Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2739-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2739-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2021
Research article |
| 25 May 2021
| 25 May 2021
Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques
Earth System Science, Stanford University, Stanford, CA, USA
Anna Fryjoff-Hung
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Andreas Anderson
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Joshua H. Viers
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, USA
Teamrat A. Ghezzehei
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Life and Environmental Science, University of California, Merced, Merced, CA, USA
Related authors
Samuel N. Araya, Jeffrey P. Mitchell, Jan W. Hopmans, and Teamrat A. Ghezzehei
SOIL, 8, 177–198, https://doi.org/10.5194/soil-8-177-2022, https://doi.org/10.5194/soil-8-177-2022, 2022
Short summary
Short summary
We studied the long-term effects of no-till (NT) and winter cover cropping (CC) practices on soil hydraulic properties. We measured soil water retention and conductivity and also conducted numerical simulations to compare soil water storage abilities under the different systems. Soils under NT and CC practices had improved soil structure. Conservation agriculture practices showed marginal improvement with respect to infiltration rates and water storage.
Teneille Nel, Manisha Dolui, Abbygail R. McMurtry, Stephanie Chacon, Joseph A. Mason, Laura M. Phillips, Erika Marin-Spiotta, Marie-Anne de Graaff, Asmeret A. Berhe, and Teamrat A. Ghezzehei
EGUsphere, https://doi.org/10.5194/egusphere-2025-5164, https://doi.org/10.5194/egusphere-2025-5164, 2025
This preprint is open for discussion and under review for SOIL (SOIL).
Short summary
Short summary
Buried ancient topsoils (Brady paleosol, Nebraska) sequester vast SOC. We found repeated drying/rewetting causes greater C loss than continuous wetting, destabilizing the slow-cycling C pool, especially in shallower soils. Decomposition rates are higher in erosional settings. Burial depth and moisture regime are key to the long-term vulnerability of these ancient C stocks under climate change.
Toshiyuki Bandai and Teamrat A. Ghezzehei
Hydrol. Earth Syst. Sci., 26, 4469–4495, https://doi.org/10.5194/hess-26-4469-2022, https://doi.org/10.5194/hess-26-4469-2022, 2022
Short summary
Short summary
Scientists use a physics-based equation to simulate water dynamics that influence hydrological and ecological phenomena. We present hybrid physics-informed neural networks (PINNs) to leverage the growing availability of soil moisture data and advances in machine learning. We showed that PINNs perform comparably to traditional methods and enable the estimation of rainfall rates from soil moisture. However, PINNs are challenging to train and significantly slower than traditional methods.
Samuel N. Araya, Jeffrey P. Mitchell, Jan W. Hopmans, and Teamrat A. Ghezzehei
SOIL, 8, 177–198, https://doi.org/10.5194/soil-8-177-2022, https://doi.org/10.5194/soil-8-177-2022, 2022
Short summary
Short summary
We studied the long-term effects of no-till (NT) and winter cover cropping (CC) practices on soil hydraulic properties. We measured soil water retention and conductivity and also conducted numerical simulations to compare soil water storage abilities under the different systems. Soils under NT and CC practices had improved soil structure. Conservation agriculture practices showed marginal improvement with respect to infiltration rates and water storage.
Jing Yan and Teamrat Ghezzehei
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-52, https://doi.org/10.5194/bg-2022-52, 2022
Publication in BG not foreseen
Short summary
Short summary
Although hydraulic redistribution (HR) is a well-documented phenomenon, whether it is a passive happy accident or actively controlled by roots is not well understood. Our modeling study suggests HR is long-range feedback between roots that inhabit heterogeneously resourced soil regions. When nutrients and organic matter are concentrated in shallow layers that experience frequent drying, root-exudation facilitated HR allows plants to mineralize and extract the otherwise inaccessible nutrients.
Daniel Rath, Nathaniel Bogie, Leonardo Deiss, Sanjai J. Parikh, Daoyuan Wang, Samantha Ying, Nicole Tautges, Asmeret Asefaw Berhe, Teamrat A. Ghezzehei, and Kate M. Scow
SOIL, 8, 59–83, https://doi.org/10.5194/soil-8-59-2022, https://doi.org/10.5194/soil-8-59-2022, 2022
Short summary
Short summary
Storing C in subsoils can help mitigate climate change, but this requires a better understanding of subsoil C dynamics. We investigated changes in subsoil C storage under a combination of compost, cover crops (WCC), and mineral fertilizer and found that systems with compost + WCC had ~19 Mg/ha more C after 25 years. This increase was attributed to increased transport of soluble C and nutrients via WCC root pores and demonstrates the potential for subsoil C storage in tilled agricultural systems.
Jing Yan, Nathaniel A. Bogie, and Teamrat A. Ghezzehei
Biogeosciences, 17, 6377–6392, https://doi.org/10.5194/bg-17-6377-2020, https://doi.org/10.5194/bg-17-6377-2020, 2020
Short summary
Short summary
An uneven supply of water and nutrients in soils often drives how plants behave. We observed that plants extract all their required nutrients from dry soil patches in sufficient quantity, provided adequate water is available elsewhere in the root zone. Roots in nutrient-rich dry patches facilitate the nutrient acquisition by extensive growth, water release, and modifying water retention in their immediate environment. The findings are valuable in managing nutrient losses in agricultural systems.
Cited articles
Ahmad, S., Kalra, A., and Stephen, H.:
Estimating soil moisture using remote sensing data: A machine learning approach,
Adv. Water Resour.,
33, 69–80, https://doi.org/10.1016/j.advwatres.2009.10.008, 2010.
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., and Notarnicola, C.:
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data,
Remote Sens.-Basel,
7, 16398–16421, https://doi.org/10.3390/rs71215841, 2015.
Ambroise, C. and McLachlan, G. J.:
Selection bias in gene extraction on the basis of microarray gene-expression data,
P. Natl. Acad. Sci. USA,
99, 6562–6566, https://doi.org/10.1073/pnas.102102699, 2002.
Anderson, K. and Gaston, K. J.:
Lightweight unmanned aerial vehicles will revolutionize spatial ecology,
Front. Ecol. Environ.,
11, 138–146, https://doi.org/10.1890/120150, 2013.
Apley, D. W. and Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models, Roy. Stat. Soc. B, 82, 1059–1086, https://doi.org/10.1111/rssb.12377, 2020.
Araya, S. N.: saraya209/uas-soil-moisture: First release of uas-soil-moisture code and data, Zenodo, https://doi.org/10.5281/zenodo.4743238, 2021.
Barrett, B. W. and Petropoulos, G. P.: Satellite Remote Sensing of Surface Soil Moisture, in: Remote Sensing of Energy Fluxes and Soil Moisture Content,
edited by: Petropoulos, G. P., CRC Press, Boca Raton, FL, 85–120, 2014.
Ben-Dor, E., Chabrillat, S., Demattê, J. A. M., Taylor, G. R., Hill, J., Whiting, M. L., and Sommer, S.:
Using Imaging Spectroscopy to study soil properties,
Remote Sens. Environ.,
113 (Suppl. 1),
S38–S55, https://doi.org/10.1016/j.rse.2008.09.019, 2009.
Ben-Shimon, D. and Shmilovici, A.: Kernels for the Relevance Vector Machine – An Empirical Study, in: Advances in Web Intelligence and Data Mining, edited by: Last, M., Szczepaniak, P. S., Volkovich, Z., and Kandel, A., Springer-Verlag GmbH, Berlin, Heidelberg, 253–263, 2006.
Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., and Fereres, E.: Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors, Int. Arch. Photogramm., 38, 6, https://doi.org/10.1007/s11032-006-9022-5, 2009.
Breiman, L.:
Random Forest,
Mach. Learn.,
45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brownlee, J.: Better Deep Learning Train Faster, Reduce Overfitting, and Make Better Predictions, 1.8., Machine Learning Mastery Pty., available at: https://machinelearningmastery.com/better-deep-learning/ (last access: 7 May 2021), 2020.
California Department of Water Resources:
UC Merced Weather Station,
available at: http://cdec.water.ca.gov/dynamicapp/staMeta?station_id=UCM (last access: 1 February 2019), 2018.
California Irrigation Management Information System:
CIMIS Station Report, available at: https://cimis.water.ca.gov/WSNReportCriteria.aspx (last access: 1 February 2019), 2018.
California Irrigation Management Information System:
Data Overview, available at: https://cimis.water.ca.gov/Resources.aspx, last access: 19 April 2019.
Caruana, R. and Niculescu-Mizil, A.:
An empirical comparison of supervised learning algorithms,
in: Proceedings of the 23rd international conference on Machine learning – ICML '06, ACM Press, New York, USA, 161–168, 2006.
Chen, T. and Guestrin, C.:
XGBoost: A Scalable Tree Boosting System,
in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16, ACM Press, New York, USA, 785–794, 2016.
Chen, X. and Jeong, J. C.: Enhanced Recursive Feature Elimination, in: Proc. – 6th Int. Conf. Mach. Learn. Appl. ICMLA 2007, 13–15 December 2007, Cincinnati, OH, USA, 330–335, https://doi.org/10.1109/ICMLA.2007.35, 2007.
Colomina, I. and Molina, P.:
Unmanned aerial systems for photogrammetry and remote sensing: A review,
ISPRS J. Photogramm.,
92, 79–97, https://doi.org/10.1016/j.isprsjprs.2014.02.013, 2014.
Cortes, C. and Vapnik, V. N.:
Support-Vector Networks,
Mach. Learn.,
20, 273–297, https://doi.org/10.1023/A:1022627411411, 1995.
Das, N. N. and Mohanty, B. P.:
Root Zone Soil Moisture Assessment Using Remote Sensing and Vadose Zone Modeling,
Vadose Zone J.,
5, 296, https://doi.org/10.2136/vzj2005.0033, 2006.
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V. N.: Support vector regression machines, in: Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference, edited by: Mozer, M. C., Jordan, M. I., and Petsche, T., MIT Press, Cambridge, Massachusetts, 155–161, 1997.
Elarab, M.: The Application of Unmanned Aerial Vehicle to Precision Agriculture: Chlorophyll, Nitrogen, and Evapotranspiration Estimation, Utah State University, Utah, 2016.
Elith, J., Leathwick, J. R., and Hastie, T.:
A working guide to boosted regression trees,
J. Anim. Ecol.,
77, 802–813, https://doi.org/10.1111/j.1365-2656.2008.01390.x, 2008.
Famiglietti, J. S., Rudnicki, J. W., and Rodell, M.:
Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas,
J. Hydrol.,
210, 259–281, https://doi.org/10.1016/S0022-1694(98)00187-5, 1998.
Friedman, J. H.:
Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat.,
29, 1189–1232, https://doi.org/10.1017/CBO9781107415324.004, 2001.
Friedman, J. H.:
Stochastic gradient boosting,
Comput. Stat. Data An.,
38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002.
Fryjoff-Hung, A. F.:
2D Hydrodynamic Modeling for Evaluating Restoration Potential of a Vernal Pool Complex,
University of California, Merced, 2018.
Georganos, S., Grippa, T., Gadiaga, A. N., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N. O., Wolff, E., and Kalogirou, S.: Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling, Geocarto Int., 36, 121–136, https://doi.org/10.1080/10106049.2019.1595177, 2019.
Greenwell, B. M.:
pdp: An R Package for Constructing Partial Dependence Plots, R J., 9,
available at: https://github.com/bgreenwell/pdp/issues (last access: 13 June 2019), 2017.
Guyon, I. and Elisseeff, A.:
An Introduction to Variable and Feature Selection,
J. Mach. Learn. Res.,
3, 1157–1182, 2003.
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389–422, https://doi.org/10.1023/A:1012487302797, 2002.
Hassan-Esfahani, L., Torres-Rua, A., Jensen, A., and McKee, M.:
Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks,
Remote Sens.-Basel,
7, 2627–2646, https://doi.org/10.3390/rs70302627, 2015.
Hastie, T., Tibshirani, R., and Friedman, J.:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn.,
Springer New York, New York, NY, 2009.
Haubrock, S.-N., Chabrillat, S., Lemmnitz, C., and Kaufmann, H.:
Surface soil moisture quantification models from reflectance data under field conditions,
Int. J. Remote Sens.,
29, 3–29, https://doi.org/10.1080/01431160701294695, 2008.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.:
SoilGrids250m: Global gridded soil information based on machine learning,
edited by: Bond-Lamberty, B.,
PLoS One,
12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler, B.:
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables,
PeerJl,
6, e5518, https://doi.org/10.7717/peerj.5518, 2018.
Hillel, D.:
Environmental Soil Physics,
Academic Press, San Diego, CA, 1998.
James, G., Witten, D., Hastie, T., and Tibshirani, R.:
An Introduction to Statistical Learning,
Springer New York, New York, NY, 2013.
Jenness, J., Brost, B., and Beier, P.: Land Facet Corridor Designer, available at: http://www.corridordesign.org (last access: 13 June 2019), 2013.
Karatzoglou, A., Smola, A., Hornik, K., and Zeileis, A.: kernlab – An S4 Package for Kernel Methods in R, J. Stat. Softw., 11, 1–20, https://doi.org/10.18637/jss.v011.i09, 2004.
Keskin, H., Grunwald, S., and Harris, W. G.:
Digital mapping of soil carbon fractions with machine learning,
Geoderma,
339, 40–58, https://doi.org/10.1016/j.geoderma.2018.12.037, 2019.
Korres, W., Reichenau, T. G., Fiener, P., Koyama, C. N., Bogena, H. R., Cornelissen, T., Baatz, R., Herbst, M., Diekkrüger, B., Vereecken, H., and Schneider, K.:
Spatio-temporal soil moisture patterns – A meta-analysis using plot to catchment scale data,
J. Hydrol.,
520, 326–341, https://doi.org/10.1016/j.jhydrol.2014.11.042, 2015.
Kuhn, M.: Building Predictive Models in R Using the caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, 2002.
Malley, D. F., Martin, P. D., and Ben-Dor, E.:
Application in Analysis of Soils,
in: Near-Infrared Spectroscopy in Agriculture,
edited by: Craig, R., Windham, R., and Workman, J.,
American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI, USA, 729–784, 2004.
Manfreda, S., McCabe, M., Miller, P., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., Müllerová, J., Tauro, F., de Lima, M., de Lima, J., Maltese, A., Frances, F., Caylor, K., Kohv, M., Perks, M., Ruiz-Pérez, G., Su, Z., Vico, G., and Toth, B.:
On the Use of Unmanned Aerial Systems for Environmental Monitoring,
Remote Sens.-Basel,
10, 641, https://doi.org/10.3390/rs10040641, 2018.
Matei, O., Rusu, T., Petrovan, A., and Mihuţ, G.:
A Data Mining System for Real Time Soil Moisture Prediction,
Procedia Engineer.,
181, 837–844, https://doi.org/10.1016/j.proeng.2017.02.475, 2017.
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Git Hub, available at: https://christophm.github.io/interpretable-ml-book/, last access: 13 June 2019.
Moore, I. D., Burch, G. J., and Mackenzie, D. H.:
Topographic Effects on the Distribution of Surface Soil Water and the Location of Ephemeral Gullies,
T. ASAE,
31, 1098–1107, https://doi.org/10.13031/2013.30829, 1988.
Muller, E. and Décamps, H.:
Modeling soil moisture–reflectance,
Remote Sens. Environ.,
76, 173–180, https://doi.org/10.1016/S0034-4257(00)00198-X, 2000.
Natekin, A. and Knoll, A.:
Gradient boosting machines, a tutorial,
Front. Neurorobotics,
7, 1–11, https://doi.org/10.3389/fnbot.2013.00021, 2013.
Nichols, S., Zhang, Y., and Ahmad, A.:
Review and evaluation of remote sensing methods for soil-moisture estimation,
J. Photon. Energy,
2, 028001, https://doi.org/10.1117/1.3534910, 2011.
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, 2018.
Ochsner, T. E., Cosh, M. H., Cuenca, R. H., Dorigo, W. A., Draper, C. S., Hagimoto, Y., Kerr, Y. H., Njoku, E. G., Small, E. E., and Zreda, M.:
State of the Art in Large-Scale Soil Moisture Monitoring,
Soil Sci. Soc. Am. J.,
77, 1888, https://doi.org/10.2136/sssaj2013.03.0093, 2013.
Pachepsky, Y. A., Timlin, D., and Varallyay, G.:
Artificial Neural Networks to Estimate Soil Water Retention from Easily Measurable Data,
Soil Sci. Soc. Am. J.,
60, 727–733, https://doi.org/10.2136/sssaj1996.03615995006000030007x, 1996.
Paloscia, S., Pampaloni, P., Pettinato, S., and Santi, E.:
A Comparison of Algorithms for Retrieving Soil Moisture from ENVISAT/ASAR Images,
IEEE T. Geosci. Remote,
46, 3274–3284, https://doi.org/10.1109/TGRS.2008.920370, 2008.
Petropoulos, G. P., Ireland, G., and Barrett, B. W.:
Surface soil moisture retrievals from remote sensing: Current status, products & future trends,
Phys. Chem. Earth,
83–84, 36–56, https://doi.org/10.1016/j.pce.2015.02.009, 2015.
Price, J. C.:
Thermal inertia mapping: A new view of the earth,
J. Geophys. Res.,
82, 2582–2590, 1977.
Price, J. C.:
On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia,
Remote Sens. Environ.,
18, 59–73, https://doi.org/10.1016/0034-4257(85)90038-0, 1985.
Rana, G. and Katerji, N.:
Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review,
Eur. J. Agron.,
13, 125–153, https://doi.org/10.1016/S1161-0301(00)00070-8, 2000.
Reed, R. and Marks II, R. J.:
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks,
MIT Press, Cambridge, Massachusetts, 1999.
Ridgeway, G.: Generalized Boosted Models: A guide to the gbm package, available at: https://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf (last access: 1 January 2018) 2012.
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.:
Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions,
J. Hydrol.,
251, 163–176, https://doi.org/10.1016/S0022-1694(01)00466-8, 2001.
Schanda, E.:
Physical Fundamentals of Remote Sensing,
Springer-Verlag, Berlin, Germany, 1986.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.:
Investigating soil moisture-climate interactions in a changing climate: A review,
Earth-Sci. Rev.,
99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Singh, K. K. and Frazier, A. E.:
A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications,
Int. J. Remote Sens.,
39, 5078–5098, https://doi.org/10.1080/01431161.2017.1420941, 2018.
Sobrino, J., Mattar, C., Jiménez-Muñoz, J. C., Franch, B., and Corbari, C.: On the Synergy between Optical and TIR Observations for the Retrieval of Soil Moisture Content, in: Remote Sensing of Energy Fluxes and Soil Moisture Content, edited by: Petropoulos, G. P., CRC Press, Boca Raton, FL, 363–390, 2014.
Sørensen, R., Zinko, U., and Seibert, J.: On the calculation of the topographic wetness index: evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci., 10, 101–112, https://doi.org/10.5194/hess-10-101-2006, 2006.
Stark, B., McGee, M., and Chen, Y.: Short wave infrared (SWIR) imaging systems using small Unmanned Aerial Systems (sUAS), in: IEEE 2015 International Conference on Unmanned Aircraft Systems (ICUAS), 9–12 June 2015, Denver, CO, 495–501, 2015.
Szabó, B., Szatmári, G., Takács, K., Laborczi, A., Makó, A., Rajkai, K., and Pásztor, L.: Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics, Hydrol. Earth Syst. Sci., 23, 2615–2635, https://doi.org/10.5194/hess-23-2615-2019, 2019.
Tian, J., Su, H., He, H., and Sun, X.:
An Empirical Method of Estimating Soil Thermal Inertia,
Adv. Meteorol.,
2015, 1–9, https://doi.org/10.1155/2015/428525, 2015.
Tipping, M. E.: The Relevance Vector Machine, in: Advances in Neural Information Processing Systems 12, edited by: Solla, S. A., Leen, T. K., and Muller, K., MIT Press, Cambridge, MA, 652–658, 2000.
Tmušić, G., Manfreda, S., Aasen, H., James, M. R., Gonçalves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J. J., Mészáros, J., Zhuang, R., Johansen, K., Malbeteau, Y., de Lima, I. P., Davids, C., Herban, S., and McCabe, M. F.:
Current Practices in UAS-based Environmental Monitoring,
Remote Sens.-Basel,
12, 1001, https://doi.org/10.3390/rs12061001, 2020.
Torres-Rua, A., Ticlavilca, A., Bachour, R., and McKee, M.:
Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines,
Water,
8, 167, https://doi.org/10.3390/w8040167, 2016.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.:
Earth's Global Energy Budget,
B. Am. Meteorol. Soc.,
90, 311–323, https://doi.org/10.1175/2008BAMS2634.1, 2009.
Vereecken, H., Huisman, J. A., Pachepsky, Y. A., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., and Vanderborght, J.:
On the spatio-temporal dynamics of soil moisture at the field scale,
J. Hydrol.,
516, 76–96, https://doi.org/10.1016/j.jhydrol.2013.11.061, 2014.
Weidong, L., Baret, F., Xingfa, G., Qingxi, T., Lanfen, Z., and Bing, Z.:
Relating soil surface moisture to reflectance,
Remote Sens. Environ.,
81, 238–246, https://doi.org/10.1016/S0034-4257(01)00347-9, 2002.
Western, A. W., Grayson, R. B., Blöschl, G., Willgoose, G. R., and McMahon, T. A.:
Observed spatial organization of soil moisture and its relation to terrain indices,
Water Resour. Res.,
35, 797–810, https://doi.org/10.1029/1998WR900065, 1999.
Weston, J., Elisseeff, A., Schölkopf, B., and Tipping, M.:
Use of the Zero-Norm with Linear Models and Kernel Methods,
J. Mach. Learn. Res.,
3, 1439–1461, 2003.
Whiting, M. L., Li, L., and Ustin, S. L.:
Predicting water content using Gaussian model on soil spectra,
Remote Sens. Environ.,
89, 535–552, https://doi.org/10.1016/j.rse.2003.11.009, 2004.
Willmott, C. and Matsuura, K.:
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,
Clim. Res.,
30, 79–82, https://doi.org/10.3354/cr030079, 2005.
Wilson, J. P. and Gallant, J. C.: Digital Terrain Analysis, in: Terrain Analysis: principles and applications, edited by: Wilson, J. P. and Gallant, J. C., John Wiley & Sons, Inc, New York, NY, 1–21, 2000.
Wong, K.: Merced Vernal Pools Joins Natural Reserve System, Univ. Calif. News, 22 January 2014, available at: http://universityofcalifornia.edu/news/merced-vernal-pools-join-natural-reserve-system (last access: 5 August 2020), 2014.
Zaman, B. and Mckee, M.:
Spatio-Temporal Prediction of Root Zone Soil Moisture Using Multivariate Relevance Vector Machines,
Open J. Mod. Hydrol.,
4, 80–90, https://doi.org/10.4236/ojmh.2014.43007, 2014.
Zaman, B., McKee, M., and Neale, C. M. U.:
Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines,
Int. J. Remote Sens.,
33, 6516–6552, https://doi.org/10.1080/01431161.2012.690540, 2012.
Zhang, D. and Zhou, G.:
Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review,
Sensors,
16, 1308, https://doi.org/10.3390/s16081308, 2016.
Zhang, Y. and Schaap, M. G.:
Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3),
J. Hydrol.,
547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017.
Zhang, Y., Schaap, M. G., and Zha, Y.:
A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model,
Water Resour. Res.,
54, 9774–9790, https://doi.org/10.1029/2018WR023539, 2018.
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
Short summary
We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images to estimate soil moisture via machine learning. We were able to estimate soil moisture with high accuracy. Furthermore, by analyzing the machine learning models we developed, we learned how different factors drive the distribution of moisture across the landscape. Among the factors, rainfall, evapotranspiration, and topography were most important in controlling surface soil moisture distribution.
We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images...
