The Medium-Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ)instrument aboard China’s Fengyun-3D satellite shares similarities with NASA’s Moderate Resolution Imaging Spectroradiometer(MODIS)sensor,enabling the retrieval of g...The Medium-Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ)instrument aboard China’s Fengyun-3D satellite shares similarities with NASA’s Moderate Resolution Imaging Spectroradiometer(MODIS)sensor,enabling the retrieval of global aerosol optical depth(AOD).However,no officially released operational MERSI-Ⅱ aerosol products currently exist over the ocean.This study focuses on adapting the MODIS dark target(DT)ocean algorithm to the MERSI-Ⅱ sensor.A retrieval test is conducted on the 2019 MERSI-Ⅱ data over the global ocean,and the retrieved AODs are validated against ground-based measurements from the automatic Aerosol Robotic Network(AERONET)and the shipborne Maritime Aerosol Network(MAN).The operational MODIS DT aerosol products are also used for comparison purposes.The results show that MERSI-Ⅱ AOD granule retrievals are in good agreement with MODIS products,boasting high correlation coefficients(R)of up to 0.96 and consistent spatial distribution trends.Furthermore,the MERSI-Ⅱ retrievals perform well in comparison to AERONET and MAN measurements,with high R-values(>0.86).However,the low-value retrievals from MERSI-Ⅱ tend to be slightly overestimated compared to MODIS,despite both AODs displaying a positive bias.Notably,the monthly gridded AODs over the high latitudes of the northern and southern hemispheres suggest that MERSI-Ⅱ exhibits greater stability in space and time,effectively reducing unrealistically high-value noise in the MODIS products.These results illustrate that the MERSI-Ⅱ retrievals meet specific accuracy requirements by maintaining the algorithmic framework and most of the algorithmic assumptions,providing a crucial data supplement for aerosol studies and climate change.展开更多
The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instrumen...The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.展开更多
Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing...Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.42471424,41975036,and 42075132)the Fengyun Application Pioneering Project(Grant No.FY-APP024)+1 种基金the State Key Project of National Natural Science Foundation of China-Key projects of joint fund for regional innovation and development(Grant No.U22A20566)the Scientific and Technological Innovation Team of Universities in Henan Province(Grant No.22IRTSTHN008).
文摘The Medium-Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ)instrument aboard China’s Fengyun-3D satellite shares similarities with NASA’s Moderate Resolution Imaging Spectroradiometer(MODIS)sensor,enabling the retrieval of global aerosol optical depth(AOD).However,no officially released operational MERSI-Ⅱ aerosol products currently exist over the ocean.This study focuses on adapting the MODIS dark target(DT)ocean algorithm to the MERSI-Ⅱ sensor.A retrieval test is conducted on the 2019 MERSI-Ⅱ data over the global ocean,and the retrieved AODs are validated against ground-based measurements from the automatic Aerosol Robotic Network(AERONET)and the shipborne Maritime Aerosol Network(MAN).The operational MODIS DT aerosol products are also used for comparison purposes.The results show that MERSI-Ⅱ AOD granule retrievals are in good agreement with MODIS products,boasting high correlation coefficients(R)of up to 0.96 and consistent spatial distribution trends.Furthermore,the MERSI-Ⅱ retrievals perform well in comparison to AERONET and MAN measurements,with high R-values(>0.86).However,the low-value retrievals from MERSI-Ⅱ tend to be slightly overestimated compared to MODIS,despite both AODs displaying a positive bias.Notably,the monthly gridded AODs over the high latitudes of the northern and southern hemispheres suggest that MERSI-Ⅱ exhibits greater stability in space and time,effectively reducing unrealistically high-value noise in the MODIS products.These results illustrate that the MERSI-Ⅱ retrievals meet specific accuracy requirements by maintaining the algorithmic framework and most of the algorithmic assumptions,providing a crucial data supplement for aerosol studies and climate change.
文摘目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D 1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%~36.9%可提高到融合后69.3%~80.7%,融合结果与TPDC LST的空间决定系数为0.503~0.787,均方根误差为3.6~5.8 K,其中晴空为2.6~4.9 K,云下为4.1~6.1 K;分析还表明目前官网产品FY-3D/MERSI和MWRI LST均存在缺值较多与精度偏低等问题,显示其存在较大改进潜力,这有利于进一步改进FY-3D LST融合质量。
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)。
文摘The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.
基金Supported by the Science Foundation of Shandong(ZR2017MD018)Key Research and Development Program of Ningxia(2019BEH03008)+3 种基金Open Research Project of the Key Laboratory for Meteorological Disaster MonitoringEarly Warning and Risk Management of Characteristic Agriculture in Arid Regions(CAMF-201701 and CAMF-201803)Arid Meteorological Science Research Fund Project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of China Metrological Administration(IAM201801)Science Foundation of Ningxia(NZ12278)。
文摘Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.
