利用EDS／MODIS交叉定标FY1D／VIRR可见光-近红外通道

以EOS/MODIS的星上定标结果为标准,采用交叉定标方法标定FY1D/VIRR的可见光-近红外通道。先获得两个传感器的配对通道,然后利用6S模式计算出他们的匹配因子,根据EOS/MODIS的观测敦煌辐射校正场扫描计数值和星上定标结果以及它和FY1D/VIRR的匹配因子计算出FY1D/VIRR同时观测敦煌辐射校正场时的表观辐亮度和表观反射率,最后将得到的表观辐亮度和表观反射率与FY1D/VIRR观测敦煌辐射校正场的计数值进行比较,就得到了FY1D/VIRR通道的绝对定标系数。将本次定标结果与敦煌场地定标试验的定标结果比较,定标结果一致性较好,并可用于对发射前定标偏差进行校正。     

同化MODIS温度产品估算地表水热通量

基于集合卡尔曼滤波和通用陆面模型(CLM 1.0)发展了一个地表温度的同化系统。这个系统同化了MODIS温度产品, 并将MODIS的叶面积指数引入CLM模型中, 主要用于改进地表水热通量的估算精度。将CLM输出的地表温度与MODIS地表温度建立关系, 并作为同化系统的观测算子。将MODIS地表温度与实测地表温度进行了比较, 将其均方差(Root Mean Square Error, RMSE)作为观测误差。选取3个美国通量网站点(Blackhill、Bondville、Brookings)作为实验数据, 结果表明: 同化结果中地表温度、显热通量的估算精度均有提高。其中Blackhill站的估算精度改进最大, 均方差由81.5W·m-2减小到58.4W·m-2, Bondville站均方差由47.0W·m-2减小到31.8W·m-2, Brookings站均方差由46.5W·m-2减小到45.1W·m-2。潜热通量估算精度在Bondville站均方差由88.6W·m-2减小到57.7W·m-2, Blackhill站均方差由53.4W·m-2减小到47.2W·m-2。总之, 结合陆面过程模型同化MODIS温度产品估算地表水热通量是可行的。     

NOAA18/AVHRR与FY-3A/VIRR的OLR产品一致性和差异性分析

卫星的射出长波辐射OLR(Outgoing Long-wave Radiation)数据具有不同程度的误差。为满足业务和科研工作的需要,对国产卫星FY-3 A/VIRR的OLR产品与其他同类卫星产品进行一致性和差异性分析是非常必要的。采用风云三号A星(FY-3A)扫描辐射计(VIRR)的OLR日平均产品作为被检验数据,美国大气海洋局NOAA18卫星上搭载的甚高分辨率扫描辐射计(AVHRR)OLR日平均产品作为检验源数据,使用相关系数、平均偏差、均方根误差、相对误差等检验方法,对两种产品进行一致性和差异性分析。结果表明,两种OLR资料大部分相关系数较大,平均偏差、均方根误差和相对误差较小,个别资料相关系数较小,平均偏差、均方根误差和相对误差较大。如2010年4月23日、7月13日和10月13日,相关系数、平均偏差、均方根误差、相对误差分别为(0.63,7 Wm^-2,31 Wm^-2,0.12)、(0.5,-5 Wm^-2,45 Wm^-2,0.125)和(0.3,-200 Wm^-2,225 Wm^-2,0.85)。这些偏差主要发生在高山和海洋地区,并且暖季相对冷季偏差较大,可能是由于高山、海洋地区和暖季较强的对流活动造成两种资料的对比结果存在一定的差异;此外,由于FY-3A为上午星,NOAA18为下午星,过境时间存在一定的差异,也给二者的对比结果带来一定的差异。     

基于MODIS的2001年—2012年北京热岛足迹及容量动态监测

利用2001年—2012年MODIS分裂窗算法反演得到的1 km分辨率地表温度产品分析了北京城市热岛效应。首先计算北京2001年—2012年地表温度年平均值,其次利用半径法确定热岛足迹并计算热岛容量。结论如下:(1)热岛足迹及热岛容量昼夜差异明显,2012年白天热岛足迹是夜间的1.5倍,这是由于城市下垫面热特性差异及人为活动的综合影响。(2)2001年—2012年北京城市高温区在空间上向南北扩展,热岛足迹和热岛容量呈阶段性增长。2010年白天热岛足迹最大,半径为28 km,面积是2001年的2.4倍。当热岛足迹相同时,城市绿地和水体功能区的分布和布局方式等因素能够影响热岛容量。城市建设用地和农村居民点对城市热环境贡献率明显高于其他土地利用类型。当建设用地面积比例超过50%时,区域会产生显著的热岛现象。(3)根据北京热岛足迹及容量时空动态变化特征,提出改善城市热岛的措施。     

A new 500-m resolution map of canopy height for Amazon forest using spaceborne LiDAR and cloud-free MODIS imagery

In the present study, we aimed to map canopy heights in the Brazilian Amazon mainly on the basis of spaceborne LiDAR and cloud-free MODIS imagery with a new method (the Self-Organizing Relationships method) for spatial modeling of the LiDAR footprint. To evaluate the general versatility, we compared the created canopy height map with two different canopy height estimates on the basis of our original field study plots (799 plots located in eight study sites) and a previously developed canopy height map. The compared canopy height estimates were obtained by: (1) a stem diameter at breast height (D) – tree height (H) relationship specific to each site on the basis of our original field study, (2) a previously developed D–H model involving environmental and structural factors as explanatory variables (Feldpausch et al., 2011), and (3) a previously developed canopy height map derived from the spaceborne LiDAR data with different spatial modeling method and explanatory variables (Simard et al., 2011). As a result, our canopy height map successfully detected a spatial distribution pattern in canopy height estimates based on our original field study data (r = 0.845, p = 8.31 × 10⁻³) though our canopy height map showed a poor correlation (r = 0.563, p = 0.146) with the canopy height estimate based on a previously developed model by Feldpausch et al. (2011). We also confirmed that the created canopy height map showed a similar pattern with the previously developed canopy height map by Simard et al. (2011). It was concluded that the use of the spaceborne LiDAR data provides a sufficient accuracy in estimating the canopy height at regional scale.     

Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data

Beijing has experienced rapid urbanization and associated urban heat island effects and air pollution. In this study, a contribution index was proposed to explore the effect of urbanization on land surface temperature (LST) using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived data with high temporal resolution. The analysis indicated that different zones and landscapes make diurnally and seasonally different contributions to the regional thermal environment. The differences in contributions by the three main functional zones resulted from differences in their landscape compositions. The roles of landscapes in this process varied diurnally and seasonally. Urban land was the most important contributor to increases in regional LSTs. The contributions of cropland and forest varied distinctly between daytime and nighttime owing to differences in their thermal inertias. Vegetation had a notable cooling effect as the normalized vegetation difference index (NDVI) increased during summer. However, when the NDVI reached a certain value, the nighttime LST shifted markedly in other seasons. The results suggest that urban design based on vegetation partitions would be effective for regulating the thermal environment.     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

FY-3C/MWHTS资料反演陆地晴空大气温湿廓线

针对风云三号C星微波湿温探测仪(FY-3C/MWHTS)的陆地晴空观测资料,建立了一维变分反演系统,对大气的温湿廓线进行反演。为了更好地描述温湿廓线的相关性,同时减小温度和湿度在反演过程中相互之间的误差传递,提出了使用背景协方差矩阵的联合矩阵和单独矩阵进行组合反演的方法。对于MWHTS模拟亮温和观测亮温之间的偏差,使用逐扫描点的统计回归方法进行校正。选择中国部分陆地区域的晴空观测亮温进行温湿廓线的反演,并利用欧洲中期天气预报中心(ECMWF)再分析数据、美国国家环境预报中心(NCEP)分析数据以及无线电探空观测(RAOB)数据对反演结果进行验证,温湿廓线的反演结果与ECMWF再分析数据验证的最大均方根误差分别是2.59 K和11.87%,与NCEP分析数据验证的最大均方根误差分别是1.88 K和21.50%,与RAOB数据验证的最大均方根误差分别是3.43 K和25.48%,验证结果表明了反演结果的可靠性。另外与国外同类载荷AMSU观测亮温的物理方法和统计方法反演精度进行了对比,结果表明:MWHTS具有较强的湿度廓线以及高空温度廓线的探测能力,且针对MWHTS的观测亮温建立的一维变分反演系统具有较高的反演精度。与NCEP 6小时预报廓线的验证结果表明:反演的湿度廓线可以提高预报廓线的精度。     

Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010

Rice is the most consumed staple food in the world and a key crop for food security. Much of the world’s rice is produced and consumed in Asia where cropping intensity is often greater than 100% (more than one crop per year), yet this intensity is not sufficiently represented in many land use products. Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source, inputs, and crop establishment methods. Thus, spatial and temporal information on the seasonal extent of rice is an important input to decision making related to increased agricultural productivity and the sustainable use of limited natural resources. The goal of this study was to demonstrate that hyper temporal moderate-resolution imaging spectroradiometer (MODIS) data can be used to map the spatial distribution of the seasonal rice crop extent and area. The study was conducted in Bangladesh where rice can be cropped once, twice, or three times a year.MODIS normalized difference vegetation index (NDVI) maximum value composite (MVC) data at 500 m resolution along with seasonal field-plot information from year 2010 were used to map rice crop extent and area for three seasons, boro (December/January–April), aus (April/May–June/July), and aman (July/August–November/December), in Bangladesh. A subset of the field-plot information was used to assess the pixel-level accuracy of the MODIS-derived rice area. Seasonal district-level rice area statistics were used to assess the accuracy of the rice area estimates. When compared to field-plot data, the maps of rice versus non-rice exceeded 90% accuracy in all three seasons and the accuracy of the five rice classes varied from 78% to 90% across the three seasons. On average, the MODIS-derived rice area estimates were 6% higher than the sub-national statistics during boro, 7% higher during aus, and 3% higher during the aman season. The MODIS-derived sub-national areas explained (R² values) 96%, 93%, and 96% of the variability at the district level for boro, aus, and aman seasons, respectively.The results demonstrated that the methods we applied for analysing and interpreting moderate spatial and high temporal resolution imagery can accurately capture the seasonal variability in rice crop extent and area. We discuss the robustness of the approach and highlight issues that must be addressed before similar methods are used across other areas of Asia where a mix of rainfed, irrigated, or supplemental irrigation permits single, double, and triple cropping in a single calendar year.     

利用MODIS多通道反照率产品估算OCO-2氧气A吸收带陆表反照率方法

Land surface reflection depends on land surface albedo and interferes with the retrieval of cloud geometrical thickness from OCO-2 oxygen A-band observations due to its second-strongest reflection after the cloud. However, no product can provide the land surface albedo of the OCO-2 oxygen A-band required for the retrieval. Therefore, the accurate estimation of land surface albedo is necessary and beneficial to the retrieval quality. In this study, we proposed a method for estimating land surface albedo in the oxygen A-band from multichannel black/white albedos from MODIS/MCD43C3 products. Although the estimation (MODIS→OCO-2) is related to land cover type, the comparison based on Shannon entropy proved that the multichannel albedo data contains the type information and is sufficient to achieve the same accuracy as land cover-type estimation. In addition, we implement the estimation model by BP neural network, and the accuracy is consistent with that of the analysis based on the Shannon entropy. We verified the multichannel-based estimation model by tests in different times and spaces. Its correlation coefficients were all over 0.93, and the root-mean-squared errors were 0.026. In addition, the multichannel-based model was always superior to the single-channel linear model on all land cover types, whether applied to the best-performing type of barren or sparsely vegetated land or the worse-performing type of snow and ice. The quality of MODIS albedo data is the most important for the accuracy of estimation. The root-mean-squared error with the best inputs was slightly better than 0.02 and increased to more than 0.05 as the quality of the inputs decreased. The method of estimating the land surface albedo in the OCO-2 oxygen A-band from MODIS multichannel black/white albedo data is feasible and can resist the disturbance caused by unknown land cover type. The estimation accuracy depends on the quality of the input MODIS albedo data. © 2023 Science Press. All rights reserved.     

Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data

Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.