672
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data

, &
Article: 2246638 | Received 10 Nov 2022, Accepted 07 Aug 2023, Published online: 23 Aug 2023

References

  • Agustí‐Panareda, A., Balsamo, G., & Beljaars, A. 2010. Impact of improved soil moisture on the ECMWF precipitation forecast in West Africa. Geophysical Research Letters, 37, L20808. doi.https://doi.org/10.1029/2010GL044748. 20
  • Ahmed, D. A., Benhamou, S., Bonsall, M. B., & Petrovskii, S. V. (2021). Three-dimensional Random Walk models of individual animal movement and their application to trap counts modelling. Journal of Theoretical Biology, 524, 110728. https://doi.org/10.1016/j.jtbi.2021.110728
  • Almagbile, A., Zeitoun, M., Hazaymeh, K., Sammour, H. A., & Sababha, N. 2019. Statistical analysis of estimated and observed soil moisture in sub-humid climate in north-western Jordan. Environmental Monitoring and Assessment, 191(96). https://doi.org/10.1007/s10661-019-7230-9.
  • Babaeian, E., Sidike, P., Newcomb, M. S., Maimaitijiang, M., White, S. A., Demieville, J., Ward, R. W., Sadeghi, M., LeBauer, D. S., Jones, S. B., Sagan, V., & Tuller, M. (2019). A new optical remote sensing technique for high-resolution mapping of soil moisture. Frontiers in Big Data, 2, 37. https://doi.org/10.3389/fdata.2019.00037
  • Baik, J., Zohaib, M., Kim, U., Aadil, M., & Choi, M. (2019). Agricultural drought assessment based on multiple soil moisture products. Journal of Arid Environments, 167, 43–568. https://doi.org/10.1016/j.jaridenv.2019.04.007
  • Balcilar, M., van Eyden, R., Inglesi-Lotz, R., & Gupta, R. (2014). Time-varying linkages between tourism receipts and economic growth in South Africa. Applied Economics, 46(36), 4381–4398. https://doi.org/10.1080/00036846.2014.957445
  • Bartholomé, E., & Belward, A. S. (2005). GLC2000: A new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959–1977. https://doi.org/10.1080/01431160412331291297
  • Bearup, D., & Petrovskii, S. (2015). On time scale invariance of Random Walks in confined space. Journal of Theoretical Biology, 367, 230–245. https://doi.org/10.1016/j.jtbi.2014.11.027
  • Bjornlund, V., Bjornlund, H., & Van Rooyen, A. F. 2020. Why agricultural production in sub-Saharan Africa remains low compared to the rest of the world – a historical perspective. International Journal of Water Resources Development, 36(sup1) S20–S53. https://doi.org/10.1080/07900627.2020.1739512
  • Bogena, H. R., Herbst, M., Huisman, J. A., Rosenbaum, U., Weuthen, A., & Vereecken, H. (2010). Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone Journal, 9(4), 1002. https://doi.org/10.2136/vzj2009.0173
  • Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., & Zemp, M. (2014). The Concept of essential climate variables in support of climate Research, applications, and policy. Bulletin of the American Meteorological Society, 95(9), 1431–1443. https://doi.org/10.1175/BAMS-D-13-00047.1
  • Boke-Olén, N., Ardo, J., Eklundh, L., Holst, T., Lehsten, V., & Bond-Lamberty, B. (2018). Remotely sensed soil moisture to estimate savannah NDVI. PLoS ONE, 13(7), e0200328. https://doi.org/10.1371/journal.pone.0200328
  • Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., & Fernández-Prieto, D. (2018). How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 73, 752–766. https://doi.org/10.1016/j.jag.2018.08.023
  • Celik, M. F., Isik, M. S., Yuzugullu, O., Fajraoui, N., & Erten, E. 2022. Soil moisture prediction from remote sensing images coupled with climate, soil texture and topography via Deep Learning. Remote Sensing, 14, 5584. https://doi.org/10.3390/rs14215584. 21
  • Champagne, C., Davidson, A., Cherneski, P., L’Heureux, J., & Hadwen, T. (2015). Monitoring agricultural risk in Canada using L-band passive microwave soil moisture from SMOS. Journal of Hydrometeorology, 16(1), 5–18. https://doi.org/10.1175/JHM-D-14-0039.1
  • Chen, Y., Xia, J., Zhao, X., & Zhuge, Y. (2019). Soil moisture ecological characteristics of typical shrub and grass vegetation on Shell Island in the Yellow River Delta, China. Geoderma, 348, 45–53. https://doi.org/10.1016/j.geoderma.2019.04.011
  • Clark, T. E., & McCracken, M. W. (2009). Improving forecast accuracy by combining recursive and rolling forecasts. International Economic Review, 50(2), 363–395. https://doi.org/10.1111/j.1468-2354.2009.00533.x
  • Cressie, N. A. C. (1990). The origins of kriging. Mathematical Geology, 22(3), 239–252. https://doi.org/10.1007/BF00889887
  • Cui, H., Jiang, L., Paloscia, S., Santi, E., Pettinato, S., Wang, J., Fang, X., & Liao, W. 2022. The potential of ALOS-2 and Sentinel-1 radar data for soil moisture retrieval with high spatial resolution over agroforestry areas, China. IEEE Transactions on Geoscience & Remote Sensing, 60, 4402617.https://doi.org/10.1109/TGRS.2021.3082805
  • Dente, L., Vekerdy, Z., de Jeu, R., & Su, Z. (2013). Seasonality and autocorrelation of satellite-derived soil moisture products. International Journal of Remote Sensing, 34(9–10), 3231–3247. https://doi.org/10.1080/01431161.2012.716923
  • Diodato, N., de Guenni, L., Garcia, M., & Bellocchi, G. 2019. Decadal oscillation in the predictability of Palmer drought severity index in California. Climate, 7, (1), 6. https://doi.org/10.3390/cli7010006
  • Di, C., Wang, T., Istanbulluoglu, E., Jayawardena, A. W., Li, S., & Chen, X. (2019). Deterministic chaotic dynamics in soil moisture across Nebraska. Journal of Hydrology, 578, 124048. https://doi.org/10.1016/j.jhydrol.2019.124048
  • Dorigo, W. A., Gruber, A., de Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R. M., & Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162, 380–395. https://doi.org/10.1016/j.rse.2014.07.023
  • Dorigo, W., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., De Jeu, R. A., & Naeimi, V. 2010. Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences, 14, 2605–2616. (12) https://doi.org/10.5194/hess-14-2605-2010
  • Dorigo, W., van Oevelen, P., Wagner, W., Drusch, M., Mecklenburg, S., Robock, A., & Jackson, T. 2011. A new international network for in situ soil moisture data. Eos Transactions American Geophysical Union, 92, 141–142. (17) https://doi.org/10.1029/2011EO170001
  • Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y. … Smolander, T.… 2017. ESA CCI soil moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185–215. https://doi.org/10.1016/j.rse.2017.07.001
  • Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., van Oevelen, M., Robock, S., Drusch, P., Mecklenburg, A., & 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. (5) https://doi.org/10.5194/hess-15-1675-2011
  • Dudal, R. 1980. Soil-related constraints to agricultural development in the tropics. In Priorities for alleviating soil-related constraints to food production in the tropics. International Rice Research Institute, pp. 23–37.
  • Entekhabi, D., Reichle, R. H., Koster, R. D., & Crow, W. T. (2010). Performance metrics for soil moisture retrievals and application requirements. Journal of Hydrometeorology, 11(3), 832–840. https://doi.org/10.1175/2010JHM1223.1
  • FAO. 1978. Report on agroecological zones. Volume 1: methodology and results for Africa. Food and Agriculture Organization,
  • FAO. 2017. Climate Change Predictions in Sub-Saharan Africa: Impacts and adaptations. Food and Agriculture Organization of the United Nations. Retrieved October 13, 2021 https://www.fao.org/3/i7040e/i7040e.pdf
  • Fathololoumi, S., Vaezi, A. R., Firozjaei, M. K., & Biswas, A. 2021. Quantifying the effect of surface heterogeneity on soil moisture across regions and surface characteristic. Journal of Hydrology, 126132. https://doi.org/10.1016/j.jhydrol.2021.126132. 596
  • Feng, H. 2016. Individual contributions of climate and vegetation change to soil moisture trends across multiple spatial scales. Scientific Reports, 6(1) https://doi.org/10.1038/srep32782
  • Feng, H., & Liu, Y. (2015). Combined effects of precipitation and air temperature on soil moisture in different land covers in a humid basin. Journal of Hydrology, 531, 1129–1140. https://doi.org/10.1016/j.jhydrol.2015.11.016
  • Fér, M., Kodešová, R., Hroníková, S., & Nikodem, A. (2020). The effect of 12-year ecological farming on the soil hydraulic properties and repellency index. Biologia, 75(6), 799–807. https://doi.org/10.2478/s11756-019-00373-1
  • Fick, S. E., & Hijmans, R. J. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315. (12) https://doi.org/10.1002/joc.5086
  • Fildes, R., & Kourentzes, N. (2011). Validation and forecasting accuracy in models of climate change. International Journal of Forecasting, 27(4), 968–995. https://doi.org/10.1016/j.ijforecast.2011.03.008
  • Fischer, G., van Velthuizen, H., & Nachtergaele, F. O. 2000. Global agroecological zones assessment: Methodology and results. International Institute for Applied Systems Analysis (IIASA) and Food and Agriculture Organization of the United Nations (UN–FAO). 338 pp. https://pure.iiasa.ac.at/id/eprint/6182/1/IR-00-064.pdf.
  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. 2015. The climate Hazards Infrared precipitation with Stations—a new environmental record for monitoring extremes. Scientific Data. 2 1 https://doi.org/10.1038/sdata.2015.66
  • Fu, X., Yu, Z., Luo, L., Lu, H., Liu, D., Ju, Q., Yang, T., Xu, F., Gu, H., Yang, C., Chen, J., & Wang, T. 2014. Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and Ensemble Kalman Filter. Stochastic Environmental Research and Risk Assessment, 28, 681–693. 3 https://doi.org/10.1007/s00477-013-0781-3
  • Gabriel, J. L., Quemada, M., Martín-Lammerding, D., & Vanclooster, M. 2017. Assessing the cover crop effect on soil hydraulic properties by inverse modelling in a 10-year field trial. Hydrology and Earth System Sciences – Discussions. https://doi.org/10.5194/hess-2017-643.
  • García-Moreno, J., Gordillo-Rivero, Á. J., Zavala, L. M., Jordán, A., & Pereira, P. (2013). Mulch application in fruit orchards increases the persistence of soil water repellency during a 15-years period. Soil and Tillage Research, 130, 62–68. https://doi.org/10.1016/j.still.2013.02.004
  • Gaur, N., & Mohanty, B. P. (2019). A nomograph to incorporate geophysical heterogeneity in soil moisture downscaling. Water Resources Research, 55(1), 34–54. https://doi.org/10.1029/2018WR023513
  • Gibon, F., Pellarin, T., Román-Cascón, C., Alhassane, A., Traoré, S., Kerr, Y., Lo Seen, D., & Baron, C. (2018). Millet yield estimates in the Sahel using satellite derived soil moisture time series. Agricultural and Forest Meteorology, 262, 100–109. https://doi.org/10.1016/j.agrformet.2018.07.001
  • Giller, K. E., Delaune, T., Silva, J. V., van Wijk, M., Hammond, J., Descheemaeker, K., van de Ven, G., Schut, A. G. T., Taulya, G., Chikowo, R., & Andersson, J. A. 2021. Small farms and development in sub-Saharan Africa: farming for food, for income or for lack of better options? Food Security, 13, 1431–1454. 6 https://doi.org/10.1007/s12571-021-01209-0
  • Global Climate Observing System. 2011. Systematic observation requirements for satellite-based data products for climate: The supplemental details to the satellite-based component of the “Implementation plan for the global observing system for climate in support of the UNFCCC (2010 update, GCOS-154)”. https://www.wmo.int/pages/prog/gcos/documents/SatelliteSupplement2011Update.pdf
  • González-Zamora, Á., Sánchez, N., Pablos, M., & Martínez-Fernández, J. (2019). CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain. Remote Sensing of Environment, 225, 469–482. https://doi.org/10.1016/j.rse.2018.02.010
  • Goovaerts, P. 2019. Kriging interpolation. The Geographic information Science & Technology Body of Knowledge (4th Quarter 2019 edition), John P. Wilson (ed.). https://doi.org/10.22224/gistbok/2019.4.4.
  • Green, K. C., Armstrong, J. S., & Soon, W. (2009). Validity of climate change forecasting for public policy decision making. International Journal of Forecasting, 25(4), 826–832. https://doi.org/10.1016/j.ijforecast.2009.05.011
  • Green, J. K., Seneviratne, S. I., Berg, A. M., Findell, K. L., Hagemann, S., Lawrence, D. M., & Gentine, P. (2019). Large influence of soil moisture on long-term terrestrial carbon uptake. Nature, 565(7740), 476–479. https://doi.org/10.1038/s41586-018-0848-x
  • Gruber, A., Dorigo, W. A., Crow, W., & Wagner, W. (2017). Triple collocation-based merging of satellite soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing 55(12), 6780–6792.
  • Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., & Dorigo, W. (2019). Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth System Science Data, 11(2), 717–739. https://doi.org/10.5194/essd-11-717-2019
  • Hatfield, J. L., Sauer, T. J., & Prueger, J. H. (2001). Managing soils to achieve greater water use efficiency: A review. Agronomy Journal, 93(2), 271–280. https://doi.org/10.2134/agronj2001.932271x
  • Herbst, M., Rosier, P. T. W., McNeil, D. D., Harding, R. H., & Gowing, D. J. 2008. Seasonal variability of interception evaporation from the canopy of a mixed deciduous forest. Agricultural and Forest Meteorology, 148, 1655–1667. (11) https://doi.org/10.1016/j.agrformet.2008.05.011
  • Huang, J., Desai, A. R., Zhu, J., Hartemink, A. E., Stoy, P. C., Loheide, S. P. I., Bogena, H. R., Zhang, Y., Zhang, Z., & Arriaga, F. (2020). Retrieving heterogeneous surface soil moisture at 100 m across the globe via fusion of remote sensing and land surface parameters. Frontiers in Water, 2, 578367. https://doi.org/10.3389/frwa.2020.578367
  • Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307. https://doi.org/10.1093/biomet/76.2.297
  • Hyndman, R. J., & Athanasopoulos, G. 2018. Forecasting: Principles and practice. 2nd edition. OTexts,
  • Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. 2021. Forecast: Forecasting Functions for Time Series and Linear models. R Package Version 8.21. Retrieved February 25, 2021 https://pkg.robjhyndman.com/forecast/
  • Hyndman, R. J., & Khandakar, Y. 2008. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26, 1–22. (3) https://doi.org/10.18637/jss.v027.i03
  • IFPRI. 2015. Agroecological zones for Africa South of the Sahara. HarvestChoice and International Food Policy Research Institute. Harvard Dataverse, V3. https://doi.org/10.7910/DVN/M7XIUB.
  • Jing, W., Song, J., & Zhao, X. 2018. A comparison of ECV and SMOS soil moisture products based on OzNet monitoring Network. Remote Sensing, 10(5), 5. https://doi.org/10.3390/rs10050703
  • Kac, M. (1947). Random Walk and the theory of Brownian motion. The American Mathematical Monthly, 54(7P1), 369–391. https://doi.org/10.1080/00029890.1947.11990189
  • Kidd, R., & Haas, E. (2017). ESA Climate Change Initiative Phase II Soil Moisture. Soil Moisture ECV Product User Guide (PUG). Version 3.3. Earth Observation Data Centre for Water Resources Monitoring (EODC) GmbH and TU. Wien, GeoVille, ETH Zürich: TRANSMISSIVITY.
  • Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., & Sharma, A. (2015). A global comparison of alternate AMSR2 soil moisture products: Why do they differ? Remote Sensing of Environment, 161, 43–62. https://doi.org/10.1016/j.rse.2015.02.002
  • Konings, A. G., Piles, M., Das, N., & Entekhabi, D. (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198, 460–470. https://doi.org/10.1016/j.rse.2017.06.037
  • Korres, W., Reichenau, T. G., Fiener, P., Koyama, C. N., Bogena, H. R., Cornelissen, T., Baatz, R., Herbst, M., Diekkrüger, B., Vereecken, H., & Schneider, K. (2015). Spatio-temporal soil moisture patterns – a meta-analysis using plot to catchment scale data. Journal of Hydrology, 520, 326–341. https://doi.org/10.1016/j.jhydrol.2014.11.042
  • Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., & Puma, M. J. (2009). On the nature of soil moisture in land surface models. Journal of Climate, 22(16), 4322–4335. https://doi.org/10.1175/2009JCLI2832.1
  • Krivoruchko, K., & Gribov, A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics, 32, 100368. https://doi.org/10.1016/j.spasta.2019.100368
  • Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
  • Lawrence, J. E., & Hornberger, G. M. 2007. Soil moisture variability across climate zones. Geophysical Research Letters, 34 (20), L20402. https://doi.org/10.1029/2007GL031382
  • Legates, D. R., Mahmood, R., Levia, D. F., DeLiberty, T. L., Quiring, S. M., Houser, C., & Nelson, F. E. 2010. Soil moisture: A central and unifying theme in physical geography. Progress in Physical Geography, 35, 65–86. https://doi.org/10.1177/0309133310386514. (1)
  • Liang, W.-L., Li, S.-L., & Hung, F.-X. (2017). Analysis of the contributions of topographic, soil, and vegetation features on the spatial distributions of surface soil moisture in a steep natural forested headwater catchment. Hydrological Processes, 31(22), 3796–3809. https://doi.org/10.1002/hyp.11290
  • Li, Y., Campbell, E. P., Haswell, D., Sneeuwjagt, R. J., & Venables, W. N. (2003). Statistical forecasting of soil dryness index in the southwest of Western Australia. Forest Ecology and Management, 183(1–3), 147–157. https://doi.org/10.1016/S0378-1127(03)00103-8
  • Lin, C. C., & Segel, L. A. 1974. Mathematics applied to deterministic problems in the natural sciences. Macmillan,
  • Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F., Evans, J. P., & van Dijk, A. I. J. M. (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
  • Liu, Y. W., Liu, Y. B., & Wang, W. (2019). Inter-comparison of satellite-retrieved and global land data Assimilation System-simulated soil moisture datasets for global drought analysis. Remote Sensing of Environment, 220, 1–18. https://doi.org/10.1016/j.rse.2018.10.026
  • Liu, Y., Wang, W., & Liu, Y. 2018. ESA CCI soil moisture assimilation in SWAT for improved hydrological simulation in Upper Huai River Basin. Advances in Meteorology, 2018. 1–13 https://doi.org/10.1155/2018/7301314
  • Loew, A., & Schlenz, F. (2011). A dynamic approach for evaluating coarse scale satellite soil moisture products. Hydrology and Earth System Sciences, 15(1), 75–90. https://doi.org/10.5194/hess-15-75-2011
  • Ma, C., Li, X., & McCabe, M. F. 2020. Retrieval of high-resolution soil moisture through combination of Sentinel-1 and Sentinel-2 data. Remote Sensing, 12(14): 2303. https://doi.org/10.3390/rs12142303.
  • Mayaux, P., Bartholomé, E., Massart, M., Van Cutsem, C., Cabral, A., Nonguierma, A., & Diallo, O. 2003. A land cover map of Africa. EUR 20665 EN, European Commission,
  • McNally, A., Shukla, S., Arsenault, K. R. C., Wang, S., Peters-Lidard, C. D., & Verdin, J. P. (2016). Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation, 48, 96–109. https://doi.org/10.1016/j.jag.2016.01.001
  • Meyer, R., Zhang, W., Kragh, S. J., Andreasen, M., Jensen, K. H., Fensholt, R., Stisen, S., & Looms, M. C. 2021. Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale. Hydrology and Earth System Sciences Discussion. https://doi.org/10.5194/hess-2021-508.
  • Mills, T. C. 2019. Applied time series analysis: A practical guide to modelling and forecasting. Elsevier Science,
  • Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., & Stouffer, R. J. (2008). Stationarity is dead: Whither water management? Science, 319(5863), 573–574. https://doi.org/10.1126/science.1151915
  • Mohammed, J. (2020). Challenges in implementing biodiversity policy in sub-Saharan Africa region. American Journal of Biological and Environmental Statistics, 6(2), 24–30. https://doi.org/10.11648/j.ajbes.20200602.12
  • Papacharalampous, G. A., Tyralis, H., & Koutsoyiannis, D. (2018). Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophysica, 66(4), 807–831. https://doi.org/10.1007/s11600-018-0120-7
  • Petropoulos, G. P., Ireland, G., & Barrett, B. 2015. Surface soil moisture retrievals from remote sensing: current status, products & future trends. Physics and Chemistry of the Earth, 83–84, 36–56. https://doi.org/10.1016/j.pce.2015.02.009
  • Pierdicca, N., Fascetti, F., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2015). Analysis of ASCAT, SMOS, in-situ and land model soil moisture as a regionalized variable over Europe and North Africa. Remote Sensing of Environment, 170, 280–289. https://doi.org/10.1016/j.rse.2015.09.005
  • Qiu, Y., Fu, B., Wang, J., & Chen, L. (2001). Soil moisture variation in relation to topography and land use in a hillslope catchment of the Loess Plateau, China. Journal of Hydrology, 240(3–4), 243–263. https://doi.org/10.1016/S0022-1694(00)00362-0
  • Qiu, J., Gao, Q., Wang, S., & Su, Z. (2016). Comparison of temporal trends from multiple soil moisture data sets and precipitation: the implication of irrigation on regional soil moisture trend. International Journal of Applied Earth Observation and Geoinformation, 48, 17–27. https://doi.org/10.1016/j.jag.2015.11.012
  • Rao, P., Wang, Y., Wang, F., Liu, Y., Wang, X., & Wang, Z. 2022. Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China. Earth System Science Data, 14, 3053–3073. https://doi.org/10.5194/essd-14-3053-2022. 7
  • R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org/.
  • Robinson, D. A., Campbell, C. S., Hopmans, J. W., Hornbuckle, B. K., Jones, S. B., Knight, R., Ogden, F., Selker, J., & Wendroth, O. (2008). Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone Journal, 7(1), 358–389. https://doi.org/10.2136/vzj2007.0143
  • Robock, A., Vinnikov, K. Y., Srinivasan, G., Entin, J. K., Hollinger, S. E., Speranskaya, N. A., Liu, S., & Namkhai, A. (2000). The global soil moisture data bank. Bulletin of the American Meteorological Society, 81(6), 1281–1299. https://doi.org/10.1175/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2
  • Sabaghy, S., Walker, J. P., Renzullo, L. J., & Jackson, T. J. (2018). Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities. Remote Sensing of Environment, 209, 551–580. https://doi.org/10.1016/j.rse.2018.02.065
  • Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52–68. https://doi.org/10.1016/j.rse.2017.05.041
  • Scheren, P., Tyrrell, P., Brehony, P., Allan, J. R., Thorn, J. P. R., Chinho, T., Katerere, Y., Ushie, V., & Worden, J. S. 2021. Defining pathways towards African ecological futures. Sustainability, 13, 8894. https://doi.org/10.3390/su13168894. 16
  • Sehler, R., Li, J., Reager, J. T., & Ye, H. (2019). Investigating relationship between soil moisture and precipitation globally using remote sensing observations. Journal of Contemporary Water Research & Education, 168(1), 106–118. https://doi.org/10.1111/j.1936-704X.2019.03324.x
  • Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3–4), 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004
  • Shumway, R. H., & Stoffer, D. S. 2016. Time series analysis and its applications: With R examples. 4th edition. Springer, https://doi.org/10.1007/978-3-319-52452-8
  • Snapp, S. 2017. Agroecology: Principles and practice (chapter 2). In Agricultural systems: Agroecology and rural innovation for development, 2nd edition (Eds. S. Snapp & B.). Pound Elsevier Inc. https://doi.org/10.1016/B978-0-12-802070-8.00002-5. pp. 33–72.
  • Song, P., Huang, J., & Mansaray, L. R. (2019). An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression. Agricultural and Forest Meteorology, 275, 146–158. https://doi.org/10.1016/j.agrformet.2019.05.022
  • Su, C.-H., Ryu, D., Dorigo, W., Zwieback, S., Gruber, A., Albergel, C., Reichle, R. H., & Wagner, W. 2016. Homogeneity of a global multisatellite soil moisture climate data record. Geophysical Research Letter, 43, 11245–11252. (21) https://doi.org/10.1002/2016GL070458
  • Susha Lekshmi, S. U., Singh, D. N., & Baghini, M. S. 2014. A critical review of soil moisture measurement. Measurement, 54, 92–105. https://doi.org/10.1016/j.measurement.2014.04.007
  • UNEP-WCMC. 2016. The state of biodiversity in Africa: A mid-term review of progress towards the Aichi biodiversity Targets,
  • Vereecken, H., Huisman, J. A., Bogena, H., Vanderborght, J., Vrugt, J. A., & Hopmans, J. W. (2008). On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resources Research, 44(4), 253–270. https://doi.org/10.1029/2008WR006829
  • Vergopolan, N., Chaney, N. W., Pan, M., Pan, M., Sheffield, J., Beck, H. E., Ferguson, C. R., Torres-Rojas, L., Sadri, S., & Wood, E. F. 2021. SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. Scientific Data, 8, 264. https://doi.org/10.1038/s41597-021-01050-2. (1)
  • von Randow, R. C. S., von Randow, C., Hutjes, R. W. A., Tomasella, J., & Kruijt, B. (2012). Evapotranspiration of deforested areas in central and southwestern Amazonia. Theoretical and Applied Climatology, 109(1–2), 205–220. https://doi.org/10.1007/s00704-011-0570-1
  • Wang, Y., Yang, J., Chen, Y., Wang, A., & De Maeyer, P. 2018. The spatiotemporal response of soil moisture to precipitation and temperature changes in an arid region, China. Remote Sensing, 10(3), 468. https://doi.org/10.3390/rs10030468
  • Weiss, G. H. 1983. Random Walks and their applications: Widely used as mathematical models, Random Walks play an important role in several areas of physics, chemistry, and biology. American Scientist, 71(1), 65–71. https://www.jstor.org/stable/27851819
  • Yang, F., & Liang, D. 2020. Random Walk simulation of non-conservative pollutant transport in shallow water flows. Environmental Modelling and Software, 134, 104870. https://doi.org/10.1016/j.envsoft.2020.104870
  • Yan, S., & Wu, G. (2010). Application of Random Walk model to fit temperature in 46 gamma world cities from 1901 to 1998. Natural Science, 2(12), 1425–1431. https://doi.org/10.4236/ns.2010.212174
  • Yu, B., Liu, G., Liu, Q., Wang, X., Feng, J., & Huang, C. 2018. Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China. Catena, 165, 125–132. https://doi.org/10.1016/j.catena.2018.01.020
  • Zeng, J., Li, Z., Chen, Q., Bi, H., Qiu, J., & Zou, P. (2015). Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sensing of Environment, 163, 91–110. https://doi.org/10.1016/j.rse.2015.03.008
  • Zeyliger, A. M., Muzalevskiy, K. V., Zinchenko, E. V., & Ermolaeva, O. S. (2022). Field test of the surface soil moisture mapping using Sentinel-1 radar data. Science of the Total Environment, 807, 151121. https://doi.org/10.1016/j.scitotenv.2021.151121
  • Zhang, K., Wang, Q., Chao, L., Ye, J., Li, Z., Yu, Z., Yang, T., & Ju, Q. (2019). Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. Journal of Hydrology, 574, 903–914. https://doi.org/10.1016/j.jhydrol.2019.04.087
  • Zhao, W., Sánchez, N., Lu, H., & Li, A. (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology, 563, 1009–1024. https://doi.org/10.1016/j.jhydrol.2018.06.081
  • Zhao, W., Wen, F., Wang, Q., Sanchez, N., & Piles, M. 2021. Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products. Journal of Hydrology, 126930. https://doi.org/10.1016/j.jhydrol.2021.126930. 603
  • Zohaib, M., Kim, H., & Choi, M. (2017). Evaluating the patterns of spatiotemporal trends of root zone soil moisture in major climate regions in East Asia. Journal of Geophysical Research: Atmospheres, 122(15), 7705–7722. https://doi.org/10.1002/2016JD026379
  • Zwieback, S., Colliander, A., Cosh, M. H., Martínez-Fernández, J., McNairn, H., Starks, P. J., Thibeault, M., & Berg, A. (2018). Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences, 22(8), 4473–4489. https://doi.org/10.5194/hess-22-4473-2018