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The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study's main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases. [ABSTRACT FROM AUTHOR]

In recent years, the amount of water used for agricultural purposes has been rising due to an increase in food demand. However, anthropogenic water usage, such as for irrigation, is still not or poorly parameterized in regional- and larger-scale land surface models (LSMs). By contrast, satellite observations are directly affected by, and hence potentially able to detect, irrigation as they sense the entire integrated soil–vegetation system. By integrating satellite observations and fine-scale modelling it could thus be possible to improve estimation of irrigation amounts at the desired spatial–temporal scale. In this study we tested the potential information offered by Sentinel-1 backscatter observations to improve irrigation estimates, in the framework of a data assimilation (DA) system composed of the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a water cloud model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and leaf area index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast backscatter residuals back to updates in soil moisture and LAI through an ensemble Kalman filter (EnKF). The benefits of Sentinel-1 backscatter observations in two different polarizations (VV and VH) were tested in two separate DA experiments, performed over two irrigated sites, the first one located in the Po Valley (Italy) and the second one located in northern Germany. The results confirm that VV backscatter has a stronger link with soil moisture than VH backscatter, whereas VH backscatter observations introduce larger updates in the vegetation state variables. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had a large impact on the analysis, with more contradicting results obtained for the evaluation at the fine agriculture scale (i.e. field scale). Above all, this study sheds light on the limitations resulting from a poorly parameterized sprinkler irrigation scheme, which prevents improvements in the irrigation simulation due to DA and points to future developments needed to improve the system. [ABSTRACT FROM AUTHOR]

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements. The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/ , last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository. [ABSTRACT FROM AUTHOR]

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org , last access: 30 March 2020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science. [ABSTRACT FROM AUTHOR]

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and skepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project, the GROW Observatory (GROW; https://growobservatory.org). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within twenty four high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low inter-sensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes, only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science. [ABSTRACT FROM AUTHOR]

GROW Observatory is a project funded under the European Union's Horizon 2020 research and innovation program. Its aim is to establish a large scale (more than 20,000 participants), resilient and integrated 'Citizen Observatory' (CO) and community for environmental monitoring that is self-sustaining beyond the life of the project. This article describes how the initial framework and tools were developed to evolve, bring together and train such a community; raising interest, engaging participants, and educating to support reliable observations, measurements and documentation, and considerations with a special focus on the reliability of the resulting dataset for scientific purposes. The scientific purposes of GROW observatory are to test the data quality and the spatial representativity of a citizen engagement driven spatial distribution as reliably inputs for soil moisture monitoring and to create timely series of gridded soil moisture products based on citizens' observations using low cost soil moisture (SM) sensors, and to provide an extensive dataset of in situ soil moisture observations which can serve as a reference to validate satellite-based SM products and support the Copernicus in situ component. This article aims to showcase the initial steps of setting up such a monitoring network that has been reached at the mid-way point of the project's funded period, focusing mainly on the design and development of the CO monitoring network. [ABSTRACT FROM AUTHOR]

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value). [ABSTRACT FROM AUTHOR]

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources' management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies. [ABSTRACT FROM AUTHOR]

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems. [ABSTRACT FROM AUTHOR]

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products. [ABSTRACT FROM AUTHOR]

The involvement of non-experts in scientific work and data collection, namely citizen science, is rapidly increasing. Although the potential of citizen science activities and projects to generate huge amounts of data otherwise not feasible is widely recognized, the obtained data and observations are often treated with caution and skepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments and/or simplified methods. In this study, we evaluate the performance of the low-cost soil moisture sensor used within the European citizen science project GROW Observatory (GROW; https://growobservatory.org/). The aim of GROW is to empower hundreds of motivated citizens to monitor soil moisture and other environmental variables within nine high-density clusters around Europe covering different climate and soil conditions. The citizen's contribution is laying the foundation for scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the low-cost sensors were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria, where data has already been collected for more than a year. We assess the skill of the low cost sensors against the professional probes by the means of various methods. Apart from common statistical metrics like correlation, bias and rmsd, we investigate and compare the temporal stability as well as soil moisture memory. We will demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.This study is funded by the GROW Observatory project of the European Union's Horizon 2020 research and innovation programme (https://growobservatory.org/) and the International Soil Moisture Network under IDEAS+ of the European Space Agency (https://ismn.geo.tuwien.ac.at/). [ABSTRACT FROM AUTHOR]

Monitoring soil moisture at both high spatial and temporal resolution is necessary for a number of applications, ranging from crop irrigation to landslide prediction. Microwave remote sensing has been successfully used to estimate soil moisture globally. Radiometers and scatterometers typically observe the same location with a daily frequency, thus providing adequate temporal coverage. However, most of the soil moisture products currently available are characterized by a coarse spatial resolution (~ 25-50 km). Clearly, such products lack the ability to monitor the variability occurring at much finer scales (~ 1-100 m), which is mainly controlled by topography, soil texture and vegetation. The aim of this study is to estimate soil moisture at sub-field resolution by combining coarse scale remotely sensed data and ancillary information. We employ data collected from dozens of low-cost sensors measuring soil moisture and incoming solar radiation, the latter being then converted to fAPAR. The sensors are installed in the Hydrological Open Air Laboratory (HOAL), a small agricultural catchment (66 ha) characterized by complex topography and different land cover, located in Petzenkirchen (Austria). Additional ancillary data, i.e. soil texture and a Digital Elevation Model (DEM), are available for the study area. We use a Random Forest regression model to estimate high-resolution soil moisture using the following input features: soil texture, topographic indices (derived from the DEM), fAPAR, and the average catchment soil moisture computed from the low-cost sensor measurements. The latter, which represents the ideal scenario where satellite-derived soil moisture perfectly agrees with ground observations, is then replaced with a coarse scale remotely sensed product (ASCAT SM, 25 km spatial resolution), and results compared. The accuracy of the model(s) is evaluated with a cross-validation.Results show the overall good accuracy of the estimated high-resolution soil moisture as compared to in-situ measurements. Clearly, using the catchment average as input provides better results than using coarse scale satellite-derived data. In fact, the latter might not be fully representative for the study area due to the large spatial mismatch between satellite footprint and catchment size. The inclusion of a proxy of vegetation dynamics (i.e. fAPAR) in all experiment setups improves the accuracy of the estimated soil moisture; indeed fAPAR is the second most important variable for the model, following average catchment or remotely sensed soil moisture. Overall, the increasing availability of low-cost sensors providing reliable measurements allows to develop catchment-specific models to estimate soil moisture at sub-field resolution. These can provide valuable information both in the spatial and temporal domain (e.g. for locations and periods where sensors are not installed, respectively). Further analysis will assess the suitability of using remotely sensed dataset for monitoring vegetation development (e.g. LAI, NDVI, etc.), and the possibility to employ such model for similar catchments. [ABSTRACT FROM AUTHOR]