Arctic sea ice leads, which are narrow linear openings between sea ice, are critical to polar climate and ocean–atmosphere interactions. However, detecting multi-width leads accurately under the case of cloud interference remains challenging for thermal infrared (TIR) data. In this article, we propose an improved U-Net-based model–CTMLU-Net–which is designed to extract multi-width leads, including internal bright leads, from FY-3D MERSI-II 250-m TIR imagery under varied cloudy conditions. The model incorporates a dynamic fast Fourier transform (FFT) module to distinguish leads from clouds in the frequency domain and introduces cloud categories into the training labels to improve the lead detection accuracy. A dual-loss module and multi-window brightness temperature anomaly (BTA) inputs further enhance the sensitivity to different lead widths. Under cloudy conditions, CTMLU-Net achieved a precision of 86.82% and a recall of 71.73%, outperforming the traditional BTA method by 39.26% and 23.95%, respectively. For multi-width leads, it achieved a precision of 92.10% and a recall of 87.73%, and for bright leads, the corresponding scores were 90.59% and 84.75%, respectively. Compared to the original U-Net model, the FFT module improved the precision and recall by 4.80% and 17.66% in cloudy conditions, respectively. Compared to single-loss setting, the dual-loss module further enhanced the multi-width lead detection precision and recall by 23.64% and 9.92%. CTMLU-Net was also applied to 7045 scenes of MERSI-II TIR images to generate monthly Arctic lead frequency maps from November 2019 to April 2020. The spatiotemporal patterns of the leads can capture dynamic events, such as the collapse of the Beaufort High in spring 2020. Overall, CTMLU-Net offers a robust and accurate solution for multi-width sea ice lead detection under cloudy conditions.

The third generation Microwave Temperature Sounder (MWTS-III) onboard Fengyun-3E (FY-3E) can obtain the atmospheric temperature profile in the early morning time, which is valuable for numerical weather prediction (NWP), climate analysis and other environment studies. However, calibrated brightness temperature (BT) of the surface-insensitive sounding channels (i.e. channels 7-17) have a sea-land contrast bias. It is 0.2 K-0.3 K for channels 9-17 and -1.33 K and -2.86 K for channels 7 and 8, respectively. The bias characteristic of channels 9-17 is similar to that of the corresponding channels of FY-3C, which is caused by the interference from a window channel and can be corrected by empirical method. Moreover, the spectral response functions of channels 7 and 8 were examined over a wider frequency range by measuring the instrument of similar design and construction to the on-orbit MWTS-III. The measured data shows that channels 7 and 8 have out-of-band response located between 50 GHz to 52 GHz, which close to the response range of channel 4. A linear function developed in this letter is effective for the BT bias correction. The sea-land contrast bias of these two channels has been decreased to -0.35K and -0.16K. And the daily mean bias of channels 7 and 8 are improved from -1.8K and -2.3K to -0.5K and -0.4K, respectively. This correction function has been integrated into the FY-3E MWTS-III operational calibration program in December 7, 2022. Meanwhile, the subsequent MWTS-III has been further improved to avoid the sea-land contrast bias.

The challenges of utilizing the 250-m resolution thermal infrared (TIR) data obtained from the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Chinese Fengyun-3D (FY-3D) satellite are bowtie effect and nonuniform brightness stripe noise. While previous solutions have addressed these issues separately, this article introduced a more integrated two-step image quality enhancement strategy for MERSI-II TIR images. It considered the interactions between the two issues and overcame the excessive or inadequate destriping in existing models due to the ideal stripe-type assumption. Specifically, for the bowtie effect, a rigorous geometric model suitable for MERSI-II was constructed by considering the Earth’s curvature and adjusting the preset image width. For the nonuniform brightness stripe noise, a novel adaptive multiscale frequential (AMSF) algorithm was developed. The multiscale spectral detection effectively captured the anomaly frequency, and the adaptive threshold dynamically adjusted the detection range, profiting in preserving details. The proposed strategy was validated on MERSI-II TIR images, outperforming existing methods in quantitative and qualitative assessments with higher efficiency on both bowtie effect and stripe noise removal. Further experiments conducted on Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrated the AMSF algorithm’s applicability to different data. In addition, the 250-m MERSI-II FY-3D data can help us understand the rapid variations of Arctic sea ice leads, which are key features within the sea ice. Using the quality-enhanced images to extract the sea ice leads in winter Arctic Baffin Bay improved the overall accuracy from 0.88 to 0.95, thereby providing more accurate and reliable sea ice lead data.

A re-calibration algorithm of MWRI (Microwave Radiation Imager) onboard FengYun series satellites was developed based on the error budget analysis of MWRI and the main calibration parameters simulation and iteration. SNO (Simultaneous Nadir Overpass) between MWRI and GMI were used to get the brightness temperature of stable radiation target including sea surface and rain forest. Based on those brightness temperature datasets of stable target, DD (double difference) were used with RTTOV simulation result to cancel the effect of different sensor parameters between MWRI and GMI. Finally, emissivity of hot reflector, back lobe of hot reflector, efficiency of hot load, non-linearity parameter of MWRI were simulated and corrected. Using the corrected parameters, re-calibration of MWRI was done and we get the 10 years’ time series dataset. Validation result shows that the dataset has very high accuracy and stability compared with operational dataset.

The third generation of MicroWave Temperature Sounder (MWTS-III) is a major payload of FengYun-3F (FY-3F) satellite, which was launched on August 3, 2023. It can provide valuable atmospheric temperature profiles in morning time for numerical weather prediction (NWP) applications. The prelaunch thermal vacuum (TVAC) calibration test was performed to examine the instrument quality. This paper briefly describes of the instrument design of MWTS-III and details the TVAC tests calibration methodology and radiometric performance, including sensitivity, striping, nonlinearity, and calibration accuracy. Channels 1 and 2, which are received by direct-detection receivers, have a noise equivalent delta temperature (NEDT) of less than 0.2K, weak correlations with other channels, striping indexes (SI) of 1.0 and 1.2, small peak nonlinearity and calibration residual errors within ±0.03K. The NEDT for Channels 3-17 is 0.16 K-1.38 K. The inter-channel correlation and striping phenomena are more significant for these 15 channels. Since the linear calibration deviations were well mitigated by the nonlinear calibration, the calibration residual errors for channels 3-11 are within ±0.05 K and for channels 12-17 are within ±0.17 K. The predicted on-orbit calibration accuracy for all channels are between 0.37 K and 1.48K. In summary, all channels of FY-3F MWTS-III meet the specifications, and the instrument has great application potential.

The second-generation Microwave Humidity Sounder (MWHS-2) onboard Fengyun (FY)-3C has a data quality comparable to that of counterpart microwave moisture sounders. However, channels 13 and 14 have large biases that are negatively correlated with the instrument temperature and prevent the operational assimilation of the data of these two channels. To better understand the biases of channels 13 and 14, the correlation of the observation minus simulation data (O–B bias) of different channels with the instrument temperature and other factors is investigated for FY-3C MWHS-2 in this article. A sensitivity analysis using a gradient boosting decision tree indicates that the instrument temperature and scan position are the two dominant factors, with total bias-contribution scores exceeding 0.8 for both channels 13 and 14. This conclusion is verified through further analysis of the bias distributions for the scan position, scene brightness temperature, and ascending/descending orbits. Using 12-week data recorded in 2016, the correlations of the O–B bias with the scan position and instrument temperature are specifically investigated and a correction algorithm is formulated, with which data for 2016 and 2017 are corrected. The corrected data in both channels have smaller, more stable biases, and are less affected by the instrument temperatures and scan position. The bias contributions associated with these two factors should thus be further studied.

The spatial extent and duration of soil freeze/thaw (F/T) control water and heat exchange, the energy cycle, and climate change. Global warming causes permafrost thawing, which increases carbon emissions and in turn exacerbates climate change. Passive microwave remote sensing has been proven to be effective in monitoring land surface F/T. However, it was found that the applicability of existing passive microwave remote sensing-retrieved F/T products in large-scale areas (such as the Qinghai-Tibetan Plateau (QTP)) was influenced by some landscape factors, such as the arid climate type and terrain elevation gradient. FY-3 series satellites have accumulated nearly 10 years of passive microwave data, but there is little work based on FY-3 passive microwave data to see its potential in land surface F/T status monitoring. In this work, we proposed a dynamic method to determine the surface F/T status by combining the edge detection method and discriminant function algorithm from FY-3B X-and Ka-band microwave radiation imager (MWRI) data. Comparing the results against three F/T products based on in situ 5 cm soil temperature, we demonstrate that this algorithm performs best over different validation areas with an overall accuracy of 86.5%. More specifically, the new algorithm improved the accuracy of current F/T products in arid and semiarid regions from 73% to 90%. Additionally, the spatial distribution of frozen days over the QTP of 2018 based on the new algorithm has good consistency with the permafrost map. However, the accuracy is influenced by snowmelt and appears to be overestimated for thaw soil during the day. This algorithm performs well in QTP areas with complex topography and climate types and holds the promise of providing users with highly accurate F/T products on larger and even global scales.

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature ( TB) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI TBto continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height ( H) for vegetation effects correction. The Qpmodel, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m3·m-3. Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.

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.

Microwave radiation imager (MWRI) is regarded as one of the most important microwave payloads on board China FengYun-3C Meteorological Satellite. The instrument suffers from calibration anomalies and exhibits observation- background (O-B) calibration bias difference between the ascending and descending passes at all channels (hereinafter AD bias). The calibration bias difference of MWRI between ascending and descending orbits hampers data assimilation in the numerical weather predictions and reanalysis systems. This paper proposes a physical-based correction algorithm for MWRI calibration, following a brief introduction to the calibration process of the instrument. The relationship between the observed brightness temperatures and the physical temperature of the hot load reflector is established to mitigate the intrusion of the emissive hot reflector at all channels which was not accurately estimated in the previous calibration process. Before- and after-correction comparisons using one-year observations show that the AD bias is effectively reduced, i.e., from ~2 K before correction to less than 0.2 K after correction, when rectifying the emissivity of the hot reflector in the calibration equation, whereas the change in the mean values of MWRI radiance is negligible.

The main objective of this work is to improve the snow depth (SD) estimation for the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMI/S) in China. To avoid systematic bias of different sensors to brightness temperature, it's better to retrieve SD with similar sensors. Meanwhile, long-term dataset is essential, which has significant impact on climate, weather and water resources. Sensors (SSM/I, SSMI/S) that span from 1987 to present are optimal for long time series of SD product generation. However, the accuracy of current SD (SWE) product still can't meet demand of climate and hydrological models. In this study, each grid-cell SD was estimated as the sum of SDs from each land cover algorithm weighted by percentages of land cover types. Through evaluation of this algorithm using measurements from 2005-2006, the root mean square errors (RMSE) are about 3.2, 2.9 and 5.3 cm for farmland, grassland and forest respectively. However, for mixed pixels, the RMSE is 6.6 cm. Finally, reasons about poor performance for mixed pixels were discussed. For further work, incorporating 10 GHz and fractional snow cover (FSC) is expected to improve SD retrievals in China.

The MicroWave Radiation Imager (MWRI) onboard FengYun-3C (FY-3C) Satellite is a total-power radiometer observing earth surface and atmosphere at 10.65, 18.7, 23.8, 36.5 and 89 GHz with dual polarization. The on-orbit test of FY-3C MWRI was performed shortly after the launch of platform. Sensor performance factors include receiver sensitivity black body stability and scan period stability were tested. Beside those factors, many detail instrument parameters were also tested. Results indicate good performance of FY-3C MWRI. All of the factors are stable. Cross calibration between FY-3C MWRI\FY-3B MWRI\SSMIS\TMI were also performed during the onorbit test. The cross area include north/south polar, land and sea. The largest bias is 3.4K, which is the difference between FY-3C and SSMIS at sea surface area. Most of other biases are between 1K and 2K.

In recent years, drought occurs frequently in China. Soil moisture, as a key parameter in the drought monitoring, becomes especially concerned. The FY-3B/MWRI soil moisture product provides global observations of land surface soil moisture. This paper gives a basic introduction of FY-3B/MWRI soil moisture product including the retrieval method used. Then an example in the application for drought monitoring will be demonstrated and finally we will evaluate the utility of MWRI soil moisture for drought monitoring.1

Microwave Radiation Imager (MWRI) onboard the FengYun (FYJ-3A/B satellites observes the Earth atmosphere at 10.65, 18.7, 23.8, 36.5 and 89.0 GHz with each having dual polarization. Its calibration system is uniquely designed with a main reflector viewing both cold and hot calibration targets. Two quasi-optical reflectors are used to reflect the radiation from hot load and cold space to the main reflector. Soon after FY3b was successfully launched in November 2010, evaluation of on-orbit calibration accuracy of MWRI was carried out. In this paper, CRTM was used to simulate the MWRI observations over ocean, by using GDAS data as model inputs. "O-B" Results and "Double difference " results show that: 1). the on-orbit calibration status of MWRI is stable, 2). the brightness temperatures from MWRI observation are highly consistent with those derived from AMSR-E and model simulation.

This paper presents a method (drought index) to monitor the drought process in China. The method based on the relationship between drought and soil moisture change. Although the absolute value of soil moisture from AMSR-E land product is not accurate enough, but we can use the soil moisture change information in the temporal scale. The soil moisture product of AMSR-E from 2002 to present is used to build up the drought index. In each EASE-GRID grid, the smallest soil moisture value and the largest soil moisture value in the 8 years are defined to dry/wet edge points and the drought index of them are 0/100. With the two edge points, we can get drought index in each grid, each day. Using the former method, we represent 4 typical drought processes in China. Result show that the drought index from AMSR-E soil moisture product can brought out the development and distribution of drought process roughly.

The estimation of soil wetness variations is of considerable importance to improve the reliability of flood warning. In this paper, Polarization Ratio Variation Index (PRVI) is presented which, on the basis of long-term multi-temporal Special Sensor Microwave/Image (SSM/I) data, can monitor soil wetness variations at a large scale. Nearly 18-years long time series PRVI are calculated based on the SSM/I data in Huaihe River Basin of China for the 1988-2005 period. Preliminary results achieved for the several important flooding events in Huaihe River Basin from 1988 to 2005 years are described, which confirms the reliability of proposed method.

In this paper, we carried out a case study with a multidirectional and hyperspectral data, CHRIS (the Compact High Resolution Imaging Spectrometer) on board platform PROBA (Project for On Board Autonomy). After orthocorrection and atmospheric correction, we analyzed the directional reflectance (HDRF) of a dense conifer forest to get two CSI, i.e. effective value of re-collision probability, pr, and escape probability R1at different angles. These two invariants at all five angles were regressed to LVIS_H100 data, which is relevant to canopy height, and demonstrated a comparable statistics to the regression with spectral reflectances. In this sense, CSI method can act as an effective tool to reduce the dimension of hyperspectral remote sensing data by a ratio of N/2 (N is the number of spectral bands). Re-collision probability, pr, and escape probability R1can be understood as two principle components in PCA, however, they are superior to principle components because the transformation is based on radiation transfer physics and reduction result have explicit interpretation. In similar consideration to reduce the redundancy as above, we also calculated the deviation of each spectral band to the fitted value from prand R1and make a statistics for all the sampling pixels. We picked out the best fitted bands, as most of them locate in NIR range, we employed statistics to determine which to supplement in visible range, and discussed the potential of these bands to be selected as optimal VNIR bands combination of future vegetation sensor design.

In this paper, we present a simple but efficient algorithm to derive land surface temperature (LST). The major point of our algorithm is to build liner regress equations between brightness temperature of AMSR-E and LST product from MODIS. Each equation corresponds to a group of land surface cells, within which the emissivities are similar. We found that for a given land surface type and a given latitude range, land surface emissivities are comparatively steady within a period (one month for example). In this way, we can separate all of the AMSR-E brightness temperature and MODIS LST product data into groups by land surface type, latitude range and date range. In each group, the regress equation can be build. We used 2004 data to build the regress equations and validated those used 2004 and 2006 data. We found that: 1, in snow covered area the accuracy of retrieved LST is poor. 2, in desert, accuracy of retrieval LST is also poor. 3, in other most land cover, retrieved LST by AMSR-E fit well with the MODIS LST product.

Abstract—A microwave scattering model based on second-order solution of radiative transfer equation has been developed for dry snow. Advanced integral equation model (AIEM) and a semi-empirical model were included in the model to account for ground contribution. Also, ellipsoid grain shape was adopted to describe ice particle. This model was compared with the ground-based scatterometer data (frequencies are 1.25GHz and 15.5 GHz) from NASA Cold-land Processes Field Experiment (CLPX). Inputs to the model were from Local-Scale Observation Site (LSOS) snow pit measurements, except that particle size and shape were computed as free parameters. The comparison shows that the model agrees well with the field data. Also from the comparison, it could be seen that particle shape had a significant effect on the cross-polarization signals. Keywords- snow; second-order model; CLPX I.

The purpose of this paper is to utilize the standard atmospheric simulation method to evaluate the influence of water content for practical split-window algorithm proposed by us. The computation results indicate that the accuracy of our algorithm is about 0.37°C when the transmittance is computed from the simulation water content. In order to evaluate the practical of our algorithm, the analysis of water content influenced the transmittance were made and the results indicate that our algorithm is not sensitivity to water content. The average LST error is changed between 0.19~1.1°C when the water content error in the simulation standard atmosphere changes between - 80~130%. In order to confirm the conclusion, we retrieve the land surface temperature through change the water content retrieved from MODIS data. We retrieve LST from MODIS image data through changing retrieval water content error and got a similar conclusion mentioned above. However, the accuracy can be improved when the water content error between - 80%~0%. Finally, the analysis results from mathematics method view indicate that the water content decreases the sensitivity of transmittance about 5-6 times. All the analysis indicates that our algorithm is practical and not sensitive to water content. The accuracy can be improved by utilizing reasonable prior knowledge of retrieval water content. Keywords-LST, Water Content, MODIS data

This paper presents a study to demonstrate the potential of a spaceborne K/sub u/-band APR (active phase-array radar) - Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to monitor snow cover in Tibetan Plateau. A method based on the difference of backscattering from snow covered ground with zenithal/non-zenithal incidence angles was carried out and validated by a second-order RT model. In the end, we use the TRMM/PR data from 2003-01 to 2004-04 to do the snow monitor of several areas in Tibetan Plateau.

Abstract-Seasonal snow cover plays a significant role in regional, large even global scale climate processes, hydrological cycle and surface thermal balance processes. MODIS snow cover methods provide an operational model for mapping each pixel into snow or no-snow by using a normalized difference snow Index (NDSI) and its threshold test. But they cannot provide the fractional snow cover information. This paper presents an automated snow-mapping technique at sub-pixel resolution in Tibetan Plateau based on MODIS normalized difference snow fraction and normalized difference Vegetation fraction. This paper presents an automated snow-mapping technique at sub-pixel resolution based on MODIS normalized difference snow Index (NDSI) and normalized Difference Vegetation Index(NDVI) in order to avoid overestimations due to vegetation cover. And it takes the ASTER data as ground true data to verified this method. Keywords-MODIS, ASTER, sub pixel snow mapping, Tibetan plateau I.

The ERS-1/2 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to estimate effective surface reflectivity (related to soil moisture content) over bare soil using this instrument, an original methodology based on the integral equation model (IEM) is presented that takes advantage of multiple view angular observations which means two independent observations at the same resolution cell and same time. The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period (IOP '98) field campaign in 1998 of the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment Tibet (GAME/Tibet). The retrieved values obtained for the bare surface are consistent with ground measurements collected in these areas.