Downscaling satellite soil moisture using geomorphometry and machine learning


Autoři: Mario Guevara aff001;  Rodrigo Vargas aff001
Působiště autorů: University of Delaware, Department of Plant and Soil Sciences, Newark, DE aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0219639

Souhrn

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

Klíčová slova:

Machine learning – Principal component analysis – Soil ecology – Topographic maps – Topography – Valleys – Terrain – Remote sensing


Zdroje

1. Greve P., Gudmundsson L., Seneviratne S.I., 2018. Regional scaling of annual mean precipitation and water availability with global temperature change. Earth Syst. Dynam. 9, 227–240. https://doi.org/10.5194/esd-9-227-2018

2. Seneviratne S.I., Corti T., Davin E.L., Hirschi M., Jaeger E.B., Lehner, et al. 2010. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews 99, 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004

3. Seneviratne S.I., Wilhelm M., Stanelle T., van den Hurk B., Hagemann S., Berg,et al. 2013. Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment: GLACE-CMIP5 EXPERIMENT. Geophysical Research Letters 40, 5212–5217. https://doi.org/10.1002/grl.50956

4. Western A.W., Grayson R.B., Blöschl G., Willgoose G.R., McMahon T.A., 1999. Observed spatial organization of soil moisture and its relation to terrain indices. Water Resour. Res. 35, 797–810. https://doi.org/10.1029/1998WR900065

5. Dorigo W., Wagner W., Albergel C., Albrecht F., Balsamo G., Brocca, et al. 2017. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, Earth Observation of Essential Climate Variables 203, 185–215. https://doi.org/10.1016/j.rse.2017.07.001

6. Stocker B. D., Zscheischler J., Keenan T. F., Prentice I. C., Peñuelas J., & Seneviratne S. I. 2018. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol., 218(4), 1430–1449. doi: 10.1111/nph.15123 29604221

7. Brocca L., Ciabatta L., Massari C., Camici S., & Tarpanelli A. 2017. Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water, 9(2), 140. doi: 10.3390/w9020140

8. Vargas R., Sánchez-Cañete P., Serrano-Ortiz P., Curiel Yuste J., Domingo F., López- Ballesteros A. et al. 2018. Hot-moments of soil CO2 efflux in a water-limited grassland. Soil Systems, 2(3), p.47.

9. Asner G.P., Alencar A., 2010. Drought impacts on the Amazon forest: the remote sensing perspective. New phytologist.

10. Cook B.D., Corp L.A., Nelson R.F., Middleton E.M., Morton D.C., McCorkel J.T., et al. 2013. NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sensing 5, 4045–4066. https://doi.org/10.3390/rs5084045

11. Dai A., 2011. Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change 2, 45–65. https://doi.org/10.1002/wcc.81

12. Samaniego L., Thober S., Kumar R., Wanders N., Rakovec O., Pan,et al. 2018. Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change 8, 421–426. https://doi.org/10.1038/s41558-018-0138-5

13. van der Molen M.K., Dolman A.J., Ciais P., Eglin T., Gobron N., Law B.E. et al. 2011. Drought and ecosystem carbon cycling. Agricultural and Forest Meteorology 151, 765–773. https://doi.org/10.1016/j.agrformet.2011.01.018

14. Luo Y., Ahlström A., Allison S.D., Batjes N.H., Brovkin V., Carvalhais, et al., 2016. Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochemical Cycles 30, 40–56. https://doi.org/10.1002/2015GB005239

15. Walsh B., Ciais P., Janssens I.A., Peñuelas J., Riahi K., Rydzak F., et al., 2017. Pathways for balancing CO2 emissions and sinks. Nature Communications 8, 14856. doi: 10.1038/ncomms14856 28406154

16. Owe M., Van de Griend A.A., 1998. Comparison of soil moisture penetration depths for several bare soils at two microwave frequencies and implications for remote sensing. Water Resources Research 34, 2319–2327. https://doi.org/10.1029/98WR01469

17. Entekhabi D., Yueh S., O’Neill P., Kellog K., Allen A., et al., 2014. SMAP handbook—Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw From Space, Jet Propulsion Lab., California Inst. Technol., Pasadena, Calif.

18. Singh R.S., Reager J.T., Miller N.L., Famiglietti J.S., 2015. Toward hyper-resolution land-surface modeling: The effects of fine-scale topography and soil texture on CLM4.0 simulations over the Southwestern U.S.: Effects of fine-scale resolution on CLM4.0 in Southwest US. Water Resources Research 51, 2648–2667. https://doi.org/10.1002/2014WR015686

19. Dirmeyer P., Wu J., Norton H., Dorigo W., Quiring S., Trenton W., et al. 2016. “Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States.” Journal of Hydrometeorology 17 (4): 1049–67. doi: 10.1175/JHM-D-15-0196.1 29645013

20. Liu Y.Y., Dorigo W.A., Parinussa R.M., de Jeu R.A.M., Wagner W., McCabe, et al. 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment 123, 280–297. https://doi.org/10.1016/j.rse.2012.03.014

21. Liu Y.Y., Parinussa R.M., Dorigo W.A., De Jeu R.A.M., Wagner W., van Dijk A.I.J.M., et al. 2011. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences 15, 425–436. https://doi.org/10.5194/hess-15-425-2011

22. McColl K.A., Alemohammad S.H., Akbar R., Konings A.G., Yueh S., Entekhabi D., 2017. The global distribution and dynamics of surface soil moisture. Nature Geoscience 10, 100–104. https://doi.org/10.1038/ngeo2868

23. Montzka C., Rötzer K., Bogena H.R., and Vereecken H. 2018. A new soil moisture downscaling approach for SMAP, SMOS and ASCAT by predicting sub-grid variability. Remote Sens. 10(3):427. doi: 10.3390/rs10030427

24. Afshar M.H., Yilmaz M.T., 2017. The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products. Remote Sensing of Environment 196, 224–237. https://doi.org/10.1016/j.rse.2017.05.017

25. Jin Y., Ge Y., Wang J., Heuvelink G.B.M., Wang L., 2018. Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing. Remote Sensing 10, 579. https://doi.org/10.3390/rs10040579

26. Kearney M.R., Maino J.L., 2018. Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment. Journal of Hydrology 561, 662–673. https://doi.org/10.1016/j.jhydrol.2018.04.040

27. Piles M., Camps A., Vall-llossera M., Corbella I., Panciera R., Rudiger C., Kerr Y.H., et al., 2011. Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data. IEEE Transactions on Geoscience and Remote Sensing 49, 3156–3166. https://doi.org/10.1109/TGRS.2011.2120615

28. Ranney K.J., Niemann J.D., Lehman B.M., Green T.R., Jones A.S., 2015. A method to downscale soil moisture to fine resolutions using topographic, vegetation, and soil data. Advances in Water Resources 76, 81–96. https://doi.org/10.1016/j.advwatres.2014.12.003

29. Wang A., Zhang M., Shi J., Mu T., Gong H., Xie C., 2012. Space-time analysis on downscaled soil moisture data and parameters of plant growth. Transactions of the Chinese Society of Agricultural Engineering 28, 164–169.

30. Yu G., Di L., Yang W., 2008. Downscaling of Global Soil Moisture using Auxiliary Data. IEEE, pp. III-230–III–233. https://doi.org/10.1109/IGARSS.2008.4779325

31. McBratney A., Mendonça Santos M., Minasny B., 2003. On digital soil mapping. Geoderma 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4

32. Bauer-Marschallinger B., Freeman V., Cao S., Paulik C., Schaufler S., Stachl T., et al. (2019). Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Trans. Geosci. Remote Sens., 57(1), 520–539. doi: 10.1109/TGRS.2018.2858004

33. Morelo B., Merlin O., Malbeteau Y., Al Bitar A., Cabot F., Stefan V., et al. 2016. SMOS disaggregated soil moisture product at 1km resolution: Processor overview and first validation results. Remote Sensing of Environment 180, 361–376 https://doi.org/10.1016/j.rse.2016.02.045

34. Pike R.J., Evans I.S., T., 2009. Chapter 1 Geomorphometry: A Brief Guide, in: Developments in Soil Science. Elsevier, pp. 3–30.

35. Wilson J. P., & Gallant J. C. (2000). Digital terrain analysis. Terrain analysis: Principles and applications, 6(12), 1–27.

36. Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., et al. 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8, 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015

37. Wilson, J.P., 2012. Digital terrain modeling. Geomorphology, Geospatial Technologies and Geomorphological Mapping Proceedings of the 41st Annual Binghamton Geomorphology Symposium 137, 107–121. https://doi.org/10.1016/j.geomorph.2011.03.012

38. Florinsky I.V., 2016. Chapter 9—Influence of Topography on Soil Properties, in: Florinsky I.V. (Ed.), Digital Terrain Analysis in Soil Science and Geology (Second Edition). Academic Press, pp. 265–270. https://doi.org/10.1016/B978-0-12-804632-6.00009-2

39. Florinsky I.V., 2012. The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication). Eurasian Soil Science 45, 445–451. https://doi.org/10.1134/S1064229312040047

40. Pellenq J, Kalma J, Boulet G, Saulnier G-M, Wooldridge S. et al. A disaggregation scheme for soil moisture based on topography and soil depth. Journal of Hydrology. 2003;276: 112–127. doi: 10.1016/s0022-1694(03)00066-0

41. Busch FA, Niemann JD, Coleman M. Evaluation of an empirical orthogonal function-based method to downscale soil moisture patterns based on topographical attributes. Hydrological Processes. 2011;26: 2696–2709. doi: 10.1002/hyp.8363

42. Hengl T., MacMillan R.A., 2019. Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.

43. Reichstein M., Camps-Valls G., Stevens B., Jung M., Denzler J., Carvalhais N., Et al. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195. doi: 10.1038/s41586-019-0912-1 30760912

44. Guevara M, Olmedo GF, Stell E, Yigini Y, Aguilar Duarte Y, Arellano Hernández C, et al. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America. SOIL. 2018;4: 173–193. doi: 10.5194/soil-4-173-2018

45. Warner D.L., Guevara M., Inamdar S. and Vargas R., 2019. Upscaling soil-atmosphere CO2 and CH4 fluxes across a topographically complex forested landscape. Agricultural and forest meteorology, 264, pp.80–91.

46. Coopersmith E. J., Cosh M. H., Bell J. E., & Boyles R. 2016. Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation. Adv. Water Resour., 98, 122–131. doi: 10.1016/j.advwatres.2016.10.007

47. Quiring S.M., Ford T.W., Wang J.K., Khong A., Harris E., Lindgren T.,et al., 2016. The North American Soil Moisture Database: Development and Applications. Bulletin of the American Meteorological Society 97, 1441–1459. https://doi.org/10.1175/BAMS-D-13-00263.1

48. Dorigo W., Wagner W., Albergel C., Albrecht F., Balsamo G., Brocca, et al. 2017. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, Earth Observation of Essential Climate Variables 203, 185–215. https://doi.org/10.1016/j.rse.2017.07.001

49. Bindlish R., Jackson T., Cosh M., Tianjie Zhao O ’Neill P., 2015. Global Soil Moisture From the Aquarius/SAC-D Satellite: Description and Initial Assessment. IEEE Geoscience and Remote Sensing Letters 12, 923–927. https://doi.org/10.1109/LGRS.2014.2364151

50. Entekhabi D., Njoku E., O’Neill P., Kellogg K., Crow W., Edelstein W., et al. 2010. The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE 98, 704–716. https://doi.org/10.1109/JPROC.2010.2043918

51. Naeimi V., Paulik C., Bartsch A., Wagner W., Kidd R., Park, et al. ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm. IEEE Transactions on Geoscience and Remote Sensing 50, 2566–2582. https://doi.org/10.1109/TGRS.2011.2177667

52. Naeimi V., Scipal K., Bartalis Z., Hasenauer S., Wagner W., 2009. An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations. IEEE Transactions on Geoscience and Remote Sensing 47, 1999–2013. https://doi.org/10.1109/TGRS.2008.2011617

53. Wagner W., Lemoine G., Rott H., 1999. A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data. Remote Sensing of Environment 70, 191–207. https://doi.org/10.1016/S0034-4257(99)00036-X

54. Dorigo W.A., Wagner W., Hohensinn R., Hahn S., Paulik C., Xaver A., et al. 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. https://doi.org/10.5194/hess-15-1675-2011

55. Becker J.J., Sandwell D.T., Smith W.H.F., Braud J., Binder B., Depner J., et al. Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS. Marine Geodesy 32, 355–371. https://doi.org/10.1080/01490410903297766

56. Hengl T., de Jesus J., MacMillan R., Batjes N., Heuvelink GBM., Ribeiro E, et al. SoilGrids1km—Global Soil Information Based on Automated Mapping. Bond-Lamberty B, editor. PLoS ONE. 2014;9: e105992. doi: 10.1371/journal.pone.0105992 25171179

57. Tuanmu M.-N., & Jetz W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Global Ecol. Biogeogr., 23(9), 1031–1045. 2014. doi: 10.1111/geb.12182

58. Thuleau S, and Husson F. 2018. FactoInvestigate: Automatic Description of Factorial Analysis. R package version 1.3. https://CRAN.R-project.org/package=FactoInvestigate

59. Hechenbichler, K., Schliep, K., 2006. Weighted k-nearest-neighbor techniques and ordinal classification, in: Discussion Paper 399, SFB 386.

60. Hechenbichler, K., Schliep, K., 2004. Weighted k-Nearest-Neighbor Techniques and Ordinal Classification [WWW Document]. URL https://epub.ub.uni-muenchen.de/1769/ (accessed 12.24.16).

61. Borra S., Di Ciaccio A., 2010. Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics & Data Analysis 54, 2976–2989. https://doi.org/10.1016/j.csda.2010.03.004

62. Oliver M. A., & Webster R. 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 113, 56–69. doi: 10.1016/j.catena.2013.09.006

63. Hiemstra P. H., Pebesma E. J., Twenhöfel C. J. W., & Heuvelink G. B. M. 2009. Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput. Geosci., 35(8), 1711–1721. doi: 10.1016/j.cageo.2008.10.011

64. R Core Team 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

65. Guevara M. Vargas R. Protocol for Downscaling Satellite Soil Moisture Estimates using Geomorphometry and Machine Learning. Protocols.io. protocols.io; 1970; doi: 10.17504/protocols.io.6cahase

66. Colliander A., Fisher J. B., Halverson G., Merlin O., Misra S., Bindlish, et al. 2017. Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI During SMAPVEX15. IEEE Geosci. Remote Sens. Lett., 14(11), 2107–2111. doi: 10.1109/LGRS.2017.2753203

67. He L., Chen J. M., Liu J., Bélair S., & Luo X. 2017. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res. Biogeosci., 122(7), 1549–1563. doi: 10.1002/2016JG003603

68. Tuttle S. & Salvucci G. 2016. Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science 352, 825–828. doi: 10.1126/science.aaa7185 27174987

69. Lawston P. M., Santanello J. A., & Kumar S. V. 2017. Irrigation Signals Detected From SMAP Soil Moisture Retrievals. Geophys. Res. Lett., 44(23), 11,860–11,867. doi: 10.1002/2017GL075733

70. Colliander A., Jackson T. J., Bindlish R., Chan S., Das N., Kim S. B.,et al. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sens. Environ., 191, 215–231. doi: 10.1016/j.rse.2017.01.021

71. Diffenbaugh N. S., Swain D. L., & Touma D. 2015. Anthropogenic warming has increased drought risk in California. Proc. Natl. Acad. Sci. U.S.A., 112(13), 3931–3936. doi: 10.1073/pnas.1422385112 25733875

72. Easterling D. R., Kunkel K. E., Arnold J. R., Knutson T., LeGrande A. N., Leung L. et al. 2017. Precipitation change in the United States. In Wuebbles D. J., Fahey D. W., Hibbard K. A., Dokken D. J., Stewart B. C., & Maycock T. K. (Eds.), Climate science special report: Fourth national climate assessment (Vol. I, pp. 207–230). Washington, DC: U.S. Global Change Research Program

73. Vilasa L, Miralles DG, de Jeu RAM, Dolman AJ. Global soil moisture bimodality in satellite observations and climate models. Journal of Geophysical Research: Atmospheres. 2017;122: 4299–4311. doi: 10.1002/2016jd026099

74. Dyer J., & Mercer A. 2013. Assessment of Spatial Rainfall Variability over the Lower Mississippi River Alluvial Valley. J. Hydrometeorol., 14(6), 1826–1843. doi: 10.2307/24914344

75. Reba M. L., Massey J. H., Adviento-Borbe M. A., Leslie D., Yaeger M. A., Anders M., et al. 2017. Aquifer Depletion in the Lower Mississippi River Basin: Challenges and Solutions. Journal of Contemporary Water Research & Education, 162(1), 128–139. doi: 10.1111/j.1936-704X.2017.03264.x

76. Heuvelink G.B. M., Millward A.A., 1999. Error propagation in environmental modelling with GIS. Cartographica 36, 69.

77. Miralles D.G., Crow W.T., Cosh M.H., 2010. Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations. Journal of Hydrometeorology 11, 1423–1429. https://doi.org/10.1175/2010JHM1285.1

78. Munguia-Flores F., Arndt S., Ganesan A. L., Murray-Tortarolo G., & Hornibrook E. R. C. 2018. Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geosci. Model Dev., 11(6), 2009–2032. doi: 10.5194/gmd-11-2009-2018

79. Lindsay J.B, Creed I.F. Removal of artifact depressions from digital elevation models: towards a minimum impact approach. Hydrological Processes. 2005;19: 3113–3126. doi: 10.1002/hyp.5835

80. Planchon O. & Darboux F. (2001): A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena 46: 159–176

81. Gruber A., Dorigo W.A., Zwieback S., Xaver A., Wagner W., 2013. Characterizing Coarse-Scale Representativeness of in situ Soil Moisture Measurements from the International Soil Moisture Network. Vadose Zone Journal 12, 0. https://doi.org/10.2136/vzj2012.0170

82. Nicolai-Shaw N., Hirschi M., Mittelbach H., Seneviratne S.I., 2015. Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data: SPATIAL REPRESENTATIVENESS OF SOIL MOISTURE. Journal of Geophysical Research: Atmospheres 120, 9955–9964. https://doi.org/10.1002/2015JD023305

83. Vargas R., Sonnentag O., Abramowitz G., Carrara A., Chen J.M., Ciais P., et al. 2013. Drought influences the accuracy of simulated ecosystem fluxes: a model-data meta-analysis for Mediterranean oak woodlands. Ecosystems, 16(5), pp.749–764.

84. Nelson A., Reuter H.I., Gessler P., 2009. Chapter 3 DEM Production Methods and Sources, in: Developments in Soil Science. Elsevier, pp. 65–85.

85. Tadono T., Ishida H., Oda F., Naito S., Minakawa K., Iwamoto H., 2014. Precise Global DEM Generation by ALOS PRISM. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II–4, 71–76. https://doi.org/10.5194/isprsannals-II-4-71-2014

86. Schwingshackl C., Hirschi M., Seneviratne S. I., Schwingshackl C., Hirschi M., & Seneviratne S. I. 2017. Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature. J. Clim. Retrieved from https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-16-0727.1

87. Jin Y., Ge Y., Wang J., Heuvelink G.B.M., Wang L., 2018. Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing. Remote Sensing 10, 579. https://doi.org/10.3390/rs10040579

88. Mason D. C., Garcia-Pintado J., Cloke H. L. and Dance S. L.: Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data, International Journal of Applied Earth Observation and Geoinformation, 45, 178–186, doi: 10.1016/j.jag.2015.02.004, 2016


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