Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ

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.     

Comparisons of AGRI/FY-4A Cloud Fraction and Cloud Top Pressure with MODIS/Terra Measurements over East Asia

Fengyun-4 A(FY-4 A), the second generation of China's geostationary meteorological satellite, provides high spatiotemporal resolution cloud products over East Asia. In this study, cloud fraction(CFR) and cloud top pressure(CTP)products in August 2017 derived from the Advanced Geosynchronous Radiation Imager(AGRI) aboard FY-4 A(AGRI/FY-4 A) are retrospectively compared with those from the Moderate Resolution Imaging Spectroradiometer(MODIS) aboard Terra(MODIS/Terra) over East Asia. To avoid possible errors in the comparison caused by the lower temporal coverage of MODIS/Terra products compared to that of AGRI/FY-4 A over the same region and to account for time lags between observations of the two instruments, we construct datasets of AGRI/FY-4 A CFR and CTP to match those of MODIS/Terra in each scan over East Asia in August 2017. Results show that the CFR and CTP datasets of the two instruments generally agree well, with the linear correlation coefficients(R) between CFR(CTP)data of 0.83(0.80) regardless of time lags. Though longer time lags contribute to the worse consistency between CFR(CTP) data derived from observations of the two instruments in most cases, large CFR/CTP discrepancies do not always match with long time lags. Large CFR discrepancies appear in the vicinity of the Tibetan Plateau(TP;28°–45°N, 75°–105°E). These differences in the cloud detection by the two instruments largely occur when MODIS/Terra detects clear-sky while AGRI/FY-4 A detects higher values of CFR, and this accounts for 61% of the CFR discrepancy greater than 50% near the TP. In the case of CTP, the largest discrepancies appear in the eastern Iranian Plateau(IP; 25°–45°N, 60°–80°E), where there are some samples with long time lags(20–35 min) and fewer daily data samples are available for computing monthly means compared to other regions since there are many clearsky data samples there during the study period.     

Synergistic use of AIRS and MODIS for dust top height retrieval over land

It is nontrivial to extract the dust top height(DTH) accurately from passive instruments over land due to the complexity of the surface conditions. The Moderate Resolution Imaging Spectroradiometer(MODIS) deep blue(DB) algorithm can be used to infer the aerosol optical depth(AOD) over high-reflective surfaces. The Atmospheric Infrared Sounder(AIRS) can simultaneously obtain the DTH and optical depth information. This study focuses on the synergistic use of AIRS observations and MODIS DB results for improving the DTH by using a stable relationship between the AIRS infrared and MODIS DB AODs. A one-dimensional variational(1DVAR) algorithm is applied to extract the DTH from AIRS. Simulation experiments indicate that when the uncertainty of the dust optical depth decreases from 50% to 20%, the improvement of the DTH retrieval accuracy from AIRS reaches 200 m for most of the assumed dust conditions. For two cases over the Taklimakan Desert, the results are compared against Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP) measurements. The results confirm that the MODIS DB product could help extract the DTH over land from AIRS.     

Can MODIS Detect Trends in Aerosol Optical Depth over Land?

The Moderate Resolution Imaging Spectroradiometer(MODIS) sensor onboard NASA's Aqua satellite has been collecting valuable data about the Earth system for more than 14 years, and one of the benefits of this is that it has made it possible to detect the long-term variation in aerosol loading across the globe. However, the long-term aerosol optical depth(AOD)trends derived from MODIS need careful validation and assessment, especially over land. Using AOD products with at least 70 months' worth of measurements collected during 2002–15 at 53 Aerosol Robotic Network(AERONET) sites over land,Mann–Kendall(MK) trends in AOD were derived and taken as the ground truth data for evaluating the corresponding results from MODIS onboard Aqua. The results showed that the AERONET AOD trends over all sites in Europe and North America, as well as most sites in Africa and Asia, can be reproduced by MODIS/Aqua. However, disagreement in AOD trends between MODIS and AERONET was found at a few sites in Australia and South America. The AOD trends calculated from AERONET instantaneous data at the MODIS overpass times were consistent with those from AERONET daily data, which suggests that the AOD trends derived from satellite measurements of 1–2 overpasses may be representative of those from daily measurements.     

An overview of MODIS radiometric calibration and characterization

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for NASA’s Earth Observing System (EOS), currently operating on both the Terra and Aqua satellites. The MODIS is a major advance over the previous generation of sensors in terms of its spectral, spatial, and temporal resolutions. It has 36 spectral bands: 20 reflective solar bands (RSB) with center wavelengths from 0.41 to 2.1 μm and 16 thermal emissive bands (TEB) with center wavelengths from 3.7 to 14.4 μm, making observations at three spatial resolutions: 250 m (bands 1–2), 500 m (bands 3–7), and 1km (bands 8-36). MODIS is a cross-track scanning radiometer with a wide field-of-view, providing a complete global coverage of the Earth in less than 2 days. Both Terra and Aqua MODIS went through extensive pre-launch calibration and characterization at various levels. In orbit, the calibration and characterization tasks are performed using its on-board calibrators (OBCs) that include a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a v-grooved flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch characterization and in-orbit operation. Key issues discussed in this paper include in-orbit efforts of monitoring the noise characteristics of the detectors, tracking the solar diffuser and optics degradations, and updating the sensor’s response versus scan angle. The experiences and lessons learned through MODIS have played and will continue to play major roles in the design and characterization of future sensors.     

Assessing Disagreement and Tolerance of Misclassification of Satellite-derived Land Cover Products Used in WRF Model Applications

As more satellite-derived land cover products used in the study of global change,especially climate modeling,assessing their quality has become vitally important.In this study,we developed a distance metric based on the parameters used in weather research and forecasting(WRF) to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme(IGBP) classification scheme.We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spectoradiometer(MODIS) products,and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model.Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes,while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes.The degree of disagreement varied significantly among seven regions of China.The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly.High accuracy and fuzzy agreement occurred in the following classes:water,grassland,cropland,and barren or sparsely vegetated.Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals.Some classes need to be improved further;the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling.Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.     

Air Temperature Estimation with MODIS Data over the Northern Tibetan Plateau

Time series of MODIS land surface temperature(T_s) and normalized difference vegetation index(NDVI) products,combined with digital elevation model(DEM) and meteorological data from 2001 to 2012,were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau(NTP). A time series analysis and a regression analysis of monthly mean land surface temperature(T_s) and air temperature(T_a) were conducted using ordinary linear regression(OLR) and geographical weighted regression(GWR). The analyses showed that GWR,which considers MODIS T_s,NDVI and elevation as independent variables,yielded much better results [R_(Adj)~2 0.79; root-mean-square error(RMSE) =0.51℃–1.12℃] associated with estimating T_a compared to those from OLR(R_(Adj)~2= 0.40-0.78; RMSE = 1.60℃–4.38℃).In addition,some characteristics of the spatial distribution of monthly T_a and the difference between the surface and air temperature(T_d) are as follows. According to the analysis of the 0℃ and 10℃ isothermals,T_a values over the NTP at elevations of 4000–5000 m were greater than 10℃ in the summer(from May to October),and T_a values at an elevation of3200 m dropped below 0℃ in the winter(from November to April). T_a exhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP,T d values in other areas were all larger than 0℃ in the winter.     

The Regional Surface Heating Field over the Heterogeneous Landscape of the Tibetan Plateau Using MODIS and In-Situ Data

In this study,a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape.As a case study,the methodology was applied to the whole Tibetan Plateau (TP) area.Four images of MODIS data (i.e.,30 January 2007,15 April 2007,1 August 2007,and 25 October 2007) were used in this study for comparison among winter,spring,summer,and autumn.The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform (TORP).The results show the following:(1) The derived surface heating field for the TP area was in good accord with the land-surface status,showing a wide range of values due to the strong contrast of surface features in the area.(2) The derived surface heating field for the TP was very close to the field measurements (observations).The APD (absolute percent difference) between the derived results and the field observations was <10%.(3) The mean surface heating field over the TP increased from January to April to August,and decreased in October.Therefore,the reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology.The limitations and further improvement of this method are also discussed.     

On the Nature of Caspian Clouds

Caspian clouds(CCs) are formed between the southern coast of the Caspian Sea and the Alborz Mountains. The purpose of this study is to identify characteristics of CCs using aerosol, cloud, and meteorological data from ModernEra Retrospective analysis for Research and Applications version 2(MERRA-2), Moderate Resolution Imaging Spectroradiometer(MODIS), and ECMWF Reanalysis version 5(ERA5) during 2000–2020. During this period, we identified and investigated 636 days with CCs. The results indicate...     

MODIS brightness temperature data assimilation under cloudy conditions Ⅱ: Impacts on rainstorm forecasting

Although satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall, they have not been fully taken advantage of in data assimilation of numerical weather predictions, especially those in infrared channels. Assimilating radiances is common under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky. Based on the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance (GRAPES-3DVar), cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system. This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV) and adjust the atmospheric and cloud parameters based on infrared radiance observations. In this paper, we investigate a heavy rainfall over Guangdong province on May 26, 2007, which is right after the onset of a South China Sea monsoon. In this case, channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) for observing water vapor (Channel 27) and cloud top altitude (Channel 36) are selected for the assimilation. The process of heavy rainfall is simulated by the Weather Research and Forecasting (WRF) model. Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field. The tendency of adjustment agrees well with the satellite observations. The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.     

Snow Albedo’s Dependence on Solar Zenith Angle from In Situ and MODIS Data

Since snow cover is one of the fastest modi- fications to the land surface albedo, the treatment of snow-covered surface albedo is important for the simulation of land processes in weather and climate models. A simple formulation is developed here to represent the solar zenith angle (SZA) dependence of albedo under maximum snow cover condition on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) algorithm. The SZA dependence of black-sky (or direct) albedo is weaker under snow condition than that under snow-free condition, and it does not differ much among different vegetation types. The blue-sky albedo (or combined albedo from direct and diffuse radiations) based on the above formulation and in situ diffuse ratio of solar radiation is consistent with in situ data from two Canadian sites (grassland and evergreen needleleaf forest) and one U.S. grassland site. In particular, the SZA dependence of blue-sky snow albedo is almost always weak because of high diffuse ratios for high SZA in winter. With the snow albedo formulation from this study and snow-free albedo formulations from the authors’ previous studies, albedos with partial snow cover can be obtained as the snow fraction-weighted average of snow and snow-free albedos. Citation: Wang, Z., and X. Zeng, 2008: Snow albedo’s dependence on solar zenith angle from in situ and MODIS data, Atmos. Oceanic Sci. Lett., 1, 45-50     

Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS:Application to a Dust Storm over East Asia in 2011

Aerosol optical depth(AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols,and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3 A(FY-3 A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3 A/MERSI(Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3 A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.     

A synergic algorithm for retrieval of aerosol optical depth over land

In this paper, a novel algorithm for aerosol optical depth(AOD) retrieval with a 1 km spatial resolution over land is presented using the Advanced Along Track Scanning Radiometer (AATSR) dual-view capability at 0.55, 0.66 and 0.87 μm, in combination with the Bi-directional Reflectance Distribution Function (BRDF) model, a product of the Moderate Resolution Imaging Spectroradiometer (MODIS). The BRDF characteristics of the land surface, i.e. prior input parameters for this algorithm, are computed by extracti...     

Observational Diagnosis of Cloud Phase in the Winter Antarctic Atmosphere for Parameterizations in Climate Models

The cloud phase composition of cold clouds in the Antarctic atmosphere is explored using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments for the period 2000--2006. We used the averaged fraction of liquid-phase clouds out of the total cloud amount at the cloud tops since the value is comparable in the two measurements. MODIS data for the winter months (June, July, and August) reveal liquid cloud fraction out of the total cloud amount significantly decreases with decreasing cloud-top temperature below 0oC. In addition, the CALIOP vertical profiles show that below the ice clouds, low-lying liquid clouds are distributed over ～20% of the area. With increasing latitude, the liquid cloud fraction decreases as a function of the local temperature. The MODIS-observed relation between the cloud-top liquid fraction and cloud-top temperature is then applied to evaluate the cloud phase parameterization in climate models, in which condensed cloud water is repartitioned between liquid water and ice on the basis of the grid point temperature. It is found that models assuming overly high cut-offs (》-40oC) for the separation of ice clouds from mixed-phase clouds may significantly underestimate the liquid cloud fraction in the winter Antarctic atmosphere. Correction of the bias in the liquid cloud fraction would serve to reduce the large uncertainty in cloud radiative effects.     

Estimation of Land Surface Temperature over the Tibetan Plateau Using AVHRR and MODIS Data

Estimation of large-scale land surface temperature from satellite images is of great importance for the study of climate change. This is especially true for the most challenging areas, such as the Tibetan Plateau (TP). In this paper, two split window algorithms (SWAs), one for the NOAA’s Advanced Very High Resolu-tion Radiometer (AVHRR), and the other for the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied to retrieve land surface temperature (LST) over the TP simultaneously. AVHRR and M...     

Assessing MODIS Land Cover Products over China with Probability of Interannual Change

Accurate and up-to-date land cover data are important for climate-change modeling. Quality assessment is becoming critical, as many satellite-based land cover products of differing scales have been released to meet the needs of scientific studies. In this study, the authors assessed the Moderate Resolution Imaging Spectroradiometer（MODIS） land cover products by analyzing the probability of interannual change from 2001 to 2012. The authors found that, cumulatively, 43.0% of MODIS land cover had changed over China from 2001 to 2012 at least once. Of this percentage, 12.1% was considered unreasonable change, 6.1% was considered reasonable change, and areas of confusion accounted for about 24.8%, giving rise to great uncertainty in the products. MODIS Collection 51 products clearly have less uncertainty than the Collection 5 products. Areas of reasonable change occurred in transition zones of ecological, biophysical, and climate gradients, while areas of unreasonable change appeared in heterogeneous landscapes. The misclassifications at three spatial scales of horizontal grids used in regional climate models occurred largely in the heterogeneous landscapes, and the areal percentage of misclassification decreased with larger horizontal grid spacing. In addition, the misclassifications in MODIS products often occurred among specific classes, which are geographically, ecologically, and spectrally similar, with low discriminative spectral-temporal signals. The effect of classification uncertainty should be made known, and further improvements are still needed for application in regional climate models. The authors＇ findings have important implications for better understanding the uncertainties of MODIS land cover products, and for improving the land surface parameterization for regional climate models.     

Evaluation of Cloud Top Height Retrievals from China's Next-Generation Geostationary Meteorological Satellite FY-4A

To evaluate the validity of cloud top height(CTH) retrievals from FY-4A, the first of China's next-generation geostationary meteorological satellite series, the retrievals are compared to those from Himawari-8, CloudSat,Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO), and Moderate Resolution Imaging Spectroradiometer(MODIS) operational products from August to October 2017. Regarding CTHs from CloudSat,CALIPSO, and MODIS as truth, the results show that the performance of FY-4A CTH retrievals is similar to that of Himawari-8. Both FY-4A and Himawari-8 retrieve reasonable CTH values for single-layer clouds, but perform poorly for multi-layer clouds. The mean bias error(MBE) shows that the mean value of FY-4A CTH retrievals is smaller than that of Himawari-8 for single-layer clouds but larger for multi-layer clouds. For ice crystal clouds, both FY-4A and Himawari-8 obtain the underestimated CTHs. However, there is a tendency for FY-4A and Himawari-8 to overestimate the CTH values of CloudSat and CALIPSO mainly for low level liquid water clouds. The temperature inversion near the tops of water clouds may result in an overestimation of CTHs. According to the MBE change with altitude, FY-4A and Himawari-8 overestimate the CTHs mainly for clouds below 3 km, and the overestimation is slightly more apparent in Himawari-8 data than that in FY-4A values. As the cloud optical thickness(COT) increases,the CTH bias of FY-4A CTH retrievals gradually decreases. Two typical cases are analyzed to illustrate the differences between different satellites' CTH retrievals in detail.     

Impacts of snow cover on vegetation phenology in the arctic from satellite data

The dynamics of snow cover is considered an essential factor in phenological changes in Arctic tundra and other northern biomes. The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite data were selected to monitor the spatial and temporal heterogeneity of vegetation phenology and the timing of snow cover in western Arctic Russia (the Yamal Peninsula) during the period 2000-10. The magnitude of changes in vegetation phenology and the timing of snow cover were highly heterogeneous across latitudinal gradients and vegetation types in western Arctic Russia. There were identical latitudinal gradients for "start of season" (SOS) (r2 = 0.982, p<0.0001), "end of season" (EOS) (r2 = 0.938, p<0.0001), and "last day of snow cover" (LSC) (r2 = 0.984, p<0.0001), while slightly weaker relationships between latitudinal gradients and "first day of snow cover" (FSC) were observed (r2 = 0.48, p<0.0042). Delayed SOS and FSC, and advanced EOS and LSC were found in the south of the region, while there were completely different shifts in the north. SOS for the various land cover features responded to snow cover differently, while EOS among different vegetation types responded to snowfall almost the same. The timing of snow cover is likely a key driving factor behind the dynamics of vegetation phenology over the Arctic tundra. The present study suggests that snow cover urgently needs more attention to advance understanding of vegetation phenology in the future.