Exploiting Sentinel-1 polarized data for the classification of areas and time intervals where coherently apply a change detection method for the retrieval of superficial soil moisture at the field scale

2021 ◽  
Author(s):  
Giulia Graldi ◽  
Alfonso Vitti
Keyword(s):  
Soil Moisture ◽  
Change Detection ◽  
Agricultural Area ◽  
Time Interval ◽  
Maximum Difference ◽  
Time Intervals ◽  
In Situ Data ◽  
Area Of Interest

<p>The estimation of superficial soil moisture is performed with a Change Detection (CD) method applied over an agricultural area in Spain, in the basin of the Duero river. The CD method is applied on Sentinel-1 SAR images over a time period of three years. For the period  and area of interest are available in situ soil moisture measurments of the REMEDHUS network belonging to the International Soil Moisture Network (ISMN). Two years of data are used for the calibration procedure (2018 and 2019), one year (2020) for validation purposes.<br>According to the Corine Land Cover classification of 2018, the agricultural area is mainly coverd by low vegetation. The backscatterd SAR signal is indeed modelled as the inchoerent sum of the volumetric contribution of the canopy, and the soil attenuated contribution.<br>The Sentinel-1 VH polarized band is used for the classification of the areas with homogeneous volumetric contribution, where the condition of constant vegetation contribution is respected in order to apply the CD method. Furthermore, those areas will be identified exploiting the bimodal distribution of the VH band histogram in the upper phase of the vegetative stage of the crop.<br>The soil roughness contribution to the superficial component of the backscattered signal couldn’t be neglected due to agricultural practices such as tillage and harvesting. Furthermore the data are processed at a very high resolution, in order to exploit the full spatial resolution of the SAR data. The VV polarized band will be used to identify the variations of the SAR signal due to changes in the soil roughness, and time periods with constant roughness contribution will be identified in order to apply the CD method. It is expected that the variations of the VV backscattering coefficient due to changes in soil roughness are higher than the ones caused by soil moisture changes, except for meteoric events.<br>The CD is thus applied on areas and time intervals where only soil moisture content is supposed to vary, and the maximum variation is calculated in each time interval. Finally, the calculation of the soil moisture is performed by scaling the maximum difference of SAR signal with the maximum difference of the in situ data.<br>In previous studies performed on the same area, a SAR vegetation index was used to classify homogeneous volumetric contribution, and soil rougness was neglected. Even if the trend of the solution fits well the precipitation events and the trend of the in situ data (RMSE=0.096m<sup>3</sup>/m<sup>3</sup>, R=0.583m<sup>3</sup>/m<sup>3</sup>), the results presented singularities. The above presented method for the superficial soil moisture calculation is expected to smooth the singularities present in the results of our previous studies.</p>

scholarly journals Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

2020 ◽  
Author(s):  
Juan Carlos Laso Bayas ◽  
Linda See ◽  
Hedwig Bartl ◽  
Tobias Sturn ◽  
Mathias Karner ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Survey Data ◽  
Target Location ◽  
Low Cost ◽  
Mobile App ◽  
Sample Survey ◽  
In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

scholarly journals Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

2020 ◽  
Vol 12 (4) ◽  
pp. 650
Author(s):  
Pablo Sánchez-Gámez ◽  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Marcos Portabella
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
Brightness Temperature ◽  
European Space Agency ◽  
Ground Truth ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
In Situ Data ◽  
L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

scholarly journals Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Land ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 446
Author(s):  
Juan Carlos Laso Bayas ◽  
Linda See ◽  
Hedwig Bartl ◽  
Tobias Sturn ◽  
Mathias Karner ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Survey Data ◽  
Target Location ◽  
Mobile App ◽  
Sample Survey ◽  
High Quality ◽  
In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of large amounts of high quality, spatially explicit field data at a low cost.

The International Soil Moisture Network: an open-source data hosting facility in support of hydrological research

2020 ◽  
Author(s):  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Luca Zappa ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Open Source ◽  
Measurement Techniques ◽  
European Space Agency ◽  
Web Portal ◽  
Soil Moisture Data ◽  
In Situ Data ◽  
Spatial Coverage

<p><span>The International Soil Moisture Network (ISMN, </span><span></span><span>) is an international cooperation to establish and maintain an open-source global data hosting facility, providing in-situ soil moisture data as well as accompanying soil variables. This database is an essential means for validating and improving global satellite soil moisture products as well as land surface -, climate- , and hydrological models.</span></p><p><span>For hydrological validation, the quality of used in-situ data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collect their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.</span></p><p><span>The ISMN was created to address the above-mentioned issues. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free. </span></p><p><span>Since its establishment in 2009 and with continuous financial support through the European Space Agency (ESA), the ISMN evolved into a widely used in situ data source growing continuously (in terms of data volume and users). Historic measurements starting in 1952 up to near–real time are available through the ISMN web portal. Currently, the ISMN consists of 60 networks with more than 2500 stations spread all over the globe. With a </span><span><span>steadily growing user community more than 3200 registered users strong</span></span><span> the value of the ISMN as a well-established and rich source of in situ soil moisture observations is well recognized. In fact, the ISMN is widely used in variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). </span></p><p> <span>Our partner networks range from networks with a handful of stations to networks that are composed of over 400 sites, are supported with half yearly provider reports on statistical data about their network (e.g.: data download statistic, flagging statistic, etc.). </span></p><p><span>About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (GROW observatory ) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.</span></p><p><span>In this session , we want to give an overview of the ISMN, its unique features and its support of data provider, who are willing to openly share their data, as well as hydrological researcher in need of freely available datasets.</span></p>

scholarly journals Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt

2021 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Brian K Hornbuckle ◽  
Witold F. Krajewski ◽  
Ricardo Mantilla ◽  
Michael H. Cosh
Keyword(s):  
Soil Moisture ◽  
Vegetation Indices ◽  
Hydrologic Model ◽  
Corn Belt ◽  
Row Crops ◽  
In Situ Data ◽  
Row Crop ◽  
In Situ Sensors ◽  
Crop Agriculture

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

scholarly journals The global SMOS Level 3 daily soil moisture and brightness temperature maps

2017 ◽  
Vol 9 (1) ◽  
pp. 293-315 ◽  
Author(s):  
Ahmad Al Bitar ◽  
Arnaud Mialon ◽  
Yann H. Kerr ◽  
François Cabot ◽  
Philippe Richaume ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Global Analysis ◽  
Time Lag ◽  
Retrieval Algorithm ◽  
Limiting Factor ◽  
In Situ Data ◽  
Single Orbit ◽  
Level 3

Abstract. The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:[email protected]/Land_products/GRIDDED/L3SM/RE04/.

scholarly journals A dynamic approach for evaluating coarse scale satellite soil moisture products

2010 ◽  
Vol 7 (5) ◽  
pp. 7263-7303 ◽  
Author(s):  
A. Loew ◽  
F. Schlenz
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Sampling Error ◽  
Coarse Scale ◽  
Dynamic Approach ◽  
In Situ Data ◽  
Novel Approach ◽  
Area Sampling ◽  
Satellite Product

Abstract. Validating coarse scale remote sensing soil moisture products requires a comparison of gridded data to point-like ground measurements. The necessary aggregation of in situ measurements to the footprint scale of a satellite sensor (>100 km2) introduces uncertainties in the validation of the satellite soil moisture product. Observed differences between the satellite product and in situ data are therefore partly attributable to these aggregation uncertainties. The present paper investigates different approaches to disentangle the error of the satellite product from the uncertainties associated to the up-scaling of the reference data. A novel approach is proposed, which allows for the quantification of the remote sensing soil moisture error using a temporally adaptive technique. It is shown that the point-to-area sampling error can be estimated within 0.0084 [m3/m3].

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