Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

Kalman filter based estimation of differential hardware biases with triangular interpolation technique for IRNSS

Ionosphere delay correction is the main error correction to the computation of single frequency user position using satellite navigation. However ionosphere delay consists of not only delay but also frequency dependent differential hardware biases from satellite and receiver ends. For ionosphere point of view, Indian Regional Navigation Satellite System (IRNSS) service area comes in equatorial anomaly region. It is a unique satellite navigation system which operates at L5 and S frequencies and consists of Geostationary Earth Orbit (GEO) and Geo Synchronous Orbit (GSO) satellite constellation. With IRNSS measurements availability, there is a good opportunity to estimate and analyse differential hardware biases with GEO/GSO combination and with equatorial ionosphere variation. In this paper, Kalman filter based estimation with triangular interpolation technique is used to estimate differential hardware biases for all IRNSS satellites and reference receivers at L5 frequency. The standard deviation of the 15?days of daily estimation of satellite differential hardware biases is in the range of 0.32 to 1.17 TECU for all IRNSS satellites. Similarly, the standard deviation of the 15?days of daily estimation varies up to 2.85 and 6.0 TECU for receiver differential hardware biases during calm and stormy period respectively. The ionosphere delay computed using estimated differential hardware biases is compared with Global Ionosphere Map (GIM) data. A rigorous analysis is carried out to study the error in the estimation in terms of input data noise level, satellite constellation and effect of latitude. Our result reveals that over IRNSS service area, there is an exponential increase in the error in the estimation of receiver differential hardware biases with respect to latitude.     

Soil moisture estimation by using multipolarization SAR image

In the past studies, different soil moisture estimation models were developed for bare soil areas by using remotely-sensed data. However, there are few models that can be used to estimate soil moisture in vegetated areas. Water Cloud Model (WCM) model is a widely used soil moisture estimation model has been developed for vegetated areas. In this study, the WCM model was extended to take soil roughness parameter into consideration. The modeling and its accuracy assessment were done by using multi-polarization Airborne Synthetic Aperture Radar (AIRSAR) images and ground data collected during field Soil Moisture Experiments.     

Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.     

全球电离层地图TEC数据的插值算法

由IGS工作组提供的全球电离层地图(GIM)是电离层重要的应用数据.卫星高度计能够提供全球实时的电离层延迟误差校正.利用GIM数据,以Jason-3时空分辨率进行电离层总电子含量(TEC)的时间维度插值和空间维度插值,其中空间维度插值采用了Kriging插值和双线性插值两种方法.针对两种插值方法得到的总电子含量,与平滑...     

An improved approach for rainfall estimation over Indian summer monsoon region using Kalpana-1 data

In this paper, an improved Kalpana-1 infrared (IR) based rainfall estimation algorithm, specific to Indian summer monsoon region is presented. This algorithm comprises of two parts: (i) development of Kalpana-1 IR based rainfall estimation algorithm with improvement for orographic warm rain underestimation generally suffered by IR based rainfall estimation methods and (ii) cooling index to take care of the growth and decay of clouds and thereby improving the precipitation estimation.     

Estimation of vertically integrated water vapor in Hungary using MODIS imagery

Information about the amount and spatial structure of atmospheric water vapor is essential in understanding meteorology and the Earth environment. Space-borne remote sensing offers a relatively inexpensive method to estimate atmospheric water vapor in the form of integrated water vapor (IWV). The research activity reported in the present paper is based on the data acquired by the HRPT/MODIS (High Resolution Picture Transmission, MODerate resolution Imaging Spectroradiometer) receiving station established in Budapest (Hungary) by the Space Research Group of the Eötvös Loránd University. Integrated water vapor is estimated by the remotely sensed data of the MODIS instrument with different methods and also by the operational numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF). Radiosonde data are used to evaluate the accuracy of the different IWV fields though it has been pointed out that the in situ data also suffers from uncertainties. It was found that both the MODIS and the ECMWF based fields are of good accuracy. The satellite data represent finer scale spatial structures while the ECMWF data have a relatively poor spatial resolution. The high quality IWV fields have proved to be useful for radiative transfer studies such as the atmospheric correction of other satellite data from times different than the overpass times of satellites Terra/Aqua and the forecast times of the model data. For this purpose the temporal variability of IWV is scrutinized both using ECMWF and MODIS data. Taking advantage of Terra and Aqua overpasses, the mean rate of change of IWV estimated by the near infrared method was found to be 0.47 ± 0.45 kg m⁻² h⁻¹, while it was 0.13 ± 0.65 kg m⁻² h⁻¹ based on the infrared method. The numerical weather prediction model’s analysis data estimated −0.01 ± 0.13 kg m⁻² h⁻¹ for the mean growth rate, while using forecast data it was 0.24 ± 0.18 kg m⁻² h⁻¹. MODIS data should be used when available for the estimation of the IWV in other studies. If no satellite data are available, or available data are only from one overpass, ECMWF based IWV can be used. In this case the analysis fields (or the satellite field) should be used for temporal extrapolation but the rate of change should be calculated from the forecast data due to its higher temporal resolution.     

Assessment study of ionosphere correction model using single- and multi-shell algorithms approach over sub-Saharan African region

It is a known fact that ionosphere is the largest and the least predictable among the sources of error limiting the reliability and accuracy of Global Navigation Satellite Systems (GNSS) and its regional augmentation systems like Satellite Based Augmentation System (SBAS) in a safety-of-life application. The situation becomes worse in the Equatorial Ionization Anomaly (EIA) region, where the daytime ionization distribution is modified by the fountain effect that develops a crest of electron density at around ±15° to ±20° of the magnetic equator and a trough at the magnetic equator during the local noon hours. Related to this phenomenon is the appearance of ionosphere irregularities and plasma bubbles after local sunset. These may degrade further the quality of service obtained from the GNSS/SBAS system of the said periods. Considering the present operational augmentation systems, the accuracy and integrity of the ionosphere corrections estimate decreases as the level of disturbances increases. In order to provide a correct ionosphere correction to the user of GNSS operating in African EIA region and meet the integrity requirements, a certified ionosphere correction model that accurately characterizes EIA gradient with the full capacity to over-bound the residual error will be needed. An irregularities detector and a decorrelation adaptor are essential in an algorithm usable for African sub-Saharan SBAS operation. The algorithm should be able to cater to the equatorial plasma vertical drifts, diurnal and seasonal variability of the ionosphere electron density and also should take into account the large spatial and temporal gradients in the region. This study presents the assessment of the ionosphere threat model with single and multi-layer algorithm, using modified planar fit and Kriging approaches.     

A method in near-surface estimation of air temperature (NEAT) in times following the satellite passing time using MODIS images

Air temperature is one of the most important parameters in environmental, agricultural and water resources studies. This information is not usually always available at the required temporal and spatial resolution. The air temperature is measured at a fixed point in the meteorological stations which are dispersed and may not have the appropriate spatial resolution needed for many applications. On the other hand, MODIS satellite images have relatively acceptable spatial resolution specially for use in environmental studies. There is a methodology with which the near surface air temperature can be extracted from MODIS images at the satellite passing time with an acceptable accuracy. The goal in this study is to find a way to predict the air temperature in times after/before the satellite passing time. The procedure consists of two steps. In the first step, the relationship between the air temperature at a time in a synoptic station and the air temperature in other times up to 5 h later were modeled. In the second step, using these built up relationships, the air temperature extracted from the satellite image at the passing time was extrapolated to the next hours. Finally, the results of this extrapolation method were evaluated using the air temperatures measured at those hours and in the pixels containing some other meteorological stations. The error of the method when applied to a relatively homogeneous surface cover was about 1.5 °C. This error when applied to the next hours, was below 2 °C up to 5 h after satellite passing time. This method can be useful in some agricultural and horticultural applications in which both the spatial and temporal resolution are needed simultaneously. This product is a useful tool for frost prediction, a phenomenon that usually happens at night or early in the morning.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

基于相关功率修正的地基GNSS-R土壤湿度反演

利用全球导航卫星系统反射信号测量技术(GNSS-R)进行土壤湿度反演过程中，实际接收天线方向性会造成GNSS直反信号相关功率测量偏差。针对地基观测场景下天线方向性造成的相关功率的类余弦振荡问题，提出了基于多项式拟合的信号相关功率修正方法。为了验证所提方法的有效性，开展了地基GNSS-R土壤湿度观测实验，结果表明:基于多项式拟合的相关功率修正可以消除信号相关功率的类余弦振荡，提升GNSS-R土壤湿度反演中的观测数据有效性和反演结果准确性。      

Crop parameters estimation by fuzzy inference system using X-band scatterometer data

Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.     

Impact of four dimensional assimilation of satellite data on long-range simulations over the Indian region during monsoon 2006

A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.     

Refining DORIS atmospheric drag estimation in preparation of ITRF2008

In preparation of ITRF2008, all geodetic technique services (VLBI, SLR, GPS and DORIS) are generating new solutions based on combination of individual analysis centers solutions. These data reprocessing are based on a selection of models, parameterization and estimation strategy unique to each analysis center and to each technique. While a good agreement can be found for models between groups, thanks to the existence of the IERS conventions, a great diversity still exist for parameter estimation, allowing possible future improvements in this direction. The goal of this study is to focus on the atmospheric drag estimation used to generate the new DORIS/IGN ignwd08 time series prepared for ITRF2008. We develop here a method to inter-compare different processing strategies. In a first step, by analyzing single-satellite solutions for a few weeks of data but for a large number of possible analysis strategies, we demonstrate that estimating drag coefficient more frequently (typically every 1–2 h instead of previously every 4–8 h) for the lowest DORIS satellites (SPOTs and Envisat) provides better geodetic results for station coordinates and polar motion. This new processing strategy also solved earlier problem found when processing DORIS data during intense geomagnetic events, such as geomagnetic storms. Differences between drag estimation strategies can mostly be found during these few specific periods of extreme geomagnetic activity (few days per year). In such a case, when drag coefficient is only estimated every 6 h or less often for single-satellite solution, a significant degradation in station coordinate accuracy can be observed (120 mm vs. 20 mm) and significant biases arose in polar motion estimation (5 mas vs. 0.3 mas). In a second step, we reprocessed a full year of DORIS data (2003) in a standard multi-satellite mode. We were able to provide statistics on a more reliable data set and to strengthen these conclusions. Our proposed DORIS analysis is easy to implement in all software packages and is now already used by several analysis centers of the International DORIS Service (IDS) when submitting reprocessed solutions for ITRF2008.     

Evaluation of GPM sampling error over major basins in Indian subcontinent using bootstrap technique

The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications.     

Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas

Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Remote sensing based continuous hydrologic modeling

There are two principal ways in which remote sensing can be used with continuous hydrological models: (1) by providing a cost effective way for obtaining input data and (2) by providing synoptic measurements of various state variables. This paper discusses existing hydrologic models and the modifications required to adapt them for using remotely sensed data that may significantly improve their simulation performance. Microwave and thermal infrared measurements show promise for use in hydrologic models because they can measure certain physical properties of the watershed (emissivity and temperature) from which a hydrologic condition can be inferred. Additional applications of remote sensing data include the use of spatial data, mechanisms for extrapolating point data and direct measurement of several hydrologic state variables such as soil moisture, surface temperature, snow water equivalent, frozen soils, and rainfall distribution. Results are presented from several aircraft flights where thermal infrared and microwave data were collected over a small research basin. These results are discussed with respect to their application in continuous hydrologic simulation models.     

Dual-frequency carrier smoothed code filtering with dynamical ionospheric delay modeling

In GNSS applications, carrier-smoothed-code is a widely used technique to combine code pseudo-range and carrier phase measurements. A dynamical ionospheric delay modeling method is proposed based on Kalman filter and least-squares theory. The level of the process noise is adaptively tuned along with the real-time KF state estimation, based on the online variance component estimation method. Meanwhile, the correlations of the time differenced carrier phase measurements are considered. This approach avoids overly optimistically evaluating the estimate and improves the transient accuracy of the estimates. A real GPS dataset is employed to check the performance of the proposed method under different conditions. The results show that the new algorithm can model the ionospheric delay variation well with different sampling intervals or even in ionospheric abnormal environment. The positioning accuracy can be confirmed, about 21%, 35% and 16% better are obtained in the N, E, and U direction than raw dataset.