Total ozone depletion over Greece as deduced from satellite observations

The reprocessed (version 7) daily total ozone observations made by the Total Ozone Mapping Spectrometer (TOMS) on the Nimbus-7 satellite over Athens (37.6 N, 23.4 E) for the period from November 1978 until April 1993 have been used to investigate total ozone depletion. To estimate the trends in total ozone content a linear fitting to the data has been applied, given that the other components like the quasi-biennial oscillation, the El Nino/Southern Oscillation and the solar cycle have a very small contribution to the total ozone depletion effects over that geographical region. The total ozone depletion over the 15-year period was derived from version 7 shows a strong seasonal variation from more than 6% in winter and early spring to about 1.5% in summer. The total ozone depletion over Greece is found to be about 1% (per decade) less using version 7 than using version 6.     

Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSM measurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. These model coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSM measurements demonstrated that SSM variation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419–0.379, root mean square error: 0.046–0.051 m³ m^?3, Bias: ?0.020 to ?0.025 m³ m^?3) was found for the mid-morning model and the daytime model with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614–0.675), a better accuracy (Bias: 0.032–0.044 m³ m^?3, RMSE: 0.043–0.050 m³ m^?3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research.     

卷积神经网络提取风云影像土壤湿度

目的 时空分辨率较高的土壤湿度数据对于生产实践和科学研究具有重要意义。以国产的风云气象卫星为数据源,利用卷积神经网络自主学习输入变量间深层关联的优势,获取高质量土壤湿度数据,为科学研究和生产实践服务。方法 首先构建了一个土壤湿度提取卷积神经网络（soil moisture convolutional neural network,SMCNN）,SMCNN由温度子网络和土壤湿度子网络构成,每个子网络均包含特征提取器和编码器。特征提取器用于为每个像素生成一个特征向量,其中温度子网络的特征提取器由11个卷积层组成,湿度子网络的特征提取器由9个卷积层组成,卷积层均使用1×1的卷积核。编码器用于将提取到的特征拟合为目标变量。两个子网络均使用平均方差作为损失函数。使用随机梯度下降算法对模型进行训练,最后利用训练好的模型提取区域土壤湿度数据。结果 选择宁夏回族自治区为实验区,利用获取的2016-2019年风云3D影像和相应地面站点数据作为实验数据,选择线性回归模型、BP（back propagation）神经网络模型作为对比模型开展数据实验,选择均方根误差作为评价指标。实验结果表明,SMCNN的均方根误差为0.006 7,优于对比模型,SMCNN模型在从风云影像中提取土壤湿度方面具有优势。结论 本文利用卷积神经网络分别构建用于反演地表温度和土壤湿度的子网络,再组成一个完整的土壤湿度反演网络结构,从风云3D数据中获取数值精度、时空分辨率均较高的土壤湿度数据,满足了科学研究和生产实践对大范围高精度土壤湿度数据的需求。     

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.     

Global vegetation dynamics: satellite observations over Asia

Abstract

The weekly global vegetation index (GVI) derived from the NOAA AVHRR instrument has been analysed for the 1982-1985 period over a wide range of vegetation formations of Asia. Temporal development curves of the index are presented for environments ranging from the desert of central Asia to the tropical forest of Borneo. The paper shows that, despite the coarse resolution of the GVI product, a large set of useful information on ecosystem dynamics and cropping practices can be consistently derived from time series of such data. In addition, it is shown that the impact of the 1982-1983 El Nino Southern Oscillation-related drought can be detected in the GVI data through an analysis of anomalies in the development of selected vegetation formations. The relevance of such analysis for global vegetation monitoring and change detection is then underlined.     

Aircraft scatterometer observations of soil moisture on rangeland watersheds

Extensive studies conducted by several researchers using truck-mounted active microwave sensors have shown the sensitivity of these instruments to soil-moisture variations for agricultural land covers. The logical extension of these results is the evaluation of similar systems at lower resolutions typical of operational systems and for other types of land cover. Data collected during a series of aircraft flights in 1978 and 1980 over four rangeland watersheds located near Chickasha, Oklahoma, were analysed in this study. These data included scatterometer measurements made at 1·6 and 4·75 GHz using a NASA aircraft and ground observations of soil moisture for a wide range of moisture conditions. Data were compared with previous truck and aircraft results. Results indicate that if the sensor system is calibrated it is capable of providing estimates of surface soil moisture for the rangeland conditions tested.     

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil-vegetation-atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6-22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.     

Geostationary satellite detection of bio mass burning in South America

Abstract

This paper presents the results of using Geostationary Operational Environmental Satellite (GOES) Visible Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) data to monitor biomass burning associated with deforestation and grassland management in South America. The technique of Matson and Dozier has been adapted to GOES VAS short-wave and long-wave infrared window data to determine ihe size and temperature of fires associated with these activities. Although VAS data do not offer the spatial resolution available with Advanced Very High Resolution Radiometer (AVHRR) data (7 km versus I km) this decreased resolution does not seem to hinder the ability of the VAS instrument to delect fires; in some cases it proves to be advantageous, in that saturation does not occur as often. Sequences of VAS visible data are helpful in verifying that the hot spots sensed in the infrared are actually related to fires. Furthermore, the smoke of the fires can be tracked in time to determine their motion and trajectory. In this way, the GOES satellite offers a unique ability to monitor diurnal variations in fire activity and transport of related aerosols.     

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.     

An evaluation of AMSR-E derived soil moisture over Australia

This paper assesses remotely sensed near-surface soil moisture over Australia, derived from the passive microwave Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument. Soil moisture fields generated by the AMSR-E soil moisture retrieval algorithm developed at the Vrije Universiteit Amsterdam (VUA) in collaboration with NASA have been used in this study, following a preliminary investigation of several other retrieval algorithms. The VUA-NASA AMSR-E near-surface soil moisture product has been compared to in-situ soil moisture data from 12 locations in the Murrumbidgee and Goulburn Monitoring Networks, both in southeast Australia. Temporally, the AMSR-E soil moisture has a strong association to ground-based soil moisture data, with typical correlations of greater than 0.8 and typical RMSD less than 0.03 vol/vol (for a normalized and filtered AMSR-E timeseries). Continental-scale spatial patterns in the VUA-NASA AMSR-E soil moisture have also been visually examined by comparison to spatial rainfall data. The AMSR-E soil moisture has a strong correspondence to precipitation data across Australia: in the short term, maps of the daily soil moisture anomaly show a clear response to precipitation events, and in the longer term, maps of the annual average soil moisture show the expected strong correspondence to annual average precipitation.     

Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.     

Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing

Upscaling of sparse in situ soil moisture (SM) observations is essential for the validation of current and upcoming space-borne SM retrievals, and the successful application of SM observations in hydrological models or data assimilation. In this study, we construct a novel method based on Bayesian data fusion to upscale in situ SM observations to the coarse scale of microwave remote sensing. In the framework of Bayesian theory, the valuable auxiliary information obtained in Moderate Resolution Imaging Spectroradiometer (MODIS) apparent thermal inertia (ATI) is integrated into the upscaling process. The method is validated using SM wireless sensor network data in the Tibetan plateau, which covers an area of approximately 30 × 30 km² with 20 in situ stations. Results confirm that the upscaled SM using the method with randomly selected three stations from the 20 stations is extremely close to the mean of the 20 SMs. The mean root mean square error (RMSE) between the upscaled SM and the mean of the 20 in situ SMs was 0.02 m³ m^?3, and the max RMSE was less than 0.05 m³ m^?3. Furthermore, the sensitivity of the upscaling accuracy to the number of in situ observations is discussed. When the number of in situ observations is greater than nine, the increasing accuracy of the Bayesian method is limited by the uncertainty in the ATI of the remote sensing.     

Radar estimates of soil moisture over the Konza Prairie

Abstract

Radar backscatter measurements were made as a part of the First International satellite land surface climatology project Field Experiment (FIFE) to estimate soil moisture for use by other investigators. The helicopter-mounted radar was flown along selected transects that coincided with soil moisture measurements. The radar operated at microwave frequencies of 5-3 and 9 6 GHz and at selected incidence angles between 0° and 60°. Vertical polarization was used for two days in June of 1987 and horizontal polarization was used for three days in July and October of 1987.

The scattering coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression, R², ranged between 0·9 and 0·5. The larger coefficients were for X -band measurements made at large antenna incidence angles, and the smaller coefficients were for C-band measurements made: at incidence angles near vertical.     

Assessment of the AMSR-E soil moisture product over India

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales, controlling the exchange of water and energy between the atmosphere and land surface. Satellite-based microwave radiometric observations are considered to be the best for soil moisture remote sensing because of their high sensitivity, as well as their all-weather and day–night observation capabilities with high repeativity. In this study, an attempt has been made to assess the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-EOS) soil moisture product over India. The AMSR-E soil moisture product has been assessed using in situ soil moisture observations made by the India Meteorological Department (IMD) during the monsoon period (May–August) for the years 2002–2006 over 18 meteorological stations. Apart from assessing AMSR-E soil moisture retrieval accuracy, this study also investigates the effect of vegetation, topography and coastal water contamination, and determines the regions where the AMSR-E soil moisture product could be useful for different applications.     

Influence of forest cover fraction on L-band soil moisture retrievals from heterogeneous pixels using multi-angular observations

Airborne L-band data from the Australian National Airborne Field Experiment 2005 (NAFE '05) field campaign were used to investigate the influence of fractional forest cover on soil moisture retrievals from heterogeneous (grass/forest) pixels. This study is, to our knowledge, the first to use experimental data on this subject and was done in view of the SMOS mission, in order to contribute to calibration/validation studies and the analysis of heterogeneous surfaces. Because the multi-angle observations were contained in swaths, swaths were used instead of pixels as the basic surface unit in this study. Simultaneous retrievals of soil moisture (SM) and vegetation optical depth (τ_NAD) were undertaken by inversion of the L-MEB zero-order radiative transfer model. This was done for two different retrieval configurations, the first consisting of swath-effective values of SM and τ_NAD and the second consisting of values of SM and τ_NAD for the non-forested (i.e. grass) fraction of the swath, with forest emission known from forward modelling. Model inputs for non-retrieved parameters were either default values taken from the literature or site- and time-specific values obtained from observations of nearby homogeneous swaths gathered during the same flight. The main focus of this study was on retrieval behaviour for various soil moisture conditions and forest fractions. Area-averaged retrieval results were generally very reasonable for both retrieval configurations. When retrieving swath-effective values of SM and τ_NAD, τ_NAD showed an increased overestimation with increased forest fraction. Highest retrieved values of SM were found at intermediate values of forest fraction. The results show the difficulty in flagging upper limits of pixel forest fraction during soil moisture retrievals, besides the fact that erroneous parameter values can lead to high errors in retrieved SM, especially in wet conditions. This study is the first to give a realistic idea of the errors and uncertainties involved in soil moisture retrievals from partly forested swaths, and as such will contribute to a better understanding of SMOS calibration/validation issues.     

Determination of the soil moisture recession constant from satellite data: a case study of the Yucatan peninsula

Estimation of the recession constant for soil moisture can assist in soil and water management. This article estimates soil moisture recession velocity from satellite data, thereby taking advantage of extensive data coverage in a metric that is more commonly used with point data for rivers. Retrieval from satellites of the surface soil moisture has produced global coverage of multiannual time series data, thereby allowing the application of techniques that require long time series of daily data. We applied two techniques from river hydrology to soil moisture data from the advanced scatterometer aboard the meteorological operational satellite: (1) baseflow separation; and (2) master recession curve (MRC) with the correlation method. The former filtered the data and extracted those for the base soil moisture (BSM), which is considered the water that circulates in the soil by capillarity. The latter technique allowed the estimation of recession constants by the extraction of continuously decreasing BSM segments. The use of MRC for a large range of BSM provides a recession constant representative of all the moisture decrease for each pixel, thereby permitting the identification of drought-sensitive zones. The recession constant, a metric that had not been used for soil moisture, allowed us to determine potential temporal evolution of drought in the Yucatan peninsula. Government agencies could use the approach applied in this study to improve water management and drought prevention.     

The use of a microwave backscatter model for retrieving soil moisture over bare soil

Abstract

Most attempts at predicting soil moisture from C-band microwave backscattering coefficients for bare soil are made by fitting experimental calibration relations obtained for limited ranges of incidence angle and soil surface roughness. In this paper, a more general approach is discussed using an inversion procedure to extend the use of a single experimental calibration relation to a wider range of incidence angle and surface roughness. A correcting function is proposed to normalize the backscattering coefficients to the conditions (incidence angle and surface roughness) of the calibration relation. This correcting function was derived from simulated data using the physical optics or KirchhofTs scatter model using the scalar approximation. Before discussing the inversion procedure, the backscattering coefficients calculated by the model have been compared with experimental data measured in the C-band, HH polarization and three incidence angles (Θ= 15°, 23°, 50°) under a wide range of surface soil moisture conditions (0.02H_v  0.35cm³ cm^-3) and for a single quite smooth soil surface roughness (0–011 _s  OOI4/n)m. The model was found to be experimentally validated from 15° to 23° of incidence and for surface soil moistures higher than 0-I0cm³cm^-3. For the inversion procedure, it is assumed to have a wider range of validity (15°  Θ 35° ) for ihc incidence angle. A sensitivity analysis of the model to errors on roughness parameter and incidence angle was performed in order to assess the feasability and suitability of the described inversion procedure.     

On the correction of the total ozone content over Athens,Greece as deduced from satellite observations

The observations with the Total Ozone Mapping Spectrometer ( TOMS) mounted aboard the Nimbus-7 satellite have previously been used to determine the trends of the total ozone amount over Athens, Greece ( 38° N, 24° E), since 1979, for various months ( Varotsos, C. A., and Cracknell, A. P., 1993, International Journal of Remote Sensing, 14, 2053–2059). The total ozone depletion over the 13-year time period showed a strong seasonal variation of the trend from more than 7 per cent in winter to about 2·5 per cent in summer. However, the TOMS instrument measures the back-scattered ultraviolet radiation in order to determine ozone content and is limited to observations above the cloud level. ln the presence of thick cloud the column ozone content is generally underestimated. This underestimation of the total ozone amount is quantitatively examined, especially in the synoptic cases where ozone-rich air has been transported into the lower troposphere. The influence of this underestimation on the total ozone depletion over Athens, Greece, deduced from TOMS observations, is finally attempted.     

Technical note Remote sensing of soil moisture content over bare soil at microwave frequencies

Abstract

Results of radiometric measurements over bare soil obtained with horizontally polarized microwave radiometers at 1·55 and 19·1 GHz are presented. The observed normalized brightness temperatures were used to estimate the soil moisture content using the radiative transfer model. It is found that the r.m.s. difference between observed and estimated soil moisture content is comparable to the standard deviation found in ground measurement of soil moisture content.     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.