云下遥感地表温度重构方法研究

地表温度是研究地-气之间水热平衡的重要参数,对地表温度的全天候获取具有重要意义。热红外遥感可以获得较高分辨率空间全覆盖的地表温度产品,但是有云地区数据缺失问题制约了地表温度遥感产品的全天候应用。文章发展了2种对云下缺失的地表温度进行重构的方法,方法1是借助地表温度同化数据集发展了一种时空匹配的数据融合方法,方法2是将当前在海表参数重构研究中较为流行的经验正交函数插值法(data interpolating empirical orthogonal function,DINEOF)方法应用于地表温度的重构研究中。通过对2017年中国地区地表温度遥感数据的重构对比了2种方法的效果与精度,结果显示:2种方法在整个中国地区不同季节有云条件下精度在2.5~3.5 K之间。方法可为今后地表温度遥感数据的全天候获取提供有益帮助。

风云静止卫星地表温度产品空值数据稳健修复

静止卫星地表温度数据是研究昼夜气候和环境变化的重要参数。但现有发布的静止卫星地表温度数据由于受到云等大气因素的影响,往往出现数值缺失现象。针对该问题,提出基于昼夜变化模型的风云静止卫星地表温度空值数据的稳健修复方法。由多项式、傅里叶函数和高斯函数构建新的昼夜变化模型,并利用LevenbergMarquardt算法进行模型参数的求解与优化,进而实现空值修复。以风云2号F星数据(FY-2F)为例,模拟不同类型的像元缺失情况进行修复,并将不同模型修复结果与真实温度值比较,同时也对真实数据进行了测试。结果表明:本文提出的修复方法能有效对温度空值数据修复,且优于传统方法。

结合多源数据的第二产业时空变化发展研究

针对夜间灯光数据不能较好地解释第二产业空间布局的问题,本文提出了一种适合第二产业增加值空间化的新方法。该方法将筛选的POI数据与地表温度数据相结合,构建第二产业地表温度POI指数(STPI指数),并与农村居民点的夜间灯光数据耦合建模,以淮海经济区核心城市群为研究区开展研究。结果表明,相比于耦合土地利用数据与夜间灯光遥感数据方法,本文提出的第二产业空间化模型在2014、2016、2018、2020年各个年份的拟合优度(R 2分别为0.926、0.882、0.907、0.896)均优于前者(R 2分别为0.859、0.805、0.880、0.849),每年的平均相对误差均低于前者,平均值维持在10%左右。并以徐州市辖区为例,局部对比两种方法的第二产业空间化结果,本文方法可以显著提高第二产业增加值建模精度与空间化效果,其空间分布与实际更为吻合.本文结果可为有关部门制订区域经济发展规划提供一定的参考。

Improvement of Mono-window Algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

Sensitivity of the Number of Snow Cover Days to Surface Air Temperature over the Qinghai-Tibetan Plateau

Based on the number of snow cover days （NSCDs） and homogenized surface air temperature data for the period 1951-2004, this study performs the quantitative analysis on the sensitivity of NSCDs to surface air temperature over the Qinghai-Tibetan Plateau （QTP）. Results show that both the extreme sensitivity and sensitivity under current climate are higher in the edge than in the central area of the QTP. There exists a strong negative correlation between station＇s elevation and critical temperature, at which the sensitivity reaches extremum. The negative correlation between the elevation and the extreme sensitivity is not as strong as the former one. Currently, the climatological temperatures in quite a few stations do not reach the critical stage. The sensitivity at these stations will become greater under the current background of climate warming, which means NSCDs will be more sensitive to surface air temperature.

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.