Remote sensing of rainfall at high spatial-temporal resolution through soil moisture

2020 ◽  
Author(s):  
Paolo Filippucci ◽  
Luca Brocca ◽  
Angelica Tarpanelli ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Temporal Frequency ◽  
Recent Decade ◽  
Rain Gauge ◽  
Fusion Algorithm ◽  
Space Applications ◽  
Soil Moisture Data

<p>In order to enhance our understanding of the hydrologic cycle, frequent, reliable and detailed information on precipitation are fundamental. In-situ measurements are the traditional source of this information, but they have limited spatial representativeness and the number of stations worldwide is declining and their access is often troublesome. Satellite products are able to overcome these issues and actually are the main, if not the only, source of information over many areas of the world. Notwithstanding this, the spatial resolution is still limited to tens or hundreds of kilometers, limiting their usefulness for hydrological applications. In the recent decade, a new approach for estimating rainfall from satellite-derived soil moisture observations has been proposed, named SM2RAIN (Brocca et al., 2014) and based on the inversion of the soil water balance equation. The application of SM2RAIN to Sentinel-1 satellites carrying a C-band Synthetic Aperture Radar (CSAR) sensor can provide rainfall data at unprecedented spatial and temporal resolution.</p><p>In this study, we combined the soil moisture data retrieved from backscatter observations of Sentinel-1 (1.5/4 days temporal frequency over Europe, 500 m sampling) with the soil moisture data obtained from ASCAT sensor, onboard of METOP satellites (8-24 h temporal frequency, 12.5 km sampling) through a data fusion algorithm. The result is an innovative soil moisture dataset with a temporal resolution of 1 day and a spatial resolution of 1 km (Bauer-Marschallinger et al., 2018). These data are used as input for SM2RAIN, obtaining as output a rainfall product with temporal and spatial sampling of 1 day and 1 km, respectively.</p><p>The approach was applied over test regions in Italy and Austria obtaining promising results. Specifically, the comparison with high density observations from raingauges and meteorological radars has allowed the assessment of the method at high spatial resolution and varying temporal resolution. Results show that good quality rainfall estimates at 1 km of spatial resolution can be obtained in reproducing 3- to 5-day rainfall accumulations. Further testing will be carried out in the next months and presented at the conference.</p><p><strong>Acknowlodgments</strong></p><p>The activity is funded by DWC radar project, Austrian Space Applications Programme, FFG Project 873658.</p><p><strong>Reference</strong></p><p>Bauer-Marschallinger, B., Paulik, C., Mistelbauer, T., Hochstöger, S., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L. & Wagner, W. (2018). Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing, 10(7), 1030. doi:10.3390/rs10071030</p><p>Brocca L., Ciabatta L., Massari C., Moramarco T., Hahn S., Hasenauer S., Kidd R., Dorigo W., Wagner W., Levizzani V. – “Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data”. J. Geophys. Res. Atmos. vol. 119, pp. 5128–5141, 2014. doi: 10.1002/2014JD021489</p>

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

scholarly journals A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2962 ◽  
Author(s):  
Dong Fan ◽  
Hua Wu ◽  
Guotao Dong ◽  
Xiaoguang Jiang ◽  
Huazhu Xue
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Daily Precipitation ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Hysteresis Phenomenon ◽  
Monthly Precipitation ◽  
Soil Moisture Data ◽  
Spatio Temporal ◽  
The Relationship

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

QA4SM – An online tool for satellite soil moisture data validation

2021 ◽  
Author(s):  
Samuel Scherrer ◽  
Wolfgang Preimesberger ◽  
Monika Tercjak ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Space Applications ◽  
Online Tool ◽  
Soil Moisture Data

<p>To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.</p><p>QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.</p><p>Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.</p><p>The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.</p><p>We present an overview and examples of the online tool, new features, and give an outlook on future developments.</p><p><em>Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).</em></p>

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

scholarly journals Soil moisture mapping over West Africa with a 30-min temporal resolution using AMSR-E observations and a satellite-based rainfall product

2009 ◽  
Vol 13 (10) ◽  
pp. 1887-1896 ◽  
Author(s):  
T. Pellarin ◽  
T. Tran ◽  
J.-M. Cohard ◽  
S. Galle ◽  
J.-P. Laurent ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Microwave Emission ◽  
Emission Model ◽  
Rainfall Estimation ◽  
Simple Method ◽  
Antecedent Precipitation ◽  
The Difference

Abstract. An original and simple method to map surface soil moisture over large areas has been developed to obtain data with a high temporal and spatial resolution for the study of possible feedback mechanisms between soil moisture and convection in West Africa. A rainfall estimation product based on Meteosat geostationary satellite measurements is first used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture maps at a spatial resolution of 10×10 km2 and a temporal resolution of 30-min. However, given the uncertainty of the satellite-based rainfall estimation product, the resulting soil moisture maps are not sufficiently accurate. For this reason, a technique based on assimilating AMSR-E C-band measurements into a microwave emission model was developed in which the estimated rainfall rates between two successive AMSR-E brightness temperature (TB) measurements are adjusted by multiplying them by a factor between 0 and 7 that minimizes the difference between simulated and observed TBs. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the method which was found to improve the soil moisture estimates on all three sites.

The Assimilation of Remote Sensing-Derived Soil Moisture Data into a Hydrological Model for the Mahanadi Basin, India

2019 ◽  
Vol 47 (8) ◽  
pp. 1357-1374 ◽  
Author(s):  
Soumya S. Behera ◽  
Bhaskar Ramchandra Nikam ◽  
Mukund S. Babel ◽  
Vaibhav Garg ◽  
Shiv Prasad Aggarwal
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Soil Moisture Data ◽  
Mahanadi Basin

scholarly journals Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

2018 ◽  
Vol 10 (12) ◽  
pp. 1950 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Nazzareno Pierdicca
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Added Value ◽  
Sar Images ◽  
Radar Images ◽  
Spatio Temporal ◽  
Set Up ◽  
The Impact ◽  
Hydrological Applications

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps.

scholarly journals Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1038 ◽  
Author(s):  
Mario Guallpa ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Time Resolution ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Tropical Andes ◽  
The Andes ◽  
Significant Difference ◽  
The Impact

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

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