Spatial-temporal variations in near-surface soil freeze-thaw cycles in the source region of the Yellow River during the period 2002–2011 based on the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) data

Detecting near-surface soil freeze-thaw cycles in high-altitude cold regions is important for understanding the Earth＇s surface system, but such studies are rare. In this study, we detected the spatial-temporal variations in near-surface soil freeze-thaw cycles in the source region of the Yellow River（SRYR） during the period 2002–2011 based on data from the Advanced Microwave Scanning Radiometer for the Earth Observing System（AMSR-E）. Moreover, the trends of onset dates and durations of the soil freeze-thaw cycles under different stages were also analyzed. Results showed that the thresholds of daytime and nighttime brightness temperatures of the freeze-thaw algorithm for the SRYR were 257.59 and 261.28 K, respectively. At the spatial scale, the daily frozen surface（DFS） area and the daily surface freeze-thaw cycle surface（DFTS） area decreased by 0.08% and 0.25%, respectively, and the daily thawed surface（DTS） area increased by 0.36%. At the temporal scale, the dates of the onset of thawing and complete thawing advanced by 3.10（±1.4） and 2.46（±1.4） days, respectively; and the dates of the onset of freezing and complete freezing were delayed by 0.9（±1.4） and 1.6（±1.1） days, respectively. The duration of thawing increased by 0.72（±0.21） day/a and the duration of freezing decreased by 0.52（±0.26） day/a. In conclusion, increases in the annual minimum temperature and winter air temperature are the main factors for the advanced thawing and delayed freezing and for the increase in the duration of thawing and the decrease in the duration of freezing in the SRYR.

Advanced land observing satellite data to identify ground vegetation in a juniper forest,northeast Iran

Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

A Neural Network Method for Monitoring Snowstorm: A Case Study in Southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

A comparison of Argo nominal surface and near-surface temperature for validation ofAMSR-E SST

Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.

Comparison of TMI and AMSR-E sea surface temperatures with Argo near-surface temperatures over the global oceans

Satellite-derived sea surface temperatures（SSTs） from the tropical rainfall measuring mission（TRMM）microwave imager（TMI） and the advanced microwave scanning radiometer for the earth observing system（AMSR-E） were compared with non-pumped near-surface temperatures（NSTs） obtained from Argo profiling floats over the global oceans. Factors that might cause temperature differences were examined, including wind speed, columnar water vapor, liquid cloud water, and geographic location. The results show that both TMI and AMSR-E SSTs are highly correlated with the Argo NSTs; however, at low wind speeds, they are on average warmer than the Argo NSTs. The TMI performs slightly better than the AMSR-E at low wind speeds, whereas the TMI SST retrievals might be poorly calibrated at high wind speeds. The temperature differences indicate a warm bias of the TMI/AMSR-E when columnar water vapor is low, which can indicate that neither TMI nor AMSR-E SSTs are well calibrated at high latitudes. The SST in the Kuroshio Extension region has higher variability than in the Kuroshio region. The variability of the temperature difference between the satellite-retrieved SSTs and the Argo NSTs is lower in the Kuroshio Extension during spring. At low wind speeds, neither TMI nor AMSR-E SSTs are well calibrated, although the TMI performs better than the AMSR-E.

The GRAPES Variational Bias Correction Scheme and Associated Preliminary Experiments

The variational assimilation theory is generally based on unbiased observations. In practice, however, almost all observations suffer from biases arising from observational instruments, radiative transfer operator, precondition of data, and so on. Therefore, a bias correction scheme is indispensable. The current scheme for radiance bias correction in the GRAPES 3DVar system is an offline scheme. It is actually a static correction for the radiance bias before the process of cost function minimization. In consideration of its effects on forecast results, this kind of scheme has some shortcomings. Thus, this study provides a variational bias correction （VarBC） scheme for the GRAPES 3DVar system following Dee’s idea. In the VarBC scheme, the observation operator is modified and a new control variable is defined by taking the predictor coefficients as the control parameters. According to the feature of the GRAPES-3DVAR, an incremental formulation is applied and the original bias correction scheme is maintained in the actual process of observations. The VarBC is designed to co-exist with the original scheme, because it is a dynamic revision to the observational operator on the basis of the old method, i.e., it adjusts the model state vector along with the control parameters to an unbiased state in the process of minimization and the assimilation system remains consistent with available information automatically. Preliminary experimental results show that the mean departures of background-minus-observation and analysis-minus-observation are reduced as expected. In a case study of the heavy rainfall that happened in South China on 11–13 June 2008, the 500-hPa geopotential height is better simulated using the analyzed field from the VarBC as the initial condition.

Identifying AMSR-E radio-frequency interference over winter land

Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis （PCA） method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index （sensitive to RFI signal） and two scattering indices （sensitive to snow scattering）, are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the ＂true＂ RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals＇ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.