An accurate sea foam emissivity model at microwave frequencies is indispensable for applications of microwave data in satellite data assimilation and remote sensing of atmospheric and surface parameters. This study develops an analytic model for estimating sea foam emissivity by integrating the two-stream radiative transfer approximation with a macroscopic parameterization approach. Sea foam is modeled as a vertically inhomogeneous medium, with an exponential vertical profile based on void fraction and foam thickness. The emissivity for vertical and horizontal polarization is derived by combining the interface reflections with the two-stream radiative transfer solutions. The model is validated against the experimental data and shows a high consistency with incoherent methods, thus demonstrating its robustness for remote sensing and oceanographic applications. A sensitivity analysis is also performed for the key model parameters. It is found that within a frequency ranging from 1 to 37 GHz, the L-band is the most sensitive to sea foam thickness and the sensitivity decreases as the frequency increases. The emissivity is saturated for a specific foam thickness. Furthermore, the algorithm developed in this study includes two additional adjustable parameters, namely, the single-scattering reflectance and the asymmetry factor, which enable the simulation to account for the scattering effects of foam, thereby outperforming the traditional incoherent method.

Currently, two microwave radiation imagers (MWRI) are flying onboard Fengyun-3 series (midmorning and afternoon orbits) satellites and provide 10 channels of microwave brightness temperatures. This study will develop a global weekly updated land emissivity atlas for NWP communities for data assimilation applications. Two MWRI data are first intercalibrated for their data consistency. The retrieval algorithms are based on the earlier methodologies (1dvar and analytic). The retrieved atlas are compared with the existing data base (TELSEM2) which is widely used in NWP community for understanding the global consistency, and the results show higher correlation correlations at 10 channels. Moreover, three different emissivity datasets from AMSR-E, TELSEM2 and FY-3D are selected to compare the spatial correlation and mean absolute deviation with 1dvar results (FY-3D). It shows the FY-3D have higher spatial correlation (0.924) with AMSR-E1 and TELSEM2 datasets.

Land surface temperature (LST) plays an important role in the balance of water and energy between land surface and atmosphere. The accuracy of remotely sensed LST products can be checked by using ground measured temperature data. In this study, 0 cm ground surface temperature (GST) and 2 m air temperature (AT) data from China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0) were used to verify the accuracies of AQUA/Moderate-resolution Imaging Spectroradiometer (AQUA/MODIS) and FengYun-3D/MEdium Resolution Spectral Imager (FY-3D/MERSI) LST products in high vegetation covered areas. The results showed that the higher the canopy height, the larger the difference between remotely sensed LST and 0 cm GST. The bias between remotely sensed LST and 0 cm GST changing from -7 K in evergreen broadleaf forest area to -3.7 K in urban and built-up area. For AQUA/MODIS, the unbiased Root Mean Square Error (ubRMSE) are 2.77-3.39 K compared with 2 m AT and 2.69-3.87 K compared with 0 cm GST. However, for FY-3D/MERSI LST, the ubRMSE range from 3.42-4.01 K compared with 2m AT and 3.39-4.15 K compared with 0 cm GST. The precision of AQUA/MODIS products is better than that of FY-3D/MERSI LST products in high vegetation covered areas.

In this study, the channel data related to fire point identification of Advanced Geostationary Radiation Imager on Fengyun-4B (FY-4B/AGRI) and Advanced Meteorological Imager on GEO-KOMPSAT 2A (GK-2A/AMI) were cross-compared. A total of 267 sampling points in China (Guangdong, Guangxi, Guizhou, Yunnan, Hainan) were selected to carry out fine positioning correction on the data in different elevation intervals. Then, a fire monitoring algorithm based on FY-4B was proposed, in which the self-adaptive threshold adjustment of the underlying surface parameters and the reprocessing module of fire point identification were added. The algorithm can realize the high-precision and stable monitoring of fire. The continuous dynamic monitoring was carried out using the grassland fire Mongolia from April 18 to 20, 2022 as an example. The results showed that the parameters of FY-4B/AGRI and GK2A/AMI channels have high consistency. The root mean square error (RMSE) of reflection channel was 1.33%, and the maximum RMSE of brightness temperature channel was less than 1.3 Kelvin (K). Through the positioning analysis in different elevation intervals, the average longitude offset of FY-4B satellite data was -0.5°–0° and the average latitude offset was -0.9°–0.6°. Overall, these findings indicate that the high-frequency observations of FY-4B can be fully utilized to monitor forest and grassland fires, which can continuously track the dynamic evolution of fire and can distinguish the spatial distribution of different fire intensities in large-scale fire field.

Efficient and accurate monitoring of land surface eco-environmental situation (LSEES) is critical to promoting the sustainable development of global society. This study utilizes satellite data from EOS/MODIS to derive the land surface eco-environmental index (LSEESI) through the covariance-based principal component analysis method. Four strategies are used to evaluate the performance of this methodology. The stability, reasonability, comprehensive representation, and regional adaptability of this model are approved. LSEESI is also compared with the remote sensing ecological index (RSEI) and shows that LSEESI better indicates the LSEES (R2= 0.674 for LSEESI, 0.437 for RSEI). Application of the LSEESI model in Yangtze River Delta during 2001-2021 shows the conclusions as follows: 1) Overall, the LSEES in Yangtze River Delta is stable or improving, and the annual average LSEESI increased from 0.572 to 0.593. 2) There were significant spatial differences in LSEES in Yangtze River Delta. Areas with relatively poor LSEES were mainly in Suzhou-Wuxi-Changzhou urban agglomeration, Hangzhou-Jiaxing-Ningbo urban agglomeration, and Shanghai. Regions with deteriorating LSEES were also mainly concentrated in the above urban agglomerations around Lake Taihu. 3) The contribution of temperature, precipitation, and NTL to LSEES was 0.07, 0.38, and 0.55, respectively, suggesting that LSEES change in Yangtze River Delta in recent 21 years might have been influenced primarily by human activity, with only some parts of Anhui Province affected mainly by climate change. This study demonstrated that the proposed LSEESI model can effectively monitor and quantitatively evaluate LSEES change, and provide the information necessary for monitoring and managing eco-environmental systems.

The MEdium Resolution Spectral Imager-II (MERSI-II) onboard FY-3D satellite is used to retrieve surface vegetation parameters. MERSI TOA reflectances are corrected to surface reflectance and then used to compute normalized differential vegetation index (NDVI) and enhanced vegetation index (EVI) at the canopy levels. MERSI-II VI products are also compared with MODIS data and show a good consistency.

Satellite-derived aerosol optical depth (AOD) has been widely used to estimate surface fine particulate matter (PM2.5) concentrations. To understand the temporal and spatial variation of PM2.5distribution at various scales, we developed techniques for estimating hourly PM2.5concentration from visible and infrared imagers onboard Himawari-8, GK-2A and FY-4A satellites. During daytime, satellite observed reflectance at the top of atmosphere (TOA) is directly converted to PM2.5through a machine-learning algorithm. At night, the thermal channel data from satellites are tested for PM estimation through using the aerosol absorbing properties. It is found that the PM concentration is higher in the morning and lower in the afternoon in east central China. PM concentrations also exhibit a north-to-south decreasing gradient with the highest one in winter and the lowest in summer. From AHI PM data collected in the past five years, we found a decreasing trend of PM2.5concentration in most of areas of China.

Microwave land surface emissivity (LSE) is an important parameter for retrievals of land surface and atmospheric characteristics, and it is also crucial as an input parameter for numerical weather prediction model data assimilation. This study develops a method for retrieving all-weather LSE over China based on radiative transfer model through reconstructing spatial-temporal continuous land surface temperature (LST) data using China Land Data Assimilation System (CLDAS). Atmospheric effect is also removed with the relationships among atmospheric transmittances, atmospheric effective radiating temperature, precipitable water vapor (PWV), and cloud liquid water (CLW). The retrieved LSE are preliminarily validated by the simulations of Community Radiative Transfer Model (CRTM) and two LSEs show a determined parameter (R2) of 0.81 at 18.7 GHz.

FY-3B is the second meteorological satellite of China's FY3 (FengYun 3) series which was launched on November 5, 2010. There is a highly sensitive microwave radiometer on board the FY-3B satellite which is called the Microwave Radiation Imager (MWRI). The soil moisture product retrieved from the FY-3B/MWRI data provides global observations of land surface soil moisture. It provided very useful information during drought and flood monitoring. In order to validate this soil moisture product in China region, we tried to collect ground measured soil moisture data from the meteorological sites. However the spatial range of the data can only cover part area of China main land. In addition, the minimum depth of these data was 10cm which is not considered as the most suitable soil layer in the validation of remotely sensed soil moisture. Therefore we collected the CLDAS soil moisture analysis product which is derived from the China Meteorological Administration (CMA) Land Data Assimilation System, CLDAS-V1.0. The CMA Land Data Assimilation System is based on EnKF and land process models. The CLDAS soil moisture analysis product covered the whole China area with a spatial resolution of 1/16 ° × 1/16 °. This paper will give the comparison between these two products during the year 2012 and 2013.

Chinese and Japanese researchers established a joint project in 2000 to set up ground observation stations along dust source areas, transportation roads, and precipitation areas by collecting TSP (dry dust precipitation) and AVHRR data to retrieve albedo (surface energy). The selected data from retrieved albedo temporal imagery are used to construct LST/TSP/albedo curves. Finally a comparison was made between albedo curves and TSP curves. The result showed that there were good correlation between these two kinds of curves. It was proved that the albedo could be one of the physical parameters for predicting dust storm in future monitoring systems.

We introduce a four-stage process for urban construction edge detection using IKONOS images. The four stages include: (1) binary image processing, (2) pixel swapping by using different kennels to separate pixels according to the gray level of the image, in this paper 8 adjacent kennels are used, (3) interactive analyzing and selecting numbers representing edges, (4) using many edge images based on image gray level, in this paper we introduce three gray level edge detection processes.

This paper introduces our recent research on using remote sensing technology to identify features of locust breeding habitats in reed vegetation in the Dagang reservoir area of Tianjin, which involved the calibration and verification work in advance of a potential operational system. The general objectives of the study were: (1) to elaborate a reliability testing of LAI/Biomass and field spectral based vegetation estimation specifically in relation to migratory locust densely populated breeding areas; (2) to calibrate and compare the TM NDVI and ARVI vegetation index data which was acquired during the same period as the field data collection; (3) to generate a classification scheme for the use and interpretation of greenness in relation to migratory locust habitats in eastern China.

IHS transform was one of typical method for remote sensing data fusion. In recent years, newly developed method taking the advantage of IHS and Wavelet algorithms makes image fusion. In this case after the Wavelet substitution based on pixels or features, and then transforms inversely. In this paper we introduces a high frequency substitution method to improve spatial resolutions. The procedure of the method introduced as flowchart, in which the dot line area is our newly added method. The result was used in making 1:50000 scale NDVI imagery for monitoring land cover change in Minjiang River, Sichuan province, China and for providing information monitoring of Return Farmland Back to Forest or Grassland Project.