Bessenbacher, V., Seneviratne, S. I., and Gudmundsson, L.: CLIMFILL v0.9: a framework for intelligently gap filling Earth observations, Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, 2022b.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Chen, B., Xu, G., Coops, N. C., Ciais, P., Innes, J. L., Wang, G., Myneni,
R. B., Wang, T., Krzyzanowski, J., Li, Q., Cao, L., and Liu, Y.: Changes in
vegetation photosynthetic activity trends across the Asia–Pacific region
over the last three decades, Remote Sens. Environ., 144, 28–41,
https://doi.org/10.1016/j.rse.2013.12.018, 2014.
Chen, Y., Yang, K., Qin, J., Zhao, L., Tang, W., and Han, M.: Evaluation of
AMSR-E retrievals and GLDAS simulations against observations of a soil
moisture network on the central Tibetan Plateau,
J. Geophys. Res.-Atmos., 118, 4466–4475, https://doi.org/10.1002/jgrd.50301,
2013.
Cristea, N. C., Breckheimer, I., Raleigh, M. S., HilleRisLambers, J., and
Lundquist, J. D.: An evaluation of terrain-based downscaling of fractional
snow covered area data sets based on LiDAR-derived snow data and
orthoimagery, Water Resour. Res., 53, 6802–6820,
https://doi.org/10.1002/2017WR020799, 2017.
Cui, Y., Yang, X., Chen, X., Fan, W., Zeng, C., Xiong, W., and Hong, Y.: A
two-step fusion framework for quality improvement of a remotely sensed soil
moisture product: A case study for the ECV product over the Tibetan Plateau,
J. Hydrol., 587, 124993,
https://doi.org/10.1016/j.jhydrol.2020.124993, 2020.
Cui, Y., Zeng, C., Zhou, J., Xie, H., Wan, W., Hu, L., Xiong, W., Chen, X.,
Fan, W., and Hong, Y.: A spatio-temporal continuous soil moisture dataset
over the Tibet Plateau from 2002 to 2015, Sci. Data, 6, 247,
https://doi.org/10.1038/s41597-019-0228-x, 2019.
Dente, L., Vekerdy, Z., Wen, J., and Su, Z.: Maqu network for validation of
satellite-derived soil moisture products,
Int. J. Appl. Earth Obs., 17, 55–65,
https://doi.org/10.1016/j.jag.2011.11.004, 2012.
Detto, M., Montaldo, N., Albertson, J. D., Mancini, M., and Katul, G.: Soil
moisture and vegetation controls on evapotranspiration in a heterogeneous
Mediterranean ecosystem on Sardinia, Italy, Water Resour. Res., 42, W08419, https://doi.org/10.1029/2005WR004693, 2006.
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.,
Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C.,
Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and
Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203,
185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
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., and Kidd, R.:
Evaluation of the ESA CCI soil moisture product using ground-based
observations, Remote Sens. Environ., 162, 380–395,
https://doi.org/10.1016/j.rse.2014.07.023, 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.
Duan, Z. and Bastiaanssen, W. G. M.: First results from Version 7 TRMM 3B43
precipitation product in combination with a new downscaling–calibration
procedure, Remote Sens. Environ., 131, 1–13,
https://doi.org/10.1016/j.rse.2012.12.002, 2013.
ElSaadani, M., Habib, E., Abdelhameed, A. M., and Bayoumi, M.: Assessment of
a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and
Filling the Gaps in Between Soil Moisture Observations,
Fr. Art. Int., 4, 636234, https://doi.org/10.3389/frai.2021.636234, 2021.
Ford, T. W. and Quiring, S. M.: Comparison and application of multiple
methods for temporal interpolation of daily soil moisture, International
J. Climatol., 34, 2604–2621, https://doi.org/10.1002/joc.3862,
2014.
Fu, G., Crosbie, R. S., Barron, O., Charles, S. P., Dawes, W., Shi, X., Van
Niel, T., and Li, C.: Attributing variations of temporal and spatial
groundwater recharge: A statistical analysis of climatic and non-climatic
factors, J. Hydrol., 568, 816–834,
https://doi.org/10.1016/j.jhydrol.2018.11.022, 2019.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Guisan, A., Weiss, S. B., and Weiss, A. D.: GLM versus CCA spatial modeling
of plant species distribution, Plant Ecol., 143, 107–122, https://doi.org/10.1023/A:1009841519580, 1999.
Gunnarsson, A., Gardarsson, S. M., Pálsson, F., Jóhannesson, T., and Sveinsson, Ó. G. B.: Annual and inter-annual variability and trends of albedo of Icelandic glaciers, The Cryosphere, 15, 547–570, https://doi.org/10.5194/tc-15-547-2021, 2021.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Hu, L., Monaghan, A., Voogt, J. A., and Barlage, M.: A first satellite-based
observational assessment of urban thermal anisotropy,
Remote Sens. Environ., 181, 111–121, https://doi.org/10.1016/j.rse.2016.03.043, 2016.
Jing, W., Zhang, P., and Zhao, X.: Reconstructing Monthly ECV Global Soil
Moisture with an Improved Spatial Resolution, Water Resour. Manage.,
32, 2523–2537, https://doi.org/10.1007/s11269-018-1944-2, 2018.
Karbalaye Ghorbanpour, A., Hessels, T., Moghim, S., and Afshar, A.:
Comparison and assessment of spatial downscaling methods for enhancing the
accuracy of satellite-based precipitation over Lake Urmia Basin, J. Hydrol., 596, 126055, https://doi.org/10.1016/j.jhydrol.2021.126055, 2021.
Kerr, Y. H., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., and
Berger, M.: Soil moisture retrieval from space: the Soil Moisture and Ocean
Salinity (SMOS) mission, IEEE T. Geosci. Remote,
39, 1729–1735, https://doi.org/10.1109/36.942551, 2001.
Leng, P., Li, Z.-L., Duan, S.-B., Gao, M.-F., and Huo, H.-Y.: A practical
approach for deriving all-weather soil moisture content using combined
satellite and meteorological data, ISPRS J. Photogramm., 131, 40–51, https://doi.org/10.1016/j.isprsjprs.2017.07.013,
2017.
Li, B., Liang, S., Liu, X., Ma, H., Chen, Y., Liang, T., and He, T.:
Estimation of all-sky 1 km land surface temperature over the conterminous
United States, Remote Sens. Environ., 266, 112707,
https://doi.org/10.1016/j.rse.2021.112707, 2021a.
Li, L., Dai, Y., Shangguan, W., Wei, N., Wei, Z., and Gupta, S.: Multistep
Forecasting of Soil Moisture Using Spatiotemporal Deep Encoder–Decoder
Networks, J. Hydrometeorol., 23, 337–350, https://doi.org/10.1175/jhm-d-21-0131.1,
2022a.
Li, L., Dai, Y., Shangguan, W., Wei, Z., Wei, N., and Li, Q.:
Causality-Structured Deep Learning for Soil Moisture Predictions, J. Hydrometeorol., 23, 1315–1331, https://doi.org/10.1175/jhm-d-21-0206.1, 2022b.
Li, Q., Li, Z., Shangguan, W., Wang, X., Li, L., and Yu, F.: Improving soil
moisture prediction using a novel encoder-decoder model with residual
learning, Comput. Electron. Agr., 195, 106816,
https://doi.org/10.1016/j.compag.2022.106816, 2022c.
Li, Q., Wang, Z., Shangguan, W., Li, L., Yao, Y., and Yu, F.: Improved daily
SMAP satellite soil moisture prediction over China using deep learning model
with transfer learning, J. Hydrol., 600, 126698,
https://doi.org/10.1016/j.jhydrol.2021.126698, 2021b.
Li, X., Liu, K., and Tian, J.: Variability, predictability, and uncertainty
in global aerosols inferred from gap-filled satellite observations and an
econometric modeling approach, Remote Sens. Environ., 261, 112501,
https://doi.org/10.1016/j.rse.2021.112501, 2021c.
Li, X., Zhang, C., Li, W., and Liu, K.: Evaluating the Use of DMSP/OLS
Nighttime Light Imagery in Predicting PM
2.5 Concentrations in the
Northeastern United States, Remote Sens., 9, 620, https://doi.org/10.3390/rs9060620, 2017.
Li, Y., Piao, S., Li, L. Z. X., Chen, A., Wang, X., Ciais, P., Huang, L.,
Lian, X., Peng, S., Zeng, Z., Wang, K., and Zhou, L.: Divergent hydrological
response to large-scale afforestation and vegetation greening in China,
Sci. Adv., 4, eaar4182, https://doi.org/10.1126/sciadv.aar4182, 2018.
Liu, K., Li, X., and Long, X.: Trends in groundwater changes driven by
precipitation and anthropogenic activities on the southeast side of the Hu
Line, Environ. Res. Lett., 16, 094032, https://doi.org/10.1088/1748-9326/ac1ed8,
2021a.
Liu, K., Li, X., and Wang, S.: Characterizing the spatiotemporal response of
runoff to impervious surface dynamics across three highly urbanized cities
in southern China from 2000 to 2017, Int. J. Appl. Earth Obs., 100, 102331,
https://doi.org/10.1016/j.jag.2021.102331, 2021b.
Liu, K., Su, H., Li, X., and Chen, S.: Development of a 250-m Downscaled
Land Surface Temperature Data Set and Its Application to Improving Remotely
Sensed Evapotranspiration Over Large Landscapes in Northern China, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2020.3037168, 2020a.
Liu, K., Wang, S., Li, X., and Wu, T.: Spatially Disaggregating Satellite
Land Surface Temperature With a Nonlinear Model Across Agricultural Areas,
J. Geophys. Res.-Biogeo., 124, 3232–3251,
https://doi.org/10.1029/2019JG005227, 2019.
Liu, Y., Yao, L., Jing, W., Di, L., Yang, J., and Li, Y.: Comparison of two
satellite-based soil moisture reconstruction algorithms: A case study in the
state of Oklahoma, USA, J. Hydrol., 590, 125406,
https://doi.org/10.1016/j.jhydrol.2020.125406, 2020b.
Llamas, R. M., Guevara, M., Rorabaugh, D., Taufer, M., and Vargas, R.:
Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on
Geostatistical Techniques and Multiple Regression, Remote Sens., 12, 665, https://doi.org/10.3390/rs12040665, 2020.
Long, D., Bai, L., Yan, L., Zhang, C., Yang, W., Lei, H., Quan, J., Meng,
X., and Shi, C.: Generation of spatially complete and daily continuous
surface soil moisture of high spatial resolution,
Remote Sens. Environ., 233, 111364, https://doi.org/10.1016/j.rse.2019.111364, 2019.
Long, D., Yan, L., Bai, L., Zhang, C., Li, X., Lei, H., Yang, H., Tian, F.,
Zeng, C., Meng, X., and Shi, C.: Generation of MODIS-like land surface
temperatures under all-weather conditions based on a data fusion approach,
Remote Sens. Environ., 246, 111863,
https://doi.org/10.1016/j.rse.2020.111863, 2020.
Mao, H., Kathuria, D., Duffield, N., and Mohanty, B. P.: Gap Filling of
High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Mach. Learn.-Based Framework, Water Resour. Res., 55, 6986–7009,
https://doi.org/10.1029/2019WR024902, 2019.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., and Guo, Z.: A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, 2021.
Merlin, O., Jacob, F., Wigneron, J., Walker, J., and Chehbouni, G.:
Multidimensional Disaggregation of Land Surface Temperature Using
High-Resolution Red, Near-Infrared, Shortwave-Infrared, and Microwave-L
Bands, IEEE T. Geosci. Remote, 50, 1864–1880, https://doi.org/10.1109/TGRS.2011.2169802, 2012.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Otkin, J. A., Anderson, M. C., Hain, C., Svoboda, M., Johnson, D., Mueller,
R., Tadesse, T., Wardlow, B., and Brown, J.: Assessing the evolution of soil
moisture and vegetation conditions during the 2012 United States flash
drought, Agr. Forest Meteorol., 218–219, 230–242,
https://doi.org/10.1016/j.agrformet.2015.12.065, 2016.
Prihodko, L., Denning, A. S., Hanan, N. P., Baker, I., and Davis, K.:
Sensitivity, uncertainty and time dependence of parameters in a complex land
surface model, Agr. Forest Meteorol., 148, 268–287,
https://doi.org/10.1016/j.agrformet.2007.08.006, 2008.
Ramoelo, A., Cho, M. A., Mathieu, R., Madonsela, S., van de Kerchove, R.,
Kaszta, Z., and Wolff, E.: Monitoring grass nutrients and biomass as
indicators of rangeland quality and quantity using random forest modelling
and WorldView-2 data, Int. J. Appl. Earth Obs., 43, 43–54, https://doi.org/10.1016/j.jag.2014.12.010, 2015.
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Touré, A.: Assessment and Enhancement of MERRA
Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/jcli-d-10-05033.1, 2011.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Schaake, J. C., Duan, Q., Koren, V., Mitchell, K. E., Houser, P. R., Wood,
E. F., Robock, A., Lettenmaier, D. P., Lohmann, D., Cosgrove, B., Sheffield,
J., Luo, L., Higgins, R. W., Pinker, R. T., and Tarpley, J. D.: An
intercomparison of soil moisture fields in the North American Land Data
Assimilation System (NLDAS), J. Geophys. Res.-Atmos.,
109, D01S90, https://doi.org/10.1029/2002JD003309, 2004.
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and Freitas, N. d.:
Taking the Human Out of the Loop: A Review of Bayesian Optimization,
P. IEEE, 104, 148–175, https://doi.org/10.1109/JPROC.2015.2494218, 2016.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.:
Mapping the global depth to bedrock for land surface modeling,
J. Adv. Model Earth Sy., 9, 65–88,
https://doi.org/10.1002/2016MS000686, 2017.
Sismanidis, P., Bechtel, B., Keramitsoglou, I., Göttsche, F., and
Kiranoudis, C. T.: Satellite-derived quantification of the diurnal and
annual dynamics of land surface temperature, Remote Sens. Environ.,
265, 112642, https://doi.org/10.1016/j.rse.2021.112642, 2021.
Song, P., Zhang, Y., and Tian, J.: Improving Surface Soil Moisture Estimates
in Humid Regions by an Enhanced Remote Sensing Technique,
Geophys. Res. Lett., 48, e2020GL091459, https://doi.org/10.1029/2020GL091459,
2021.
Stroud, J. R., Müller, P., and Sansó, B.: Dynamic models for
spatiotemporal data, J. R. Stat. Soc. B, 63, 673–689,
https://doi.org/10.1111/1467-9868.00305, 2001.
Su, Z., de Rosnay, P., Wen, J., Wang, L., and Zeng, Y.: Evaluation of
ECMWF's soil moisture analyses using observations on the Tibetan Plateau,
J. Geophys. Res.-Atmos., 118, 5304–5318,
https://doi.org/10.1002/jgrd.50468, 2013.
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., and
Feuston, B. P.: Random Forest: A Classification and Regression Tool for
Compound Classification and QSAR Modeling,
J. Chem. Inf. Comp. Sci., 43, 1947–1958, https://doi.org/10.1021/ci034160g, 2003.
Uebbing, B., Forootan, E., Braakmann-Folgmann, A., and Kusche, J.: Inverting
surface soil moisture information from satellite altimetry over arid and
semi-arid regions, Remote Sens. Environ., 196, 205–223,
https://doi.org/10.1016/j.rse.2017.05.004, 2017.
van Zyl, J. J.: The Shuttle Radar Topography Mission (SRTM): a breakthrough
in remote sensing of topography, Acta Astronaut., 48, 559–565,
https://doi.org/10.1016/S0094-5765(01)00020-0, 2001.
Wanders, N., Karssenberg, D., de Roo, A., de Jong, S. M., and Bierkens, M. F. P.: The suitability of remotely sensed soil moisture for improving operational flood forecasting, Hydrol. Earth Syst. Sci., 18, 2343–2357, https://doi.org/10.5194/hess-18-2343-2014, 2014.
Wang, A., Lettenmaier, D. P., and Sheffield, J.: Soil Moisture Drought in
China, 1950–2006, J. Climate, 24, 3257–3271, https://doi.org/10.1175/2011jcli3733.1, 2011.
Wang, C., Xie, Q., Gu, X., Yu, T., Meng, Q., Zhou, X., Han, L., and Zhan,
Y.: Soil moisture estimation using Bayesian Maximum Entropy algorithm from
FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei
province, China, Int. J. Remote Sens., 41, 7018–7041, https://doi.org/10.1080/01431161.2020.1752953, 2020.
Wang, K., Wang, P., Liu, J., Sparrow, M., Haginoya, S., and Zhou, X.:
Variation of surface albedo and soil thermal parameters with soil moisture
content at a semi-desert site on the western Tibetan Plateau,
Bound.-Lay. Meteorol., 116, 117–129, https://doi.org/10.1007/s10546-004-7403-z, 2005.
Wei, F., Wang, S., Fu, B., Brandt, M., Pan, N., Wang, C., and Fensholt, R.:
Nonlinear dynamics of fires in Africa over recent decades controlled by
precipitation, Glob. Change Biol., 26, 4495–4505,
https://doi.org/10.1111/gcb.15190, 2020.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44,
https://doi.org/10.1016/j.rse.2019.02.022, 2019.
Yao, X., Fu, B., Lü, Y., Sun, F., Wang, S., and Liu, M.: Comparison of
Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex
Terrain Catchment, PLOS ONE, 8, e54660, https://doi.org/10.1371/journal.pone.0054660, 2013.
Zhang, L., Liu, Y., Ren, L., Teuling, A. J., Zhang, X., Jiang, S., Yang, X.,
Wei, L., Zhong, F., and Zheng, L.: Reconstruction of ESA CCI
satellite-derived soil moisture using an artificial neural network
technology, Sci. Total Environ., 782, 146602,
https://doi.org/10.1016/j.scitotenv.2021.146602, 2021a.
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021b.
Zhang, R., Di, B., Luo, Y., Deng, X., Grieneisen, M. L., Wang, Z., Yao, G.,
and Zhan, Y.: A nonparametric approach to filling gaps in
satellite-retrieved aerosol optical depth for estimating ambient PM
2.5
levels, Environ. Pollut., 243, 998–1007,
https://doi.org/10.1016/j.envpol.2018.09.052, 2018.
Zhang, X., Zhou, J., Liang, S., and Wang, D.: A practical reanalysis data
and thermal infrared remote sensing data merging (RTM) method for
reconstruction of a 1 km all-weather land surface temperature, Remote Sens. Environ., 260, 112437,
https://doi.org/10.1016/j.rse.2021.112437, 2021c.
Zhang, X., Chen, B., Zhao, H., Fan, H., and Zhu, D.: Soil Moisture Retrieval
over a Semiarid Area by Means of PCA Dimensionality Reduction, Canadian
J. Remote Sens., 42, 136–144, https://doi.org/10.1080/07038992.2016.1175928, 2016.
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman,
E., Mallick, B., Zhang, X., and Brown, M.: Detecting change-point, trend,
and seasonality in satellite time series data to track abrupt changes and
nonlinear dynamics: A Bayesian ensemble algorithm, Remote Sens.
Environ., 232, 111181, https://doi.org/10.1016/j.rse.2019.04.034, 2019a.
Zhao, W., Duan, S.-B., Li, A., and Yin, G.: A practical method for reducing
terrain effect on land surface temperature using random forest regression,
Remote Sens. Environ., 221, 635–649,
https://doi.org/10.1016/j.rse.2018.12.008, 2019b.
Zhao, W., Sánchez, N., Lu, H., and Li, A.: A spatial downscaling
approach for the SMAP passive surface soil moisture product using random
forest regression, J. Hydrol., 563, 1009–1024,
https://doi.org/10.1016/j.jhydrol.2018.06.081, 2018.
Zhu, X., Liu, D., and Chen, J.: A new geostatistical approach for filling
gaps in Landsat ETM
+ SLC-off images, Remote Sens. Environ., 124,
49–60, https://doi.org/10.1016/j.rse.2012.04.019, 2012.
