An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources
Abstract
:1. Introduction
1.1. Background
1.2. Motivation
2. Connecting Science to Society: iAqueduct Framework
2.1. Stakeholder Requirements and Potential Knowledge Gaps
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- How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data?
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- How to explore and apply the downscaled information at the management level for a better understanding of water–energy–soil–vegetation processes?
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- How can such fine-scaled information be used to improve the management of soil and water resources?
2.2. iAqueduct Framework
3. iAqueduct Technological Platform
3.1. Downscaling of Satellite Water Cycle Products (WB1)
3.1.1. Spatial Downscaling Procedures
- (1)
- Bayesian statistical bias correction of satellite data based on in situ observation. The calibration and validation of coarse-resolution satellite water cycle products at selected field sites with in situ observation is an integral part of this procedure (at the kilometer scale but corrected for spatio-temporal error, e.g., due to topography, soil texture, and climate, cf. those by [19] for precipitation; [26] for evapotranspiration; and [8] for soil moisture);
- (2)
- Development of downscaling methods based on Copernicus Sentinel data (from kilometer to hectometer scale). This procedure concerns evapotranspiration and soil moisture (by assuming the precipitation is homogeneous at the kilometer scale). Downscaling will be achieved by the combined use of optical, thermal, and radar data from Sentinel-1, 2, and 3;
- (3)
- Generation of high-resolution water cycle products of soil moisture, vegetation patterns, and vegetation stress (sub-meter spatial scale and daily interval). High-resolution maps will be provided with UAS equipped with thermal cameras, multispectral, and hyperspectral cameras. Such data will support the development of downscaling procedures, linking satellite to point measurements for calibration and validation at the selected field sites;
- (4)
- Characterization of the spatiotemporal distribution of soil moisture and evapotranspiration processes will be conducted after validation of the high-resolution imagery from UAS with outcomes of field measurements and outputs from ecohydrological models. The proper description of the controlling factors for the spatial variability of soil moisture is crucial to further advance the potential of downscaling methodologies;
- (5)
- Downscaling of the remote sensing data to the field scale (from the hectometer to plot scale) can be achieved by using a Bayesian approach exploiting the predicted variance and spatial correlation of the soil moisture process along with the ancillary data derived from UAS and WB2 activities on the physical characteristics of soil and vegetation. In particular, WB2 will support the development of new strategies aimed at the mapping of soil hydraulic and physical characteristics that will enhance the capabilities of soil moisture downscaling procedures (see, e.g., [11,42,43]).
3.1.2. Preliminary Results of Downscaling Surface Soil Moisture
3.1.3. From Surface Moisture Information to Profile Soil Moisture
3.2. Retrieval of Soil Hydraulic and Thermal Properties (WB2)
3.2.1. Towards a Protocol for Field-Scale Data Collection
3.2.2. Preliminary Results of Soil Spectroscopy and Hyperspectral Remote Sensing
3.2.3. Basic and Advanced Pedotransfer Functions
3.3. Linking Soil Properties, Soil Moisture, and Evapotranspiration (WB3)
3.3.1. Example: WaPOR Database
3.3.2. Approach of iAqueduct
3.4. Developing Plant- and Plot-Level Ecohydrological Models Using Remote Sensing Information (WB4)
3.5. Improving Distributed Catchment-Scale Ecohydrological Models Using Spatial Information (WB5)
4. Towards Sustainable Water Management (WB6)
4.1. Summary
4.2. iAqueduct Toolbox
4.3. Challenges
- -
- How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data?
- -
- How to explore and apply the downscaled information at the management level for a better understanding of water-energy- soil-vegetation processes?
- -
- How can such fine-scale information be used to improve the management of soil and water resources?
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Selected Observatories for iAqueduct
Appendix A.1. Twente, The Netherlands (Temperate Maritime Climate)
Appendix A.2. Zala, Hungary (Cold, Humid Winter, Warm Summer)
Appendix A.3. Alento, Italy (Temperate, Dry Hot Summer)
Appendix A.4. Fiumarella of Corleto, Italy (Temperate, Hot Humid Summer)
Appendix A.5. Carraixet Creek, Spain (Semiarid, Steppe, Mediterranean)
Appendix A.6. Kibbutz Sde Yoav and Afeka, Israel (Arid, Dry Hot Summer)
- (i)
- Kibbutz Sde Yoav, Israel (30 Samples): Kibbutz Sde Yoav is an agricultural settlement located in south-central Israel, between the cities of Ashkelon, Kyriat Gat, and Kyriat Malakhi. According to a detailed map of the soils of Israel, the soil type of the study area of Kibbutz Sde Yoav is alluvial, and according to an updated version of the Koeppen climate classification [174], the climate of Sde Yoav is hot-semiarid (Bsh). In this study area, 30 samples were collected.
- (ii)
- Afeta, Tel Aviv, Israel (18 Samples): Afeka is a residential neighborhood located in the north of Tel Aviv. The soil type of the study area of Afeka is brown-red sandy soil, and the climate according the classification of Rubel and Kottek, 2010 [174] is hot-summer Mediterranean climate (Csa). From Afeka, we collected 18 samples. In Afeka, we only collected samples for the calibration of the model and to expand our dataset. We did not carry out UAS campaigns there because they are forbidden.
- (iii)
- Alento, Italy (21 Samples): The Alento River Catchment is located in the Campania Region (Salerno Province, Italy). As in Afeka, the climate of Alento according the classification of Rubel and Kottek, 2010 is hot-summer Mediterranean climate (Csa). According to [175] in the book “Soils of Italy”, Alento is located in an area characterized by three soil types: Cambisols, Leptosols, and Luvisols. In this book, Costantini and Dazzi remark that this area is characterized hills and mountains on limestones covered by volcanic ashes, including alluvial and coastal plains.
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Variables/Parameters | Targeted Research | In Situ 1 | UAS 2 | Airborne 3 | Satellite Missions 4 |
---|---|---|---|---|---|
Precipitation | Downscaling | X | X | ||
Air Temperature | X | ||||
Air Pressure | X | ||||
Humidity | X | ||||
Wind speed/direction | X | ||||
Four-component (and Net) Radiation | X | X 5 | |||
Soil Heat Flux | X | ||||
Evaporation/transpiration | Downscaling | X | |||
Runoff | X | X | |||
Stream Flow | X | ||||
Groundwater level | X | ||||
Soil Properties (texture, hydraulic, thermal, etc.) | Retrieval | X | X | X | X |
Soil Moisture (surface, profile) | Downscaling | X | X | X | |
Soil Temperature (surface, profile) | X | X | X | X | |
Soil Freeze-Thaw (surface, profile) | X | X | X | X | |
Snow Depth | X | X | X | X | |
Snow Water Equivalent | X | ||||
Land Cover Types | X | X | X | ||
Vegetation Coverage | X | X | X | X | |
Plantation Structure | X | X | |||
Leaf Area Index | X | X | X | ||
Vegetation Structure Parameters (density, canopy height, crown diameter, etc.) | X | X | X | ||
Biomass (NPP and NEE) | X | X | X | X | |
DEM | X | X | |||
Laser altimetry | X | X | |||
Reflectance (optical range) | Energy balance and vegetation dynamics | X | X | X | X |
Fluorescence (optical range) | X | X | |||
Emittance (thermal range) | Energy balance and temperature downscaling | X | X | X | X |
Brightness temperature (microwave range) | Soil moisture downscaling | X | X | ||
Backscattering coefficient (microwave range) | Soil moisture downscaling | X |
Acquired Spectral Data | State of Soil Sample | Extension of Survey | Equipment Used for the Survey | Date of Survey | |||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | ||||
soil reflectance in the 450–1000 and 450–2400 nm range | x | x | 20 points, close to the SoilNET probes | ASD Spectrometer with SoilPRO | 13 June 2019 | ||
soil reflectance in the 450–1000 and 450–2400 nm range | x | x | 20 points, close to the SoilNET probes | ASD Spectrometer, Laboratory | 3–4 October 2018 13 June 2019 | ||
soil reflectance in the 450–950 nm range, 125 channels | x | x | 7.5 ha of study site | Cubert UHD-185 hyperspectral snapshot camera on UAS platform with a spatial resolution of 5 cm | 15 June 2019 | ||
soil reflectance in the 450–2400 nm spectral range | x | x | 20 points, close to the SoilNET probes | SoilPRO in situ measurement & spectral analysis in laboratory | 4 October 2018, 13 June 2019 | ||
soil reflectance in the 7.5–13.5 µm range | x | x | 7.5 ha of study site | FLIR Tau 336 thermal camera on UAS platform with a spatial resolution of 15 cm | 3–4 October 2018, 13–14 June 2019 | ||
RGB in VIS range | x | x | 18 ha of sub-catchment | Fuji X-T20 snapshot camera on UAS platform | 13 June 2019 |
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Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.; Vico, G.; Nasta, P.; Zhuang, R.; et al. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water 2020, 12, 1495. https://doi.org/10.3390/w12051495
Su Z, Zeng Y, Romano N, Manfreda S, Francés F, Ben Dor E, Szabó B, Vico G, Nasta P, Zhuang R, et al. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 2020; 12(5):1495. https://doi.org/10.3390/w12051495
Chicago/Turabian StyleSu, Zhongbo, Yijian Zeng, Nunzio Romano, Salvatore Manfreda, Félix Francés, Eyal Ben Dor, Brigitta Szabó, Giulia Vico, Paolo Nasta, Ruodan Zhuang, and et al. 2020. "An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources" Water 12, no. 5: 1495. https://doi.org/10.3390/w12051495