Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Mar 2023
Model description paper |
| 17 Mar 2023
| 17 Mar 2023
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
David Hughes
Department of Entomology, The Pennsylvania State University,
University Park, PA, USA
Department of Biology, The Pennsylvania State University, University
Park, PA, USA
The Current and Emerging Threat to Crop Innovation Lab, The
Pennsylvania State University, University Park, PA, USA
Farshid Rahmani
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Kathryn Lawson
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
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Cited articles
Aboelyazeed, D., Xu, C., Hoffman, F. M., Jones, A. W., Rackauckas, C., Lawson, K. E., and Shen, C.: A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2022-211, in review, 2022.
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017.
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
Al-Yaari, A., Wigneron, J.-P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill,
P. E., Jackson, T. J., De Lannoy, G. J. M., Al Bitar, A., Mialon, A.,
Richaume, P., Walker, J. P., Mahmoodi, A., and Yueh, S.: Evaluating soil
moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature
datasets, Remote Sens. Environ., 193, 257–273,
https://doi.org/10.1016/j.rse.2017.03.010, 2017.
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A.,
Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic
variables for environmental and biodiversity modeling, Sci. Data, 5, 180040,
https://doi.org/10.1038/sdata.2018.40, 2018.
Armesto, J. J. and Martnez, J. A.: Relations between vegetation structure
and slope aspect in the Mediterranean region of Chile, J. Ecol., 66,
881–889, https://doi.org/10.2307/2259301, 1978.
Baraniuk, C.: Locust Swarms Are Getting So Big That We Need Radar to Track
Them, Medium, https://onezero.medium.com/locust-swarms-are-getting-so-big-that-we-need-radar-to-track-them-dc79c06496a0 (last access: 1 August 2022), 2020.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model
L4 3 hourly 0.25 x 0.25 degree V2.0 (GLDAS_NOAH025_3H 2.0), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/342OHQM9AK6Q, 2019.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A.
I. J. M. van, McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly
0.1∘ Precipitation: Methodology and Quantitative Assessment, B.
Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1,
2019.
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, 2021.
Bennie, J., Hill, M. O., Baxter, R., and Huntley, B.: Influence of slope and
aspect on long-term vegetation change in British chalk grasslands, J. Ecol.,
94, 355–368, https://doi.org/10.1111/j.1365-2745.2006.01104.x, 2006.
Bentley, A. R., Donovan, J., Sonder, K., Baudron, F., Lewis, J. M., Voss,
R., Rutsaert, P., Poole, N., Kamoun, S., Saunders, D. G. O., Hodson, D.,
Hughes, D. P., Negra, C., Ibba, M. I., Snapp, S., Sida, T. S., Jaleta, M.,
Tesfaye, K., Becker-Reshef, I., and Govaerts, B.: Near- to long-term
measures to stabilize global wheat supplies and food security, Nat. Food, 3,
483–486, https://doi.org/10.1038/s43016-022-00559-y, 2022.
Bindas, T., Tsai, W.-P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson,
K., and Shen, C.: Improving large-basin streamflow simulation using a
modular, differentiable, learnable graph model for routing, ESS Open Archive [preprint],
https://doi.org/10.1002/essoar.10512512.1, 2022.
de Jeu, R. and Owe, M.: AMSR2/GCOM-W1 surface soil moisture (LPRM)
L3 1 day 10 km x 10 km ascending V001, Goddard Earth Sciences Data and Information Services Center (GES DISC) (Bill Teng), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/B0GHODHJLDA8,
2013.
Didan, K.: MOD13C2: MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg
CMG version 6, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD13C2.006, 2015.
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global automated quality control of in situ soil moisture data from the
international soil moisture network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013.
Ellenburg, W. L., Mishra, V., Roberts, J. B., Limaye, A. S., Case, J. L.,
Blankenship, C. B., and Cressman, K.: Detecting desert locust breeding
grounds: A satellite-assisted modeling approach, Remote Sens., 13, 1276,
https://doi.org/10.3390/rs13071276, 2021.
Entekhabi, D.: The Soil Moisture Active Passive (SMAP) mission, P. IEEE,
98, 704–716, https://doi.org/10/bz3xhb, 2010.
ESA: Land Cover CCI Product User Guide Version 2, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 1 August 2022), 2017.
Fang, K. and Shen, C.: Near-real-time forecast of satellite-based soil
moisture using long short-term memory with an adaptive data integration
kernel, J. Hydrometeorol., 21, 399–413,
https://doi.org/10.1175/jhm-d-19-0169.1, 2020.
Fang, K., Shen, C., Kifer, D., and Yang, X.: Prolongation of SMAP to
spatiotemporally seamless coverage of continental U.S. using a deep learning
neural network, Geophys. Res. Lett., 44, 11030–11039,
https://doi.org/10.1002/2017gl075619, 2017.
Fang, K., Pan, M., and Shen, C.: The value of SMAP for long-term soil
moisture estimation with the help of deep learning, IEEE T. Geosci.
Remote, 57, 2221–2233, https://doi.org/10/gghp3v, 2019.
Fang, K., Kifer, D., Lawson, K., Feng, D., and Shen, C.: The data synergy
effects of time-series deep learning models in hydrology, Water Resour.
Res., 58, e2021WR029583, https://doi.org/10.1029/2021WR029583, 2022.
FAO, IIASA, ISRIC, ISSCAS, and JRC: Harmonized World Soil Database (version
1.2), FAO IIASA, ISRIC, ISSCAS, and JRC [data set], http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 1 August 2022), 2012.
Feng, D., Fang, K., and Shen, C.: Enhancing streamflow forecast and
extracting insights using long-short term memory networks with data
integration at continental scales, Water Resour. Res., 56, e2019WR026793,
https://doi.org/10.1029/2019WR026793, 2020.
Feng, D., Lawson, K., and Shen, C.: Mitigating prediction error of deep
learning streamflow models in large data-sparse regions with ensemble
modeling and soft data, Geophys. Res. Lett., 48, e2021GL092999,
https://doi.org/10.1029/2021GL092999, 2021.
Feng, D., Liu, J., Lawson, K., and Shen, C.: Differentiable, learnable,
regionalized process-based models with multiphysical outputs can approach
state-of-the-art hydrologic prediction accuracy, Water Resour. Res., 58,
e2022WR032404, https://doi.org/10.1029/2022WR032404, 2022.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst,
L., and Wiberg, D.: Global Agro-Ecological Zones Assessment for Agriculture
(GAEZ 2008), IIASA Laxenburg, Austria and FAO, Rome, Italy, 2008.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, 2001.
Gauch, M., Mai, J., and Lin, J.: The Proper Care and Feeding of CAMELS: How
Limited Training Data Affects Streamflow Prediction, Environ. Modell. Softw., 135, 104926, https://doi.org/10.1016/j.envsoft.2020.104926, 2021.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural. Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB)–a review, Hydrol. Sci. J.,
58, 1198–1255, https://doi.org/10/gfsq5q, 2013.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.:
GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06
(GPM_3IMERGDF 06), edited by: Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set],
https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Hunter-Jones, P.: Egg development in the Desert Locust (Schistocerca
gregaria Forsk.) in relation to the availability of water, Proc. R. Entomol.
Soc. A, 39, 25–33,
https://doi.org/10.1111/j.1365-3032.1964.tb00781.x, 1964.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F.,
Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S.
E., Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg,
S.: The SMOS mission: New tool for monitoring key elements of the global
water cycle, P. IEEE, 98, 666–687, https://doi.org/10/b9szx6, 2010.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Liu, J., Rahmani, F., Lawson, K., and Shen, C.: A multiscale deep learning
model for soil moisture integrating satellite and in situ data, Geophys.
Res. Lett., 49, e2021GL096847, https://doi.org/10.1029/2021GL096847, 2022a.
Liu, J., Hughes, D., Rahmani, F., Lawson, K., and Shen, C.: Global Soil Moisture Dataset From a Multitask Model (GSM3), Zenodo [data set], https://doi.org/10.5281/zenodo.7344484, 2022b.
Meyal, A. Y., Versteeg, R., Alper, E., Johnson, D., Rodzianko, A., Franklin,
M., and Wainwright, H.: Automated cloud based long short-term memory neural
network based SWE prediction, Front. Water, 2, 1–12,
https://doi.org/10.3389/frwa.2020.574917, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Narasimhan, B. and Srinivasan, R.: Development and evaluation of Soil
Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI)
for agricultural drought monitoring, Agr. Forest Meteorol., 133, 69–88,
https://doi.org/10/fq9wdv, 2005.
Natural Earth: Free vector and raster map data, Natural Earth [data set], https://www.naturalearthdata.com/, last access: 1 August 2022.
Norbiato, D., Borga, M., Degli Esposti, S., Gaume, E., and Anquetin, S.:
Flash flood warning based on rainfall thresholds and soil moisture
conditions: An assessment for gauged and ungauged basins, J. Hydrol., 362,
274–290, https://doi.org/10/dtw4hm, 2008.
Nuwer, R.: As Locusts Swarmed East Africa, This Tech Helped Squash Them, N.
Y. Times, 8th April, https://www.nytimes.com/2021/04/08/science/locust-swarms-africa.html (last access: 1 August 2022), 2021.
O, S. and Orth, R.: Global soil moisture data derived through machine
learning trained with in-situ measurements, Sci. Data, 8, 170,
https://doi.org/10.1038/s41597-021-00964-1, 2021.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell,
J., and Colliander, A.: SMAP Enhanced L3 Radiometer Global and Polar Grid
Daily 9 km EASE-Grid Soil Moisture, Version 5 (SPL3SMP_E), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/4DQ54OUIJ9DL, 2021.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys. Res., 113,
1–17, https://doi.org/10.1029/2007JF000769, 2008.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn:
Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Rahmani, F., Shen, C., Oliver, S., Lawson, K., and Appling, A.: Deep
learning approaches for improving prediction of daily stream temperature in
data-scarce, unmonitored, and dammed basins, Hydrol. Process., 35, e14400,
https://doi.org/10.1002/hyp.14400, 2021a.
Rahmani, F., Lawson, K., Ouyang, W., Appling, A., Oliver, S., and Shen, C.:
Exploring the exceptional performance of a deep learning stream temperature
model and the value of streamflow data, Environ. Res. Lett., 16, 024025,
https://doi.org/10.1088/1748-9326/abd501, 2021b.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Daily L3
Global – 500m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A3.061, 2021.
Sheffield, J. and Wood, E. F.: Global trends and variability in soil
moisture and drought characteristics, 1950–2000, from observation-driven
simulations of the terrestrial hydrologic cycle, J. Climate, 21, 432–458,
https://doi.org/10.1175/2007JCLI1822.1, 2008.
Shen, C.: A transdisciplinary review of deep learning research and its
relevance for water resources scientists, Water Resour. Res., 54,
8558–8593, https://doi.org/10.1029/2018wr022643, 2018.
Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky,
A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng,
Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P.,
Aboelyazeed, D., Rahmani, F., Beck, H. E., Bindas, T., Dwivedi, D., Fang,
K., Höge, M., Rackauckas, C., Roy, T., Xu, C., and Lawson, K.:
Differentiable modeling to unify machine learning and physical models and
advance Geosciences, arXiv [preprint], https://doi.org/10.48550/arXiv.2301.04027, 10 January
2023.
Support CATDS: CATDS-PDC L3SM Simple UDP – 1 day soil moisture Simple User
Data Product from SMOS satellite, CATDS (CNES, IFREMER, CESBIO) [data set]
https://doi.org/10.12770/8db7102b-1b22-4db3-949d-e51269417aae, 2022.
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12, 5988,
https://doi.org/10.1038/s41467-021-26107-z, 2021.
UN WFP: Stop locusts in East Africa now or pay much more to help people
later, U. N. UN World Food Programme WFP, 14th February, https://www.wfp.org/news/stop-locusts-east-africa-now-or-pay-much-more-help-people-later-wfp (last access: 1 August 2022), 2020.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface
Temperature/Emissivity Daily L3 Global 1km SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/MODIS/MYD11A1.061, 2021.
Xue, R., Yang, Q., Miao, F., Wang, X., Shen, Y., Xue, R., Yang, Q., Miao,
F., Wang, X., and Shen, Y.: Slope aspect influences plant biomass, soil
properties and microbial composition in alpine meadow on the Qinghai-Tibetan
plateau, J. Soil Sci. Plant Nut., 18, 1–12,
https://doi.org/10.4067/S0718-95162018005000101, 2018.
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Zhi, W., Feng, D., Tsai, W.-P., Sterle, G., Harpold, A., Shen, C., and Li,
L.: From hydrometeorology to river water quality: Can a deep learning model
predict dissolved oxygen at the continental scale?, Environ. Sci. Technol.,
55, 2357–2368, https://doi.org/10.1021/acs.est.0c06783, 2021.
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Under-monitored regions like Africa need high-quality soil moisture predictions to help with...
