References

1. 1.
   Adab H, Morbidelli R, Saltalippi C, Moradian M and Ghalhari G A F. 2020. Machine learning to estimate surface soil moisture from remote sensing data. Water, 12(11): 3223
2. 2.
   Akitsue alt., 2022. In-situ observations on a moderate resolution scale for validation of the Global Change Observation Mission-Climate ecological products: The uncertainty quantiffcation in ecological reference data. International Journal of Applied Earth Observations and Geoinformation, 107, 102639.
3. 3.
   Balenzano A, Mattia F, Satalino G, Lovergine F P, Palmisano D, Peng J, Marzahn P, Wegmüller U, Cartus O, Dąbrowska-Zielińska K, Musial J P, Davidson M W J, Pauwels V R N, Cosh M H, McNairn H, Johnson J T, Walker J P, Yueh S H, Entekhabi D, Kerr Y H and Jackson T J. 2021. Sentinel-1 soil moisture at 1 km resolution: a validation study. Remote Sensing of Environment, 263:112554
4. 4.
   Badgley G., Field C. B., Berry J. A., 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3.
5. 5.
   Bailey S.W., Werdell P. J., 2006. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102, 12–23. .
6. 6.
   Benediktsson J A. 2009. Image Registration for Remote Sensing. Cambridge, U.K.: Cambridge Univ. Press.
7. 7.
   Bennartz R. 1999. On the use of SSM/I measurements in coastal regions. Journal of atmospheric and oceanic technology, 16(4): 417-431.
8. 8.
   Boschetti L, Stehman S V and Roy D. P. 2016. A stratified random sampling design in space and time for regional to global scale burned area product validation. Remote Sensing of Environment, 186: 465-478
9. 9.
   Bouvet M., Thome K., Berthelot B., Bialek A., & Woolliams E. R. 2019. Radcalnet: a radiometric calibration network for earth observing imagers operating in the visible to shortwave infrared spectral range. Remote Sensing, 11(20).
10. 10.
    Brown L, Meier C, Morris H, Pastor-Guzman J, Bai G, Lerebourg C, Gobron N, Lanconelli C, Clerici N and Dash J. 2020. Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data. Remote Sensing of Environment, 247: 111935
11. 11.
    Brus D and Heuvelink G. 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma, 138: 86–95
12. 12.
    Buman B, Hueni A, Colombo R, Cogliati S, Celesti M, Julitta T, Burkart A, Siegmann B, Rascher U, Drusch M and Damm A. 2022. Towards consistent assessments of in situ radiometric measurements for the validation of fluorescence satellite missions. Remote Sensing of Environment, 274 : 112984
13. 13.
    Castro S., L. Monzón, Wick G., Lewis R., & BeylkinG . (2018). Subpixel variability and quality assessment of satellite sea surface temperature data using a novel high resolution multistage spectral interpolation (hrmsi) technique. Remote Sensing of Environment.
14. 14.
    Cavender-Bares J, Gamon J A and Townsend P A. 2020. Remote Sensing of Plant Biodiversity. Springer Nature,
15. 15.
    Chen L D, Lu Y H, Fu B J and Wei W. 2006. A framework on landscape pattern analysis and scale change by using pattern recognition approach. Acta Ecologica Sinica, 26(3): 663-670
16. 16.
    Coll C, Valor E, Galve J M, Mira M, Bisquert M, García-Santos V, Caselles E and Caselles V. 2012. Long-term accuracy assessment of land surface temperatures derived from the advanced along-track scanning radiometer. Remote Sensing of Environment, 116: 211–225
17. 17.
    Duan S B, Li Z L, Li H, Gottsche F, Zhao W, Leng P, Zhang X and Coll C. 2019. Validation of collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225:16–29
18. 18.
    Du S., Liu X., Chen J., & Liu L. 2023. Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and a NIRv-scaled approach. Remote Sensing of Environment, 290, 113547.
19. 19.
    Duncanson L. I., Armston J., Disney M., Avitabile V., & Margolis H. 2021. Aboveground Woody Biomass Product Validation Good Practices Protocol.
20. 20.
    ESA, 2019. Fiducial Reference Measurements: FRM []. URL.
21. 21.
    Fang H L, Che T, Jin R, Li A N, Li X, Li Z Y, Liu S M, Ma M G, Xiao Q and Zhang Y Y. 2021. On the construction of China's fiducial reference measurement network for land surface remote sensing product validation. Advances in Earth Science，36(12):1215-1223
22. 22.
    Fernandese alt., 2023.Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests. Remote Sensing of Environment, 293, 113600.
23. 23.
    Gamon J A. 2015. Reviews and syntheses: optical sampling of the flux tower footprint. Biogeosciences.
24. 24.
    Ge Y, Wang J H, Heuvelink G B M, Jin R, Li X and Wang J F. 2015. Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China. International Journal of Geographical Information Science, 29(1):92–110
25. 25.
    Guillevic P C, Biard J C, Hulley G C, Privette J L, Hook S J, Olioso A, Gottsche, F M, Radocinski R, Rom´an M O, Yu Y Y and Csiszar I. 2014. Validation of land surface temperature products derived from the visible infrared imaging radiometer suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sensing of Environment, 154:19–37
26. 26.
    Guillevic P., Göttsche F., Nickeson J., Hulley G., Ghent D., Yu Y., Trigo I., Hook S., Sobrino J.A., Remedios J., Román M. & Camacho F. (2018). Land Surface Temperature Product Validation Best Practice Protocol. Version 1.1. In P. Guillevic, F. Göttsche, J. Nickeson & M. Román (Eds.), Good Practices for Satellite-Derived Land Product Validation (p. 58): Land Product Validation Subgroup (WGCV/CEOS), doi:10.5067/doc/ceoswgcv/lpv/lst.001
27. 27.
    Hoffman L H, Weaver W L and Kibler J F. 1987. Calculation and accuracy of ERBE scanner measurement locations. National Aeronautics and Space Administration. 2670.
28. 28.
    Hooker S. B., Maritorena S., 2000. An evaluation of oceanographic radiometers and deployment methodologies. J. Atmos. Ocean. Technol. 17, 811–830.
29. 29.
    Huang G H, Li X, Huang C L, Liu S M, Ma Y F and Chen H. 2016. Representativeness errors of point-scale ground-based solar radiation measurements in the validation of remote sensing products. Remote Sensing of Environment, 181:198–206
30. 30.
    Hueni A, Damm A, Kneubuehler M, Schlapfer D and Schaepman M E. 2017. Field and airborne spectroscopy cross validation -some considerations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10: 1117–1135
31. 31.
    Jiao Z. H., & Mu X. 2022. Global validation of clear-sky models for retrieving land-surface downward longwave radiation from modis data. Remote Sensing of Environment, 271, 112903-.
32. 32.
    Khlopenkov K V, Trishchenko A P and Luo Y. 2010. Achieving subpixel georeferencing accuracy in the Canadian AVHRR processing system. IEEE Transactions on Geoscience and Remote Sensing, 48:2150–2161
33. 33.
    Li H and Reynolds J F. 1995. On definition and quantification of heterogeneity. Oikos, 280-284
34. 34.
    Li X., Liu S., Ding J., Song L., Xu T., & Ma Y. , et al. 2024. A framework for quantifying the uncertainty in upscaling evapotranspiration from homogeneous to heterogeneous underlying surface. IEEE Transactions on Geoscience and Remote Sensing, 62, 4413624.
35. 35.
    Liu S. M., Xu Z. W., Zhu Z. L., Jia Z. Z., Zhu M. J., 2013. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. J. Hydrol. 487, 24–38.
36. 36.
    Liu Y X, Zhou Y K, Lu N, Tang R L, Liu N J, Li Y, Yang J, Jing W L and Zhou C H. 2021. Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe. Journal of Hydrology, 594:125949
37. 37.
    Ma L L, Zhou X, Wang N, Tao Z, Zhao Y G, Zhu X H, Gao C X, Yang J, Gao H L, Yang H, Xiao Q, Li Q Z, Lyu T T, Zhang F L and Zang W Q. 2024. Practice and reflection on the construction of remote sensing products validation network for the land observation satellite. National Remote Sensing Bulletin, 28(4):805-824
38. 38.
    Martin M., Ghent D., Pires A., Gottsche F.-M., Cermak J., Remedios J., 2019. Comprehensive in situ validation of five satellite land surface temperature data sets over multiple stations and years. Remote Sens. 11, 479. .
39. 39.
    Ma J., Zhou J., Liu S., Gttsche F. M., Zhang X., & Wang S., et al. 2021. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment, 265, 112669-.
40. 40.
    Melin F. 2022. Validation of ocean color remote sensing reflectance data: analysis of results at european coastal sites. Remote Sensing of Environment, DOI:
41. 41.
    Merlaud A, Belegante L, Constantin D E, Hoed M D, Meier A C, Allaart M, Ardelean M, Arseni M, Bösch T, Brenot H, Calcan A, Dekemper E, Donner S, Dörner S, Carmelia M, Dragomir B, Georgescu L, Nemuc A, Nicolae D, Pinardi G, Richter A, Rosu A, Ruhtz T, Schönhardt A, Schuettemeyer D, Shaiganfar R, Stebel K, Tack F, Vâjâiac S, Vasilescu J, Vanhamel J, Wagner T and Roozendael M. 2020. Satellite validation strategy assessments based on the aromat campaigns. Atmospheric Measurement Techniques, 13(10): 5513-5535
42. 42.
    Miao C Y, Immerzeel W, Xu B Q, Yang K, Duan Q Y and Li X, 2024. Understanding the Asian water tower requires a redesigned precipitation observation strategy. Proceedings of the National Academy of Sciences, 121. 23: e2403557121
43. 43.
    Miglietta F., Yang X., Berry J. A., 2022. Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sens. Environ. 270, 112856.
44. 44.
    Milton E J, Schaepman M E, Anderson K, Kneubühler M and Fox N. 2007. Progress in field spectroscopy. Remote Sensing of Environment, 113: S92–S109
45. 45.
    Molero B., Leroux D., Richaume P., Kerr Y., Merlin O., & Cosh M., et al. 2018. Multi-timescale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints. Wiley-Blackwell(1).
46. 46.
    Naethe P, Sanctis A D, Burkart A, Campbell P K E, Colombo R, Mauro B D, Damm A, El-Madany T, Fava F, Gamon J A, Huemmrich K F, Migliavacca M, Paul-Limoges E, Rascher U, Rossini M, Schüttemeyer D, Tagliabue G, Zhang Y G and Julitta T. 2024. Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2. Remote Sensing of Environment, 303:114013
47. 47.
    Nightingale J., Mittaz J. P. D., Douglas S., Dee D., & Merchant C. 2019. Ten priority science gaps in assessing climate data record quality. Remote Sensing, 11(8), 986-.
48. 48.
    Niro F., Goryl P., Dransfeld S., Boccia V., Gascon F., Adams J., Themann B., Scifoni S., Doxani G., 2021. European Space Agency (ESA) calibration/validation strategy for optical land-imaging satellites and pathway towards interoperability. Remote Sens. 13 (15), 3003.
49. 49.
    Origo N., Gorroo J., Ryder J., Nightingale J., & Bialek A. 2020. Fiducial reference measurements for validation of sentinel-2 and proba-v surface reflectance products. Remote Sensing of Environment, 241, 111690.
50. 50.
    Pan F, Wu X D, Zeng Q C, Tang R Q, Wang J P, Lin X W, You D Q, Wen J G and Xiao Q. 2024. A coarse pixel scale ground “truth” dataset based on the global in situ site measurements to support validation and bias correction of satellite surface albedo products. Earth System Scientific Data, 16: 161–176
51. 51.
    Pan S F, Pan N Q, Tian H Q, Friedlingstein P, Sitch S, Shi H, Arora V K, Haverd V, Jain A K, Kato E, Lienert S, Lombardozzi D, Nabel J E M S, Ottlé C, Poulter B, Zaehle S and Running S W. 2020. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrology and Earth System Sciences, 24(3): 1485-1509
52. 52.
    Pardoe alt., 2023, Radiometric assessment of OLCI, VIIRS, and MODIS using ffducial reference measurements along the Atlantic Meridional Transect. Remote Sensing of Environment, 299, 113844.
53. 53.
    Peng J., Yu P., Yu Y., Jia A., Wang D., & Wang H. , et al. 2023. An evaluation of the noaa global daily gap-filled viirs surface albedo. Remote Sensing of Environment: An Interdisciplinary Journal, 298.
54. 54.
    Pham T D, Yokoya N, Xia J S, Ha N T, Le N N, Nguyen T T T, Dao T H, Vu T T P, Pham T D and Takeuchi W. 2020. Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the red river delta biosphere reserve. Vietnam. Remote Sensing, 12(8): 1334
55. 55.
    Porcar-Castell A, Mac Arthur A, Rossini M, Eklundh L, Pacheco-Labrador J, Anderson K, Balzarolo M, Martín M P, Jin H, Tomelleri E, Cerasoli S, Sakowska K, Hueni A, Julitta T, Nichol C J and Vescovo L, 2015. EUROSPEC: at the interface between remote-sensing and ecosystem CO2flux measurements in Europe. Biogeosciences12:6103–6124
56. 56.
    Qin Y., Mcvicar T. R., Huang J., West S., & Steven A. D. L. 2022. On the validity of using ground-based observations to validate geostationary-satellite-derived direct and diffuse surface solar irradiance: quantifying the spatial mismatch and temporal averaging issues. Remote Sensing of Environment, DOI:
57. 57.
    Richter A, Weber M, Burrows J P, Lambert J C and Gijsel A V. 2014. Validation strategy for satellite observations of tropospheric reactive gases. Annals of Geophysics, 56
58. 58.
    Román M O, Schaaf C B, Woodcock C E, Strahler A H, Yang X, Braswell R H, Curtis P S, Davis K J, Dragoni D, Goulden M L, Gu L H, Hollinger D, Kolb T, Meyers T, Munger J W, Privette J L, Richardson A D, Wilson T B and Wofsy S C. 2009. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes. Remote Sensing of Environment, 113(11):2476–2498
59. 59.
    Stehman S. V., & Foody G. M. 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199.
60. 60.
    Stehman V and Wickham J D. 2021. Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment. Remote Sensing of Environment, 115(12): 3044-3055
61. 61.
    Schmidte alt., 2024. Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany.
62. 62.
    Tang R Q, Wu X D, Wang J P, Zeng Q C, Li Z and Wen J G. 2022. Evaluation of several recently developed sampling strategies within the coarse pixel scale for validation of coarse-resolution satellite albedo products. International Journal of Digital Earth, 15(1): 2319-2334
63. 63.
    Tang R Q, Wu X D, Wang J P, Li Z, Zeng Q C, Wen J G. , and 2023. A Geometric Location Matching Method for Validation of Satellite Products: A Case Study for Albedo. IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023
64. 64.
    Thorne P. W., Diamond H.J., Goodison B., Harrigan S., Hausfather Z., Ingleby N.B., Jones P.D., Lawrimore J.H., Lister D.H., Merlone A., Oakley T., Palecki M., Peterson T.C., de Podesta M., Tassone C., Venema V., Willett K.M., 2018. Towards a global land surface climate fiducial reference measurements network. Int. J. Climatol. 38, 2760–2774
65. 65.
    Walvoort D, Brus D J and Gruijter J J D. 2010. An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers & geosciences36(10): 1261-1267
66. 66.
    Wang J F, Haining R, Zhang T L, Xu C D, Hu M G, Yin Q, Li L F, Zhou C H, Li G Q, Chen H Y. 2024. Statistical modelling for spatially stratified heterogeneous data. Annals of the American Association of Geographers. DOI:
67. 67.
    Wang Z, Schaaf C B, Strahler A H, Chopping M J, Román M O, Shuai Y, Woodcock C E, Hollinger D Y and Fitzjarrald D R., 2014. Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods. Remote Sensing of Environment, 140: 60–77
68. 68.
    Wang H, Mao K B, Yuan Z J, Shi J C, Cao M M, Qin Z H, Duan S B and Tang B H. 2021. A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning. Remote sensing of environment, 265:112665
69. 69.
    Wang J H, Y Ge, G B Heuvelink and Zhou C H. 2013. Spatial Sampling Design for Estimating Regional GPP with Spatial Heterogeneities. IEEE Geoscience and Remote Sensing Letters, 11 (2): 539–543
70. 70.
    Wang J P, Wu X D, Ma D J, Wen J G and Xiao Q. 2023. Remote sensing retrieval based on machine learning algorithm： Uncertainty analysis. National Remote Sensing Bulletin, 27(3):790-801
71. 71.
    Wang Y L, Wang X H, Wang K, Chevallier F, Zhu D, Lian J H, He Y, Tian H Q, Li J S, Zhu J X, Jeong S and Canadell J G. 2022. The size of the land carbon sink in China. Nature, 603: E7-E9
72. 72.
    Wen, J G, Wu X D, Wang J P, Tang R Q, Ma D J, Zeng Q C, Gong B C and Xiao Q. 2022. Characterizing the effect of spatial heterogeneity and the deployment of sampled plots on the uncertainty of ground “truth” on a coarse grid scale: Case study for near-infrared (NIR) surface reflectance. Journal of Geophysical Research: Atmospheres, 127: e2022JD036779
73. 73.
    Wen J G，Liu Q H，Li Z Y，Li X，Liu S M，Xiao Q，Gao Z H，Ma M G，Che T，Liu L Y，Fang H L，Yan G J，Ge Y，Chen E X，Zhang Y，Ma L L，Wu X D and Chen X. 2023. A review of the development of remote sensing field experiments and product validation in China. National Remote Sensing Bulletin， 27（3）：573-583 DOI: .
74. 74.
    Wu K., Ryu D., Wagner W., & Hu Z. 2023. A global-scale intercomparison of triple collocation analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques. Remote Sensing of Environment.
75. 75.
    Wu X D, Wen J G, Xiao Q, You D Q, Lin X W, Wu S B and Zhong S Y. 2019. Impacts and contributors of representativeness errors of in situ albedo measurements for the validation of remote sensing products. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9740- 9755
76. 76.
    Wu X D, Wen J G, Xiao Q and You D Q. 2020. Upscaling of single site-based measurements for validation of long-term coarse-pixel albedo products. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3411-3425
77. 77.
    Wu X D, Wen J G, Xiao Q, You D Q, Gong B C, Wang J P, Ma D J and Lin X W. 2022. Spatial heterogeneity of albedo at subpixel satellite scales and its effect in validation: airborne remote sensing results from HiWATER. IEEE Transactions on Geoscience and Remote Sensing, 60(4407114): 1-14
78. 78.
    Wu X D, Wen J G, Tang R Q, Wang J P, Zeng Q C, Li Z, You D Q, Lin X W, Gong B C and Xiao Q. 2023. Quantification of the uncertainty in multiscale validation of coarse-resolution satellite albedo products: A study based on airborne CASI data. Remote Sensing of Environment, 287: 113465
79. 79.
    Wu X D, Wen J G, Xiao Q, Bao Y F, You D Q, Wang J P, Ma D J, Lin X W and Gong B C. 2021.Quantiffcation of the uncertainty caused by geometric registration errors in multiscale validation of satellite products. IEEE Geoscience and Remote Sensing Letters19: 1-5
80. 80.
    Wu X D, Wen J G, Xiao Q, You D Q, Wang J P, Ma D J and Lin X W. 2021. A Multiscale Nested Sampling Method for representative albedo observations at various pixel scales. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 8193-8207
81. 81.
    Wu, X D. Kathrin Naegeli and Stefan Wunderle. 2020. Geometric accuracy assessment of coarse resolution satellite data sets: a study based on AVHRR GAC data at the subpixel level. Earth System Science Data, 12: 539–553
82. 82.
    Xin Q C, Li J, Li Z M, Li Y M and Zhou X W. 2020. Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data. International Journal of Applied Earth Observation and Geoinformation, 93:102189
83. 83.
    Xu B, Li J, Liu Q H, Huete A R, Yu Q, Zeng Y L, Yin G F, Zhao J and Yang L. 2016. Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 3267-3282
84. 84.
    Xu L., Chen N., Xiang Z., Hamid M., & Hu C. 2021. In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254, 112248.
85. 85.
    Zeng Y., Badgley G., Dechant B., Ryu Y., Chen M., Berry J.A., 2019. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sens. Environ. 232.
86. 86.
    Zeng Y., Chen M., Hao D., Damm A., Badgley G., Rascher U., Johnson J.E., Dechant B., Siegmann B., Ryu Y., Qiu H., Krieger V., Panigada C., Celesti M.,
87. 87.
    Zhao X W, Liang J Y and Dang C Y. 2019. A stratified sampling based clustering algorithm for large-scale data. Knowledge-Based Systems, 163: 416-428
88. 88.
    Zhou J, Li M S, Liu S M, Jia Z Z and Ma Y F. 2015. Validation and performance evaluations of methods for estimating land surface temperatures from ASTER data in the middle reach of the Heihe River basin, Northwest China. Remote Sensing. 7(6): 7126-7156
89. 89.
    Zibordi G, Kwiatkowska E, Mélin F, Talone M, Cazzaniga L, Dessailly D and Gossn J I. 2022. Assessment of OLCI-A and OLCI-B radiometric data products across European seas. Remote Sensing of Environment272: 112911
90. 90.
    曾琪铖. 2024. TROPOMI SIF产品的真实性检验方法研究. 兰州大学.