Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.     

Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.     

近地表大气逆温条件下的地表温度遥感反演与验证

为了减少近地表大气逆温对地表温度遥感反演精度的影响,提出在晴空的地表温度"通用劈窗算法"模型中增加一个温度改正项来实现。在建立该误差改正项时,利用正常条件下的通用劈窗算法系数和具有不同逆温强度的逆温廓线,并结合大气辐射传输模型MODTRAN计算,得到近地表大气逆温条件下的地表温度反演误差,并在分析了该误差值与相应的逆温强度的关系后,发现该温度改正项可以表示为近地表大气逆温强度的二次项函数。为了进一步提高地表温度的反演精度,将地表温度和大气水汽含量进行分组,分别针对每个分组来确定温度改正项方程的系数。模拟结果表明,在逆温强度为1.7 K/100m时,该温度改正项可以使地表温度的反演精度提高0.44 K。利用内蒙古海拉尔试验站的实测数据对地表温度反演结果进行了验证,在近地表大气存在逆温的条件下,该方法能提高地表温度的遥感反演精度0.47K。但是,由于本文提出的方法需要已知大气温度廓线来计算大气逆温强度,因此在实际应用中该方法受到了一定的限制。     

Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar Operational Environmental Satellite data for land surface monitoring in Africa

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth's natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 min which is substantially greater than any temporal resolution that can be obtained from existing Polar Operational Environmental Satellite (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (<5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.     

热红外地表温度遥感反演方法研究进展

地表温度是表征地表过程变化的一个非常重要的特征物理量,是地表—大气能量交换的直接驱动因子,广泛地用于地表能量平衡、气候变化和资源环境监测等研究领域。本文系统地评述了热红外地表温度遥感反演方法,包括单通道算法、多通道算法、多角度算法、多时相算法和高光谱反演算法。回顾了地表温度反演的基础理论和方法;并在此基础上,进一步综述了地表温度遥感反演的验证方法,以及地表温度的时间和角度归一化方法;最后对未来提高地表温度反演精度的研究方向提出了建议。     

热红外遥感地表温度与发射率地面验证进展

地表温度与发射率是地表—大气系统长波辐射和潜热通量交换的直接驱动力,是描述区域和全球尺度上地表能量平衡与水平衡的重要参数,其时空变化信息在气象预测、气候变化、水循环、地质勘探、农林监测和城市热环境等诸多领域具有广泛的应用.热红外遥感作为当前获取区域或全球尺度上地表温度和发射率的最有效手段之一,相较于传统的地面点位测量方...     

Land surface temperature retrieval and its validation using NOAA AVHRR thermal data

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LST_corr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.     

针对Terra/MODIS数据的改进分裂窗地表温度反演算法

针对Terra/MODIS数据提出改进的分裂窗地表温度反演算法。充分考虑了传感器观测角度(VZA)的影响,并对地表和有效大气辐射按照不同的亮度温度区间分别进行Planck函数简化。利用TIGR3大气廓线库中的875条晴空大气廓线,ASTER波谱库中的106条地物发射率波谱,结合MODTRAN4大气辐射传输模型模拟得到分裂窗算法系数。利用MODTRAN4模拟数据对算法精度进行验证,结果表明本文的改进算法和原算法的均方根误差RMSE分别为0.34K和0.65K。敏感性分析表明,在中等湿润的大气条件下,算法对大气水汽含量并不敏感。该算法降低了传感器观测角度带来的地表温度反演误差。利用2009年6月美国SURFRAD辐射观测网6个站点的实测数据对改进算法、原算法以及MOD11_L2地表温度产品进行了对比验证,RMSE分别是0.93K、1.49K和1.0K,表明本文算法可以提高反演精度。     

Landsat卫星热红外数据地表温度遥感反演研究进展

作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等.Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中.本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年-2020年...     

Combination of Spectral and Textural Features in the MSG Satellite Remote Sensing Images for Classifying Rainy Area into Different Classes

The rainfall intensity classification technique using spectral and textural features from MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared) satellite data is proposed in this paper. The study is carried out over north of Algeria. The developed method is based on the artificial neural multilayer perceptron network (MLP). Two MLP algorithms are used: the MLP-S based only on spectral parameters and the MLP-ST that use both spectral and textural features. The MLP model is created with three layers (input, hidden, and output) that consist of 6 output neurons in the output layer that represent the 6 rain intensities classes: very high, moderate to high, moderate, light to moderate, light and no rain and 10 spectral input neurons for the MLP-S and 15 input neurons for MLP-ST, which as ten spectral features that were calculated from MSG thermal infrared brilliance temperature and brilliance temperature difference and as five textural features, and The rainfall intensity areas classified by the proposed technique are validated against ground-based radar data. The rainfall rates used in the training set are derived from Setif radar measurements (Algeria). The results obtained after applying this method show that the introduction of textural parameters as additional information works in improving the classification of different rainfall intensities pixels in the MSG/SEVIRI imagery compared to the techniques based only on spectral information. These results are compared with results obtained with the probability of rainfall intensity (PRI). This comparison revealed a clear outperformance of the MLP algorithms over the PRI algorithms. Best results are provided by the MLP-ST algorithm. The combination of spectral and textural features in the MSG–SEVIRI imagery is important and for the classification of the rainfall intensities to different classes.     

利用HJ-1B模拟数据反演地表温度的两种单通道算法

针对HJ-1B热红外波段特点,采用修正型QK & B算法,反演广州市2013-01-14的地表温度（land surface temperature,LST）。建立偏微分方程得出,当辐射率误差为0.01时,引起的LST误差约为0.6 K,LST误差与大气透过率成反比,与大气透过率误差成正比,0.1的透过率误差引起LST误差约1 K。大气水汽含量w误差与LST误差成线性关系,当大气水汽含量误差为0.1 g/cm²时,引起LST误差约为0.2 K。LST反演误差与近地表气温误差和大气平均作用温度误差均成正比,1 K的近地表气温误差引起LST反演误差约1K。总的来说,LST反演误差与区间比值和大气平均作用温度误差和近地表气温误差相关。用算法反演出来的广州市地表温度与MOD11_L2温度产品具有较强的空间一致性,温度差值曲线呈正态分布,主要集中在-0.9~0.9℃区域,选取广州市6个观测点,得出修正型QK & B算法和实测地温平均值相差约为0.31 K,MOD11_L2与实测地温的温度平均值相差0.65 K,误差均小于1 K。通过对修正型QK & B算法偏微分方程的推导,可对HJ-1B/IRS中的LST反演进行更细致和精确的分析,为其他针对环境卫星热红外波段类似反演LST的算法提供一定的借鉴,也为后续提高LST反演精度提供科学依据。     

利用单窗算法反演Landsat 8 TIRS数据地表温度

以Landsat 8为数据源,并结合地表发射率、大气透过率等参数遥感估算方法,提出了针对TIRS 10数据的单窗算法TIRS10_SC,并开展了研究区的地表温度反演3种单窗算法的对比研究。结果表明,TIRS10_SC算法紧密结合Landsat8 TIRS传感器的特性,通过遥感估算城区下垫面的地表发射率、大气透过率等特征,可以较为准确地估算出地表不同覆被类型的温度;裸土与水泥下垫面等相对均质的下垫面的温度反演效果稍好,TIRS10_SC算法和Q_SC算法其平均误差为0.60℃,JM_SC算法其平均误差为1.01℃;对于植被下垫面,TIRS10_SC算法和Q_SC算法其平均误差为1.48℃,JM_SC算法其平均误差为1.26℃,为了提升城区植被下垫面温度反演精度,应该进一步准确地量化其发射率特性。     

城市地表温度空间降尺度研究——以北京市为例

地表温度LST (Land Surface Temperature)是城市热环境研究的重要参数之一,城市下垫面极为复杂,LST空间差异性较高.高空间分辨率LST对精细化城市热环境监测和缓解具有重要意义.目前大部分城市遥感LST降尺度研究仍以二维角度为主,缺乏建筑三维结构的考虑.本研究同时考虑地表二维和三维指标,构建基于...     

基于FY-2F数据的中国区域地表温度日变化模型评价及特征研究

地表温度日变化模型作为非常重要的输入参数在气象、水文、生态等领域研究中具有重要意义。风云二号(FY-2F)静止气象卫星的地表温度产品的时间分辨率为1小时,这为拟合精确的地表温度日变化(DSTC)模型提供了可能。本文首先利用194个气象站点对应的2014年的FY-2F地表温度产品评价了GOT01、VAN06、JNG06、INA08、GOT09和GEM_V这6种地表温度日变化模型在中国区的模拟精度,对不同时间窗口和不同地表覆盖类型拟合精度的差异进行了分析;其次,选用JNG06模型探究了中国区域地表温度随经纬度、季节和地表覆盖类型的日变化规律。研究结果表明:在不同时间窗口内,GOT09模型获得了全局最优的拟合精度,均方根误差为0.89 K;JNG06和GEM_V模型精度次之,均方根误差分别为0.92 K和0.94 K;GOT01、INA08和VAN06模型精度最差;各模型在城市和建筑区、农用地和自然植被以及常绿阔叶林这3类地表覆盖类型的拟合精度最好,其均方根误差在0.89—0.92 K,在其余地表覆盖类型的拟合精度在1.0 K以上。JNG06模型模拟的地表温度在4种典型的地表类型随纬度的变化规律较为明显,地表温度在1月份随纬度变化较为剧烈,在7月份整体波动较为平缓。综上所述,使用FY-2F地表温度产品建立的DSTC模型在中国区域具有较高的精度,模拟的地表温度随着纬度变化的规律较为明显。使用本文模型既可以纠正现有模型又可获取归一化地表温度产品,同时可以检验和标定陆面模式地表温度模拟结果。     

A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data

The split-window algorithm is the most commonly used method for land surface temperature (LST) retrieval from satellite data. Simplification of the Planck’s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck’s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck’s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the LST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the LST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between LST from MODIS LST product and LST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust; 2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.     

地表温度日变化模型偏差系数解算的地表温度降尺度

地表温度LST(Land Surface Temperature)是全球气候变化研究的关键参数,遥感是获取全球和区域尺度地表温度的一种切实可行手段,但现有的单一传感器无法提供高时空分辨率的LST数据,限制了遥感地表温度数据的深入广泛应用.现有的降尺度方法难以生成无缝高时空分辨率的地表温度数据,且降尺度效果易受高空间分辨...     

Case study of soil moisture effect on land surface temperature retrieval

Land surface temperature (LST) is an important element of the climate system. Remote sensing methods for estimating LST have been developed in the past and several of them have been implemented at large-scales. Geostationary satellites are of particular interest because they depict the diurnal cycle. Soil moisture has a strong effect on the magnitude of surface temperature via its influence on emissivity; yet, information on soil moisture at large scales is meager. It is of interest to estimate what effect soil moisture has on the retrieval accuracy of surface temperature by methods of remote sensing. In this study, newly developed algorithms to estimate land surface temperature (LST) from geostationary satellites will be applied to GOES-8 observations during the Southern Great Plains 1997 Hydrology Experiment (SGP-97) when surface observations of both soil moisture and surface temperature were made. The ground observations were used to first demonstrate the influence of soil moisture on the diurnal cycle of the surface temperature, its amplitude and the lag in LST maxima. Subsequently, it was established that errors in LST as derived from GOES-8 measurements have a negative correlation with soil moisture, namely, increasing with the decrease of soil moisture.     

中国MODIS地表温度产品验证

分析了MODIS地表温度产品的误差来源，重点研究利用高分辨率遥感影像数据ASTER同步反演的验证方法。以2003年8月1日太 湖地区为例，用ASTER数据的反演结果与同时相的MODIS地表温度产品进行比较，分别在太湖水面、无锡城区及城郊农田3个典型地表 状况下选取感兴趣区域做线性拟合，取得了较好的结果，拟合的R2值可达0.966 6。     

A simple retrieval method of land surface temperature from AMSR-E passive microwave data—A case study over Southern China during the strong snow disaster of 2008

The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9 GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meteorological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.92 °C, with an average LST retrieval error of 0.91–1.30 °C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 2 °C. This study is one of the rare applications that combine the meteorological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions.