明:在一定的水汽和地表发射率条件下,算法反演精度一般保持在1 K内,平均误差为0.431 K,误差标准偏差为0.247 K。能够为农业干旱、作物长势等农情信息监测提供所需的地表温度数据。

基于MODIS影像渤海2012年海冰变化分析

海冰是影响渤海冬季海运的严重灾害,研究年度周期内海冰时空变化具有重要意义。依据研究内容选择11期具有代表性的中分辨率成像光谱仪(MODerate-resolution Imaging Spectroradiometer,MODIS)影像数据,采用ENVI(The Environment for Visualizing Images)软件进行预处理,利用目视解译方法提取海冰面积与空间位置信息,采用ArcGIS软件制作了海冰时空变化图、海冰结冻与融化过程变化图。最后,对海冰面积变化、海冰结冻与融化过程进行了详细分析。研究结果表明,利用MODIS影像进行年度周期内海冰变化分析技术方法是可行的。

京津冀地区气溶胶时空分布及与城市化关系的研究

利用AERONET(AErosol RObotic NETwork)数据对2008~2012年Terra MODIS(MOderate-resolution Imaging Spectroradiometer)C006 3 km卫星遥感气溶胶产品在京津冀地区的适用性进行了验证,分析京津冀地区3km分辨率气溶胶光学厚度(AOD)的时空分布和变化特征。利用DMSP(Defense Meteorological Satellite System)/OLS(Operational Linescan System)夜间灯光数据作为城市化评价手段,对京津冀地区城市化与AOD时空分布之间的关系进行了研究。结果表明:(1)MODIS 3 km气溶胶产品遥感反演数据和同期AERONET监测数据在研究区具有很好的一致性,相关系数达0.91,满足期望要求;(2)时间上,2008~2012年研究区年平均AOD值在0.361~0.453之间变化,年际间变化浮动大,总体呈下降趋势;AOD春季呈明显下降趋势,夏季总体呈微弱上升趋势,秋季和冬季呈明显上升趋势;(3)空间上,2008~2012年北京、天津和河北中南部的AOD值较高,河北北边AOD值较低;四季AOD空间分布呈现较强烈季节变化,夏季最高,冬季最低;(4)夜间灯光数据和AOD时空分布不仅在空间分布上呈现较好的一致性,且2008~2012年二者的地理权重回归(GWR)模型拟合度R2达0.8左右。研究区内AOD与夜间灯光数据二者相关性显著,城市化发展水平和人类活动对气溶胶的分布有着明显的影响。

基于MODIS与HJ-1多源卫星的上海海域溢油事故诊断

随着上海港海上运输业和石油产业链的日趋发达,海上溢油事故风险也随之加剧。本文就2012年发生在上海海域吴淞口和九段沙附近的2起重大溢油事故,基于美国NASA(National Aeronautics and Space Administration)中等分辨率MODIS(Moderate-resolution Imaging Spectroradiometer)与国产"环境一号"卫星HJ-1的多源卫星数据,对溢油信息进行对比,通过对油水敏感通道进行波段比值运算,突出油膜与背景海水的光谱反射率差异,再结合重柴油光谱特征,利用图像分割的阈值确定法,从疑似溢油区域中有效提取溢油信息,实现溢油区域定位、溢油面积和溢油量的诊断,为事发后海域应急响应工作提供基础性分析依据。

福建围填海及其对海洋环境影响的遥感初探

把遥感技术应用于海岸带现状的动态研究和遥感海洋环境反演是当前遥感应用的热点之一。文章主要利用美国资源卫星专题扫描仪(thematic mapper,TM)遥感影像研究福建围填海状况及大陆海岸线的变化,通过ENVI、ArcGIS软件对研究区遥感影像进行处理,实现海岸线的自动提取、土地分类,以监测福建围填海的动态变化。通过相同季节的中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)遥感影像反演福建海域海水表层温度及叶绿素浓度等海洋参数来了解围填海对海洋环境的可能影响。

Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

2000～2018年黄土高原沙尘天气遥感监测及尘源分析

利用 19 年( 2000 ~2018 年) MODIS( moderate resolution imaging spectroradiometer) L1B 数据对黄土高原 102 次沙尘天气过程进行遥感监测与分析,探究黄土高原沙尘天气发生的时空规律。结果表明,黄土高原沙尘天气呈减少趋势,沙尘频发季节为春季。黄土高原沙尘源地主要分布在其西北部,位于沙地和沙漠区、农灌区与黄土丘陵沟壑区、黄土高原沟壑区等生态脆弱的原生沙尘暴带。黄土高原典型的沙尘源为活动沙丘及丘间沙地、干涸湖泊、河道和农田等,表明沙尘天气频发是由自然因素和人为因素共同导致的。沙尘天气的遥感监测捕捉了黄土高原沙尘活动的时空变化特征,高效地识别了沙尘源地和尘源类型,对黄土高原气候变化、生态环境变化研究和环境修复与评价具有重要指导意义。

中国东部大陆和邻近海域暖云特性时空分布及其与气象条件的关系

基于2003~2016年MODIS/Aqua(MODerate resolution Imaging Spectroradiometer)云产品资料(MYD08_D3),分析了中国东部大陆及其邻近海域云量(CF)、云滴有效半径(CER)、和液水路径(LWP)的空间分布以及季节变化,并结合同期ERA-Interim再分析资料的850 hPa垂直速度(ω850hPa)、低对流层稳定度(LTS)、以及MODIS/Aqua水汽产品中的大气可降水量(PWV)资料,分析了云宏微观物理量与动力、热力及水汽条件之间的关系。从空间分布来看,夏季由日本海至中南半岛存在一个东北西南走向的云量高值区,覆盖我国东部地区,冬季云量高值区位于我国南方地区和东部海域上空;云滴有效半径冬、夏分布类似,均为由东南洋面至西北内陆递减;夏季液水路径分布较为均一,冬季空间差异很大,30°N是明显的高低值分界线,这与冬季水汽的分布密切相关。陆地和海洋上云量均呈冬高夏低的变化趋势,陆地大于海洋,而云滴有效半径和液水路径则为夏高冬低,海洋大于陆地。总体来说,云量与PWV和LTS均表现为正相关、与ω850hPa呈负相关,表明低层的上升运动有利于水汽向上输送、凝结形成云,但稳定的大气层结又会阻碍云进一步向上发展,使其被限制在底层空间,由于本文的研究对象为暖云,多为中低云,因而云量较高;云滴有效半径和液水路径均与LTS、ω850hPa表现为负相关,但是对PWV的变化不是很敏感,表明水汽并不是影响云滴尺度和液水路径的主导因素,其主要受动力、热力抬升作用的影响;以上关系在不同区域、不同季节的表现存在一定差异。

Multiple Cropping Intensity in China Derived from Agro-meteorological Observations and MODIS Data

Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

Responses of Alpine Wetlands to Climate Changes on the Qinghai-Tibetan Plateau Based on Remote Sensing

The alpine wetlands in QTP(Qinghai-Tibetan Plateau)have been profoundly impacted along with global climate changes.We employ satellite datasets and climate data to explore the relationships between alpine wetlands and climate changes based on remote sensing data.Results show that:1)the wetland NDVI(Normalized Difference Vegetation Index)and GPP(Gross Primary Production)were more sensitive to air temperature than to precipitation rate.The wetland ET(evapotranspiration)across alpine wetlands was greatly correlated with precipitation rate.2)Alpine wetlands responses to climate changes varied spatially and temporally due to different geographic environments,variety of wetland formation and human disturbances.3)The vegetation responses of the Zoige wetland was the most noticeable and related to the temperature,while the GPP and NDVI of the Qiangtang Plateau and Gyaring-Ngoring Lake were significantly correlated with both temperature and precipitation.4)ET in the Zoige wetland showed a significantly positive trend,while ET in Maidika wetland and the Qiangtang plateau showed a negative trend,implying wetland degradation in those two wetland regions.The complexities of the impacts of climate changes on alpine wetlands indicate the necessity of further study to understand and conserve alpine wetland ecosystems.

Temporal-Spatial Variation of Surface Suspended Matter and Controlling Factors in the Inner Shelf of the East China Sea in Winter

Based on the observed surface suspended matter in the East China Sea in February 2007 and June 2015, an empirical model was established using L1 b's band 4 data to retrieve surface suspended matter from the Moderate Resolution Imagine Spectroradiometer Terra imagery. The squared correlation coefficient is 0.8358, and the root mean square error is 0.4285 mg L-1. The model reflects the distribution characteristics of surface suspended matter in the inner shelf of the East China Sea. In this paper, the satellite images of the study area were retrieved in January from 2001 to 2015, and the monthly distribution of surface suspended matter were obtained. The inter-annual distribution of the study area is similar, and the concentration of surface suspended matter is higher near the shore than offshore. A large amount of surface suspended matter is transported southeast under the influence of Zhejiang and Fujian coastal current and Taiwan warm current. Only a small amount of surface suspension can reach the Kuroshio area. The surface suspended matter concentration changes obviously near the estuary because of the effect of differences in the flux of the Yangtze River. Meanwhile, winter monsoon, temperature front, El Ni?o events, and other factors affect the distribution of surface suspended matter in 100 m isobath to coastal water but minimally influence the distribution in 100 m isobath to deep sea.

A Simple Method to Extract Tropical Monsoon Forests Using NDVI Based on MODIS Data:A Case Study in South Asia and Peninsula Southeast Asia

Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data

Evapotranspiration(ET) is a critical component of the global hydrological cycle, and it has a large impact on water resource management as it affects the availability of freshwater resources. It is important to understand the hydrological cycle for the water resources planning and management. This study used Moderate Resolution Imaging Spectroradiometer(MODIS) satellite derived ET, and potential evapotranspiration(PET) and Tropical Rainfall Measuring Mission(TRMM) satellite derived precipitation datasets to assess the spatial and temporal distributions of ET, PET, and precipitation during the study period at Three Gorges Reservoir(TGR) region. Based on the topographic variations and land-use/land-cover distributions, the study region which includes five counties of Hubei Province and nineteen counties of Chongqing Municipality was divided into four study zones. The ET and precipitation data were evaluated using in situ observations. The ET, PET, and precipitation data were compared to analyze the spatial and long-term(2001-2016) temporal distributions of average annual ET, PET, and precipitation, and to understand the relationships between them in the study region. The results showed that each selected zone had highest ET at the counties with the Yangtze River passing through whereas lowest at the counties which were located away from the river. Results also showed increasing trends in ET and PET from south-west to north-east in the study region. Analysis showed TGR had a significant impact on spatial and temporal distributions of ET and PET in the study region. Therefore, this study helps to understand the impact of TGR on spatial and temporal distributions of ET and PET during and after the construction.

Influence of Sea Surface Temperature on Outbreak of Ulva prolifera in the Southern Yellow Sea,China

In this study,using Moderate Resolution Imaging Spectroradiometer(MODIS)satellite images and environmental satellite CCD images,the spatio-temporal distribution of Ulva prolifera in the southern Yellow Sea during the period of 2011–2018 was extracted and combined with MODIS Level3 Photosynthetically Active Radiation(PAR)product data and Earth System Research Laboratory(ESRL)Sea Surface Temperature(SST)data to analyze their influences on the growth and outbreak of Ulva prolifera.The following conclusions were drawn:1)comprehensive analysis of Ulva prolifera distribution during the eight-year period revealed that the coverage area of Ulva prolifera typically exhibited a gradually increasing trend.The coverage area of Ulva prolifera reached a maximum of approximately 1714.21 km^2 during the eight-year period in late June 2015.The area affected by Ulva prolifera fluctuated.In mid-July 2014,the area affected by Ulva prolifera reached a maximum of approximately 39020.63 km^2.2)The average growth rate of Ulva prolifera was positive in May and June but negative in July.During the outbreak of Ulva prolifera,the SST in the southern Yellow Sea tended to increase each month.The SST anomaly and average growth rate of Ulva prolifera were positively correlated in May(R^2=0.62),but not significantly correlated in June or July.3)The variation trends of PAR and SST were approximately the same,and the PAR during this time period maintained a range of 40–50 mol/(m^2·d),providing sufficient illumination for the growth and outbreak of Ulva prolifera.In addition,the abundant nutrients and suitable temperature in the sea area near northern Jiangsu shoal resulted in a high growth rate of Ulva prolifera in May.In summary,the outbreak of Ulva prolifera was closely related to the environmental factors including SST,nutrients,and PAR.Sufficient nutrients and suitable temperatures resulted in a fast growth rate of Ulva prolifera.However,under poor nutrient conditions,even more suitable temperatures were not sufficient to trigger an outbreak of Ulva prolifera.

Assessment of rice fields by GIS／GPS-supported classification of MODIS data

The new Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite image offers a large choice of opportunities for operational applications. The 1-km Advanced Very High Resolution Radiometer (AVHRR) image is not suitable for retrieval of field level parameter and Landsat data are not frequent enough for monitoring changes in crop parameters during the critical crop growth periods.A methodology to map areas of paddy fields using MODIS,geographic information system (GIS) and global position system (GPS) is introduced in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy.A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. By integration of MODIS with GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.7% percent compared with the statistical data of the Agricultural Bureau of Zhejiang Province.