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Benchmarking of topsoil moisture estimation methods based on a field study
Published online by Cambridge University Press: 20 January 2025
Abstract
Extreme weather events caused by climate change, such as drought and heavy rainfall, will further increase in Central Europe in the near future. Resilient crop production requires in-depth knowledge of soil moisture (SM), its spatial and temporal variability and the dynamics of agriculturally used land. In the current study, different SM estimation methods, including measurement and simulation-based methods, were evaluated over a 17-ha experimental arable crop field with respect to their abilities to capture the spatial and temporal SM dynamics of within-field areas and their related uncertainty and spatial representativeness. The high-spatial resolution in-situ topsoil moisture measurements (50 m grid) were compared with the estimated SM from satellite-based remote sensing (S1ASCAT) and the simulated SM from three different crop water balance models (Agricultural Risk Information System [ARIS], AquaCrop and DSSAT). The evaluation revealed that the spatial variability in the experimental field obtained from the reference could not be captured by the alternative methods investigated because of the limitations of the grid size-related soil map information. Nevertheless, the analysis revealed a very good temporal correlation of SM dynamics with the field area average across all approaches, with AquaCrop and ARIS at a soil depth of 0–10 cm and S1ASCAT soil–water index 05 achieving a R2 and a Kling–Gupta efficiency >0.80. These results indicate the added value of complementary methods for estimating SM to reduce spatial and temporal uncertainties in the estimated topsoil water content.
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- Crops and Soils Research Paper
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- Copyright © The Author(s), 2025. Published by Cambridge University Press
References
Abdulraheem, MI, Zhang, W, Li, S, Moshayedi, AJ, Farooque, AA and Hu, J (2023) Advancement of remote sensing for soil measurements and applications: a comprehensive review. Sustainability 15, 15444.CrossRefGoogle Scholar
Akash, M, Mohan Kumar, P, Bhaskar, P, Deepthi, PR and Sukhdev, A (2024) Review of estimation of soil moisture using active microwave remote sensing technique. Remote Sensing Applications: Society and Environment 33, 101118.CrossRefGoogle Scholar
Albergel, C, Rudiger, C, Pellarin, T, Calvet, JC, Fritz, N, Froissard, F, Suquia, D, Petitpa, A, Piguet, B and Martin, E (2008) From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrology and Earth System Sciences 12, 1323–1337.CrossRefGoogle Scholar
Allen, R, Pereira, L, Raes, D and Smith, M (1998) Crop Evapotranspiration – Guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56. Rome, Italy. Available online from: https://www.fao.org/4/X0490E/x0490e00.htm#Contents (accessed 7 June 2024).Google Scholar
Babaeian, E, Sadeghi, M, Jones, SB, Montzka, C, Vereecken, H and Tuller, M (2019) Ground, proximal, and satellite remote sensing of soil moisture. Reviews of Geophysics 57, 530–616.CrossRefGoogle Scholar
Babel, MS, Deb, P and Soni, P (2019) Performance evaluation of AquaCrop and DSSAT–CERES for maize under different irrigation and manure application rates in the Himalayan region of India. Agricultural Research 8, 207–217.CrossRefGoogle Scholar
Balasundra, SK, Husni, MHA and Ahmed, OH (2007) Application of geostatistical tools to quantify spatial variability of selected soil chemical properties from a cultivated tropical peat. Journal of Agronomy 7, 82–87.CrossRefGoogle Scholar
Bayraktar, H and Turalioglu, FS (2005) A kriging-based approach for locating a sampling site – in the assessment of air quality. Stochastic Environmental Research and Risk Assessment 19, 301–305.CrossRefGoogle Scholar
BFW (Bundesforschungs– und Ausbildungszentrum für Wald Naturgefahren und Landschaft) (2024) ‘eBOD’ – Digitale Bodenkarte Österreichs. Available online from: https://www.bodenkarte.at (accessed 6 June 2024).Google Scholar
Bogena, HR, Huisman, JA, Güntner, A, Hübner, C, Kusche, J, Jonard, F, Vey, S and Vereecken, H (2015) Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale: a review. Wiley Interdisciplinary Reviews–Water 2, 635–647.CrossRefGoogle Scholar
Brandtner, F (1954) Jungpleistozäner Löß und fossile Böden in Niederösterreich. E&G Quaternary Science Journal 4/5, 49–82.Google Scholar
Brocca, L, Melone, F, Moramarco, T, Wagner, W and Hasenauer, S (2010) ASCAT soil wetness index validation through in-situ and modeled soil moisture data in central Italy. Remote Sensing of Environment 114, 2745–2755.CrossRefGoogle Scholar
Brocca, L, Ciabatta, L, Massari, C, Camici, S and Tarpanelli, A (2017) Soil moisture for hydrological applications: open questions and new opportunities. Water 9, 140.CrossRefGoogle Scholar
Büntgen, U, Urban, O, Krusic, PJ, Rybníček, M, Kolář, T, Kyncl, T, Ač, A, Koňasová, E, Čáslavský, J, Esper, J, Wagner, S, Saurer, M, Tegel, W, Dobrovolný, P, Cherubini, P, Reinig, F and Trnka, M (2021) Recent European drought extremes beyond Common Era background variability. Nature Geoscience 14, 190–196.CrossRefGoogle Scholar
Deb, P, Kiem, AS, Babel, MS, Chu, ST and Chakma, B (2015) Evaluation of climate change impacts and adaptation strategies for maize cultivation in the Himalayan foothills of India. Journal of Water and Climate Change 6, 596–614.CrossRefGoogle Scholar
Dorigo, WA, Wagner, W, Hohensinn, R, Hahn, S, Paulik, C, Xaver, A, Gruber, A, Drusch, M, Mecklenburg, S, van Oevelen, P, Robock, A and Jackson, T (2011) The international soil moisture network: a data hosting facility for global in-situ soil moisture measurements. Hydrology and Earth System Sciences 15, 1675–1698.CrossRefGoogle Scholar
Draper, NR and Smith, H (1998) On worthwhile regressions, big F's, and R 2. In Draper, NR and Smith, H (eds), Applied Regression Analysis. Wiley Series in Probability and Statistics, 3rd edn. Hoboken, NJ: John Wiley & Sons, Inc., pp. 243–250. https://doi.org/10.1002/9781118625590.ch11CrossRefGoogle Scholar
Eitzinger, J, Kersebaum, KC and Formayer, H (2009) Landwirtschaft im Klimawandel. Auswirkungen und Anpassungsstrategien für die Land– und Forstwirtschaft in Mitteleuropa, Ist Edn. Clenze, Germany: Agrimedia.Google Scholar
Eitzinger, J, Trnka, M, Semeradova, D, Thaler, S, Svobodová, E, Hlavinka, P, Šiska, B, Takac, J, Malatinska, L, Novakova, M, Dubrovsky, M and Žalud, Z (2012a) Regional climate change impacts on agricultural crop production in Central and Eastern Europe – hotspots, regional differences and common trends. The Journal of Agricultural Science 151, 787–812.CrossRefGoogle Scholar
Eitzinger, J, Thaler, S, Schmid, E, Strauss, F, Ferrise, R, Moriondo, M, Bindi, M, Palosuo, T, Rötter, R, Kersebaum, KC, Olesen, JE, Patil, RH, Şaylan, L, Çaldağ, B and Çaylak, O (2012b) Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. The Journal of Agricultural Science 151, 813–835.CrossRefGoogle Scholar
Eitzinger, J, Daneu, V, Kubu, G, Thaler, S, Trnka, M, Schaumberger, A, Schneider, S and Tran, TMA (2024) Grid based monitoring and forecasting system of cropping conditions and risks by agrometeorological indicators in Austria – agricultural risk information system ARIS. Climate Services 34, 100478.CrossRefGoogle Scholar
Emery, X (2005) Simple and ordinary multigaussian kriging for estimating recoverable reserves. Mathematical Geology 37, 295–319.CrossRefGoogle Scholar
Entekhabi, D, Reichle, RH, Koster, RD and Crow, WT (2010) Performance metrics for soil moisture retrievals and application requirements. Journal of Hydrometeorology 11, 832–840.CrossRefGoogle Scholar
Ercin, E, Veldkamp, TIE and Hunink, J (2021) Cross-border climate vulnerabilities of the European Union to drought. Nature Communications 12, 3322.CrossRefGoogle ScholarPubMed
Fry, JE and Guber, AK (2020) Temporal stability of field-scale patterns in soil water content across topographically diverse agricultural landscapes. Journal of Hydrology 580, 124260.CrossRefGoogle Scholar
Garré, S, Hyndman, D, Mary, B and Werban, U (2021) Geophysics conquering new territories: the rise of ‘agrogeophysics’. Vadose Zone Journal 20, e20115.CrossRefGoogle Scholar
Gojiya, K, Rank, DH, Chauhan, P, Patel, D, Satasiya, RM and Girish, P (2023) Advances in soil moisture estimation through remote sensing and GIS: a review. International Research Journal of Modernization in Engineering Technology and Science 5, 2669–2678.Google Scholar
Grassini, P, van Bussel, LGJ, Van Wart, J, Wolf, J, Claessens, L, Yang, H, Boogaard, H, de Groot, H, van Ittersum, MK and Cassman, KG (2015) How good is good enough? Data requirements for reliable crop yield simulations and yield–gap analysis. Field Crops Research 177, 49–63.CrossRefGoogle Scholar
Grillakis, MG (2019) Increase in severe and extreme soil moisture droughts for Europe under climate change. Science of The Total Environment 660, 1245–1255.CrossRefGoogle ScholarPubMed
Gupta, HV, Kling, H, Yilmaz, KK and Martinez, GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology 377, 80–91.CrossRefGoogle Scholar
Hahn, S, Wagner, W, Steele-Dunne, SC, Vreugdenhil, M and Melzer, T (2021) Improving ASCAT soil moisture retrievals with an enhanced spatially variable vegetation parameterization. IEEE Transactions on Geoscience and Remote Sensing 59, 8241–8256.CrossRefGoogle Scholar
Hari, V, Rakovec, O, Markonis, Y, Hanel, M and Kumar, R (2020) Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming. Scientific Reports 10, 12207.CrossRefGoogle ScholarPubMed
Hlavinka, P, Trnka, M, Balek, J, Semerádová, D, Hayes, M, Svoboda, M, Eitzinger, J, Možný, M, Fischer, M, Hunt, E and Žalud, Z (2011) Development and evaluation of the SoilClim model for water balance and soil climate estimates. Agricultural Water Management 98, 1249–1261.CrossRefGoogle Scholar
Hoogenboom, G, Porter, CH, Boote, KJ, Shelia, V, Wilkens, PW, Singh, U, White, JW, Asseng, S, Lizaso, JI, Moreno, LP, Pavan, W, Ogoshi, RM, Hunt, LA, Tsuji, GY and Jones, JW (2019) The DSSAT crop modeling ecosystem. In Boote, KJ (ed.), Advances in Crop Modeling for a Sustainable Agriculture. Cambridge, UK: Burleigh Dodds Science Publishing, pp. 173–216.CrossRefGoogle Scholar
Hoogenboom, G, Porter, CH, Shelia, V, Boote, KJ, Singh, U, White, JW, Pavan, W, Oliveira, FAA, Moreno-Cadena, LP, Lizaso, JI, Asseng, S, Pequeno, DNL, Kimball, BA, Alderman, PD, Thorp, KR, Jones, MR, Cuadra, SV, Vianna, MS, Villalobos, FJ, Ferreira, TB, Batchelor, WD, Koo, J, Hunt, LA and Jones, JW (2023) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.8.2 (www.dssat.net). Gainesville, FL, USA: DSSAT Foundation.Google Scholar
Huang, J, Gómez-Dans, JL, Huang, H, Ma, H, Wu, Q, Lewis, PE, Liang, S, Chen, Z, Xue, J-H, Wu, Y, Zhao, F, Wang, J and Xie, X (2019) Assimilation of remote sensing into crop growth models: current status and perspectives. Agricultural and Forest Meteorology 276–277, 107609.CrossRefGoogle Scholar
Huang, J, Hartemink, AE and Kucharik, CJ (2021) Soil-dependent responses of US crop yields to climate variability and depth to groundwater. Agricultural Systems 190, 103085.CrossRefGoogle Scholar
Hyndman, RJ and Koehler, AB (2006) Another look at measures of forecast accuracy. International Journal of Forecasting 22, 679–688.CrossRefGoogle Scholar
Ines, AVM, Das, NN, Hansen, JW and Njoku, EG (2013) Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment 138, 149–164.CrossRefGoogle Scholar
Janssen, PHM and Heuberger, PSC (1995) Calibration of process-oriented models. Ecological Modelling 83, 55–66.CrossRefGoogle Scholar
Jia, G, Shevliakova, E, Artaxo, P, De Noblet-Ducoudré, N, Houghton, R, House, J, Kitajima, K, Lennard, C, Popp, A, Sirin, A, Sukumar, R and Verchot, L (2019) Land-climate interactions. In Shukla, PR, Skea, J, Calvo Buendia, E, Masson-Delmotte, V, Pörtner, H-O, Roberts, DC, Zhai, P, Slade, R, Connors, S, van Diemen, R, Ferrat, M, Haughey, E, Luz, S, Neogi, S, Pathak, M, Petzold, J, Portugal Pereira, J, Vyas, P, Huntley, E, Kissick, K, Belkacemi, M and Malley, J (eds), Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, pp. 131–248. https://doi.org/10.1017/9781009157988.004Google Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.CrossRefGoogle Scholar
Kisekka, I, Peddinti, SR, Kustas, WP, McElrone, AJ, Bambach-Ortiz, N, McKee, L and Bastiaanssen, W (2022) Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrigation Science 40, 761–777.CrossRefGoogle Scholar
Kivi, M, Vergopolan, N and Dokoohaki, H (2022) A comprehensive assessment of in-situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the U.S. Midwest. Hydrology and Earth System Sciences Discussions 2022, 1–33.Google Scholar
Kling, H, Fuchs, M and Paulin, M (2012) Runoff conditions in the Upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology 424–425, 264–277.CrossRefGoogle Scholar
Kumar, P, Rao, B, Burman, A, Kumar, S and Samui, P (2023) Spatial variation of permeability and consolidation behaviors of soil using ordinary kriging method. Groundwater for Sustainable Development 20, 100856.CrossRefGoogle Scholar
Lalic, B, Firanj Sremac, A, Eitzinger, J, Stricevic, R, Thaler, S, Maksimovic, I, Danicic, M, Perisic, D and Dekic, L (2018) Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria. The Journal of Agricultural Science 156, 658–672.CrossRefGoogle ScholarPubMed
Li, Z-L, Leng, P, Zhou, C, Chen, K-S, Zhou, F-C and Shang, G-F (2021) Soil moisture retrieval from remote sensing measurements: current knowledge and directions for the future. Earth-Science Reviews 218, 103673.CrossRefGoogle Scholar
Lu, Y, Song, W, Lu, J, Wang, X and Tan, Y (2017) An examination of soil moisture estimation using ground penetrating radar in desert steppe. Water 9, 521.CrossRefGoogle Scholar
Lutz, F, Del Grosso, S, Ogle, S, Williams, S, Minoli, S, Rolinski, S, Heinke, J, Stoorvogel, JJ and Müller, C (2020) The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage. Geoscientific Model Development 13, 3905–3923.CrossRefGoogle Scholar
Ma, B, Wang, Q, Xue, B, Hou, Z, Jiang, Y and Cai, W (2022) Application of UAV remote sensing in monitoring water use efficiency and biomass of cotton plants adjacent to shelterbelt. Frontiers in Plant Science 13, 894172.CrossRefGoogle Scholar
Mani, PK, Mandal, A, Biswas, S, Sarkar, B, Mitran, T and Meena, R (2020) Remote sensing and geographic information system: a tool for precision farming. In Mitran, T, Meena, RS and Chakraborty, A (eds), Geospatial Technologies for Crops and Soils. Singapore: Springer, pp. 49–111.Google Scholar
METER (2024) ECH2O EC–5 Soil Moisture Sensor. Available online from https://metergroup.com/products/ech20-ec-5-soil-moisture-sensor/ (accessed 7 June 2024).Google Scholar
Mishra, V, Cruise, JF and Mecikalski, JR (2021) Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over southeast United States. European Journal of Agronomy 123, 126208.CrossRefGoogle Scholar
Murer, E (1998) Die Ableitung der Parameter eines Bodenwasserhaushalts– und Stofftransportmodelles aus den Ergebnissen der Bodenkartierung. Modelle für die gesättigte und ungesättigte Bodenzone. Schriftenreihe BAW 7, 89–103.Google Scholar
Murer, E, Wagenhofer, J, Aigner, F and Pfeffer, M (2004) Die nutzbare Feldkapazität der mineralischen Böden der landwirtschaftlichen Nutzfläche Österreichs. Schriftenreihe BAW 20, 72–78.Google Scholar
Naeimi, V, Bartalis, Z and Wagner, W (2009) ASCAT soil moisture: an assessment of the data quality and consistency with the ERS scatterometer heritage. Journal of Hydrometeorology 10, 555–563.CrossRefGoogle Scholar
Naziq, MS, Sathyamoorthy, NK, Ga, D, Pazhanivelan, S and Vadivel, N (2024) Coupled weather and crop simulation modeling for smart irrigation planning: a review. Water Supply 24, 2844–2865.Google Scholar
Ortuani, B, Benedetto, A, Giudici, M, Mele, M and Tosti, F (2013) A non-invasive approach to monitor variability of soil water content with electromagnetic methods. Procedia Environmental Sciences 19, 446–455.CrossRefGoogle Scholar
Palosuo, T, Kersebaum, KC, Angulo, C, Hlavinka, P, Moriondo, M, Olesen, JE, Patil, RH, Ruget, F, Rumbaur, C, Takáč, J, Trnka, M, Bindi, M, Çaldağ, B, Ewert, F, Ferrise, R, Mirschel, W, Şaylan, L, Šiška, B and Rötter, R (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. European Journal of Agronomy 35, 103–114.CrossRefGoogle Scholar
Parrot (2016) Parrot Flower Power. User guide. Available online from: https://www.parrot.com/assets/s3fs-public/2021-09/flower-power_user-guide_uk.pdf (accessed 7 June 2024).Google Scholar
Peng, J, Loew, A, Merlin, O and Verhoest, NEC (2017) A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics 55, 341–366.CrossRefGoogle Scholar
Pereira, LS, Paredes, P and Jovanovic, N (2020) Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach. Agricultural Water Management 241, 106357.CrossRefGoogle Scholar
Pfeil, I, Vreugdenhil, M, Hahn, S, Wagner, W, Strauss, P and Blöschl, G (2018) Improving the seasonal representation of ASCAT soil moisture and vegetation dynamics in a temperate climate. Remote Sensing 10, 1788.CrossRefGoogle Scholar
Rahimzadeh-Bajgiran, P and Berg, A (2016) Chapter 3 – Soil moisture retrievals using optical/TIR methods. In Srivastava, PK, Petropoulos, GP and Kerr, YH (eds), Satellite Soil Moisture Retrieval. Amsterdam, Netherlands, Oxford, UK, Cambridge, USA: Elsevier, pp. 47–72.CrossRefGoogle Scholar
Raju, NS, Bilgic, R, Edwards, JE and Fleer, PF (1997) Methodology review: estimation of population validity and cross-validity, and the use of equal weights in prediction. Applied Psychological Measurement 21, 291–305.CrossRefGoogle Scholar
Rayhan Shaheb, M, Sarker, A and Shearer, SA (2022) Chapter 4. Precision agriculture for sustainable soil and crop management. In Michael, A and Indi, B (eds), Soil Science – Emerging Technologies, Global Perspectives and Applications. London, UK: IntechOpen, pp. 49–72.Google Scholar
Ritchie, JT (1998) Soil water balance and plant water stress. In Tsuji, GY, Hoogenboom, G and Thornton, PK (eds), Understanding Options for Agricultural Production. Dordrecht, Netherlands: Springer Netherlands, pp. 41–54.CrossRefGoogle Scholar
Robinson, DA, Campbell, CS, Hopmans, JW, Hornbuckle, BK, Jones, SB, Knight, R, Ogden, F, Selker, J and Wendroth, O (2008) Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review. Vadose Zone Journal 7, 358–389.CrossRefGoogle Scholar
Rötter, RP, Palosuo, T, Kersebaum, KC, Angulo, C, Bindi, M, Ewert, F, Ferrise, R, Hlavinka, P, Moriondo, M, Nendel, C, Olesen, JE, Patil, RH, Ruget, F, Takáč, J and Trnka, M (2012) Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crops Research 133, 23–36.CrossRefGoogle Scholar
Rozenstein, O, Cohen, Y, Alchanatis, V, Behrendt, K, Bonfil, DJ, Eshel, G, Harari, A, Harris, WE, Klapp, I, Laor, Y, Linker, R, Paz-Kagan, T, Peets, S, Rutter, SM, Salzer, Y and Lowenberg-DeBoer, J (2023) Data-driven agriculture and sustainable farming: friends or foes? Precision Agriculture 25, 520–531.CrossRefGoogle Scholar
Salman, M, García-Vila, M, Fereres, E, Raes, D and Steduto, P (2021) The AquaCrop Model – Enhancing Crop Water Productivity: Ten Years of Development, Dissemination and Implementation 2009–2019 (FAO Water Report No. 47). Rome, Italy: FAO. Available online from: https://openknowledge.fao.org/items/890f6033-554e-4e48-8dbb-5f1d809536e2 (accessed 7 June 2024).Google Scholar
Saue, T and Kadaja, J (2014) Water limitations on potato yield in Estonia assessed by crop modelling. Agricultural and Forest Meteorology 194, 20–28.CrossRefGoogle Scholar
Shaukat, H, Flower, KC and Leopold, M (2024) Comparing quasi-3D soil moisture derived from electromagnetic induction with 1D moisture sensors and correlation to barley yield in variable duplex soil. Soil and Tillage Research 236, 105953.CrossRefGoogle Scholar
Steduto, P, Raes, D, Hsiao, TC, Fereres, E, Heng, L, Izzi, G and Hoogeveen, J (2008) AquaCrop: a new model for crop prediction under water deficit conditions. Options Méditerranéennes, Series A 80, 285–292.Google Scholar
Steduto, P, Hsiao, TC, Raes, D and Fereres, E (2009) AquaCrop – the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal 101, 426–437.CrossRefGoogle Scholar
Tang, G, Clark, MP and Papalexiou, SM (2021). SC-Earth: a station-based serially complete earth dataset from 1950 to 2019. Journal of Climate 34, 6493–6511.CrossRefGoogle Scholar
Thaler, S, Eitzinger, J, Trnka, M and Dubrovsky, M (2012) Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in central Europe. The Journal of Agricultural Science 150, 537–555.CrossRefGoogle Scholar
Thaler, S, Gobin, A and Eitzinger, J (2017) Water footprint of main crops in Austria/Wasser–Fußabdruck wichtiger Nutzpflanzen in Österreich. Die Bodenkultur: Journal of Land Management, Food and Environment 68, 1–15.CrossRefGoogle Scholar
Thaler, S, Eitzinger, J, Trnka, M, Možný, M, Hahn, S, Wagner, W and Hlavinka, P (2018) The performance of Metop advanced SCATterometer soil moisture data as a complementary source for the estimation of crop–soil water balance in Central Europe. The Journal of Agricultural Science 156, 577–598.CrossRefGoogle Scholar
Thaler, S, Berger, K, Eitzinger, J, Mahnaz, A, Shala-Mayrhofer, V, Zamini, S and Weihs, P (2024) Radiation limits the yield potential of main crops under selected agrivoltaic designs – a case study of a new shading simulation method. Agronomy 14, 2511.CrossRefGoogle Scholar
Timlin, D, Paff, K and Han, E (2024) The role of crop simulation modeling in assessing potential climate change impacts. Agrosystems, Geosciences & Environment 7, e20453.CrossRefGoogle Scholar
Todoroff, P, De Robillard, F and Laurent, JB (2010) Interconnection of a crop growth model with remote sensing data to estimate the total available water capacity of soils. 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, pp. 1641–1644.CrossRefGoogle Scholar
Tong, R, Parajka, J, Széles, B, Greimeister-Pfeil, I, Vreugdenhil, M, Komma, J, Valent, P and Blöschl, G (2022) The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites. Hydrology and Earth System Sciences 26, 1779–1799.CrossRefGoogle Scholar
Topp, GC, Davis, JL and Annan, AP (1980) Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resources Research 16, 574–582.CrossRefGoogle Scholar
Trnka, M, Hlavinka, P, Mozny, M, Semeradova, D, Stepanek, P, Balek, J, Bartosova, L, Zahradnícek, P, Blahova, M, Skalak, P, Farda, A, Hayes, M, Svoboda, M, Wagner, W, Eitzinger, J, Fischer, M and Zalud, Z (2020) Czech drought Monitor System for monitoring and forecasting agricultural drought and drought impacts. International Journal of Climatology 40, 5941–5958.CrossRefGoogle Scholar
Trnka, M, Vizina, A, Hanel, M, Balek, J, Fischer, M, Hlavinka, P, Semerádová, D, Stepánek, P, Zahradnícek, P, Skalák, P, Eitzinger, J, Dubrovsky, M, Máca, P, Belínová, M, Zeman, E and Brázdil, R (2022) Increasing available water capacity as a factor for increasing drought resilience or potential conflict over water resources under present and future climate conditions. Agricultural Water Management 264, 107460.CrossRefGoogle Scholar
Vanderlinden, K, Giráldez, JV and Van Meirvenne, M (2005) Soil water-holding capacity assessment in terms of the average annual water balance in southern Spain. Vadose Zone Journal 4, 317–328.CrossRefGoogle Scholar
Vanuytrecht, E, Raes, D, Steduto, P, Hsiao, TC, Heng, LK, Vila, MG, Moreno, PM and Moreno, PM (2014) AquaCrop: FAO's crop water productivity and yield response model. Environmental Modelling & Software 62, 351–360.CrossRefGoogle Scholar
Vreugdenhil, M, Wagner, W, Bauer-Marschallinger, B, Pfeil, I, Teubner, I, Rüdiger, C and Strauss, P (2018) Sensitivity of Sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sensing 10, 1396.CrossRefGoogle Scholar
Vuolo, F, Essl, L, Zappa, L, Sandén, T and Spiegel, H (2017) Water and nutrient management: the Austria case study of the FATIMA H2020 project. Advances in Animal Biosciences 8, 400–405.CrossRefGoogle Scholar
Wagner, W, Lemoine, G, Borgeaud, M and Rott, H (1999a) A study of vegetation cover effects on ERS scatterometer data. IEEE Transactions on Geoscience and Remote Sensing 37, 938–948.CrossRefGoogle Scholar
Wagner, W, Lemoine, G and Rott, H (1999b) A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment 70, 191–207.CrossRefGoogle Scholar
Wagner, W, Pathe, C, Doubkova, M, Sabel, D, Bartsch, A, Hasenauer, S, Bloschl, G, Scipal, K, Martinez-Fernandez, J and Low, A (2008) Temporal stability of soil moisture and radar backscatter observed by the advanced synthetic aperture radar (ASAR). Sensors 8, 1174–1197.CrossRefGoogle ScholarPubMed
Wagner, W, Hahn, S, Kidd, R, Melzer, T, Bartalis, Z, Hasenauer, S, Figa-Saldaña, J, de Rosnay, P, Jann, A, Schneider, S, Komma, J, Kubu, G, Brugger, K, Aubrecht, C, Züger, J, Gangkofner, U, Kienberger, S, Brocca, L, Wang, Y, Blöschl, G, Eitzinger, J, Steinnocher, K, Zeil, P and Rubel, F (2013) The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications. Meteorologische Zeitschrift 22, 5–33.CrossRefGoogle Scholar
Walker, JP, Willgoose, GR and Kalma, JD (2004) In-situ measurement of soil moisture: a comparison of techniques. Journal of Hydrology 293, 85–99.CrossRefGoogle Scholar
Webster, R and Oliver, M (2007) Local Estimation or Prediction: Kriging. In Webster, R and Oliver, M (eds), Geostatistics for Environmental Scientists: Second Edition. Chichester, West Sussex, England: Wiley, pp. 153–194.CrossRefGoogle Scholar
Weir, P and Dahlhaus, P (2023) In search of pragmatic soil moisture mapping at the field scale: a review. Smart Agricultural Technology 6, 100330.CrossRefGoogle Scholar
Willmott, CJ and Matsuura, K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30, 79–82.CrossRefGoogle Scholar
Xaver, A, Zappa, L, Rab, G, Pfeil, I, Vreugdenhil, M, Hemment, D and Dorigo, WA (2020) Evaluating the suitability of the consumer low-cost parrot flower power soil moisture sensor for scientific environmental applications. Geoscientific Instrumentation, Methods Data Systems 9, 117–139.CrossRefGoogle Scholar
Zaussinger, F, Dorigo, W, Gruber, A, Tarpanelli, A, Filippucci, P and Brocca, L (2019) Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data. Hydrology and Earth System Sciences 23, 897–923.CrossRefGoogle Scholar
Zhang, YH, Li, HY, Sun, YG, Zhang, Q, Liu, PZ, Wang, R and Li, J (2022) Temporal stability analysis evaluates soil water sustainability of different cropping systems in a dryland agricultural ecosystem. Agricultural Water Management 272, 107834.CrossRefGoogle Scholar
Zhou, H, Geng, G, Yang, J, Hu, H, Sheng, L and Lou, W (2022) Improving soil moisture estimation via assimilation of remote sensing product into the DSSAT crop model and its effect on agricultural drought monitoring. Remote Sensing 14, 3187.CrossRefGoogle Scholar
Thaler et al. supplementary material
Thaler et al. supplementary material
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