References

1. 1.
   Asiama K O, Bennett R M and Zevenbergen J A. 2017. Land consolidation on Ghana’s rural customary lands: drawing from the Dutch, Lithuanian and Rwandan experiences. Journal of Rural Studies, 56: 87-99
2. 2.
   Bargiel D. 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sensing of Environment, 198: 369-383
3. 3.
   Blickensdörfer L, Schwieder M, Pflugmacher D, Nendel C, Erasmi S and Hostert P. 2022. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269: 112831
4. 4.
   Boryan C, Yang Z W, Mueller R and Craig M. 2011. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto International, 26(5): 341-358
5. 5.
   Chang Q. 2023-01-16(002). Turn more grain fields into fertile land. People’s Daily
6. 6.
   Chen S C, Arrouays D, Leatitia Mulder V, Poggio L, Minasny B, Roudier P, Libohova Z, Lagacherie P, Shi Z, Hannam J, Meersmans J, Richer-de-Forges A C and Walter C. 2022. Digital mapping of GlobalSoilMap soil properties at a broad scale: a review. Geoderma, 409: 115567
7. 7.
   Chen X R, Zhan Y L, Liu Y, Gu X F, Yu T, Wang D K, Liu Q X, Zhang Y and Zhang Y Z. 2020. Improving the classification accuracy of annual crops using time series of temperature and vegetation indices. Remote Sensing, 12(19): 3202
8. 8.
   Chen X Z, Chen S S, Li D, Su Y X and Zhong R F. 2010. Progress in land surface temperature retrieval from passive microwave remote sensing data. Advances in Earth Science, 25(8): 827-835
9. 9.
   Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing (in Chinese), 20(5): 748-767
10. 10.
    Chen Z X, Shuai Q, Shi Z, Arrouays D, Richer-de-Forges A C and Chen S C. 2023. National-scale mapping of soil organic carbon stock in France: new insights and lessons learned by direct and indirect approaches. Soil and Environmental Health, 1(4): 100049
11. 11.
    Cui W J, Chang Z Y and Li N. 2013. Influence of drought stress on ecological, photosynthetic, yield and reflectivity characteristics in winter wheat. Journal of Shanxi Agricultural Sciences, 41(12): 1314-1318, 1346
12. 12.
    D’Andrimont R, Verhegghen A, Lemoine G, Kempeneers P, Meroni M and van der Velde M. 2021. From parcel to continental scale - A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. Remote Sensing of Environment, 266: 112708
13. 13.
    dela Torre D M G, Gao J and Macinnis-Ng C. 2021. Remote sensing-based estimation of rice yields using various models: A critical review. Geo-Spatial Information Science, 24(4): 580-603
14. 14.
    Dong J W, Wu W B, Huang J X, You N S, He Y L and Yan H M. 2020. State of the art and perspective of agricultural land use remote sensing information extraction. Journal of Geo-information Science, 22(4): 772-783
15. 15.
    Du C, Ren H Z, Qin Q M, Meng J J and Zhao S H. 2015. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 7(1): 647-665
16. 16.
    Duan Y L, Huang J X, Zhang S, Liu Y X, Xiang M H, Yu Q Y, Wu W B. 2024. Remote sensing-based monitoring and evaluation of farmland flooding in North China under the influence of Typhoon Doksuri. Agricultural Comprehensive Development in China, (1): 18-21
17. 17.
    Ermida S L, Jiménez C, Prigent C, Trigo I F and DaCamara C C. 2017. Inversion of AMSR-E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature. Journal of Geophysical Research: Atmospheres, 122(6): 3348-3360
18. 18.
    Fan W J, Xu X R, Wang F Q and Liu Q. 2004. Retrieval of plant and soil temperature by AMTIS data. Journal of Remote Sensing (in Chinese), 8(4): 295-299
19. 19.
    Fang L N, Li J M, Chen Z X, Zhang Y and Yin C B. 2020. Practice of farmland construction in Japan and South Korea and their enlightenment to China’s high standard farmland construction. Chinese Journal of Agricultural Resources and Regional Planning, 41(6): 1-6
20. 20.
    Fang W Z, Sun Y Y, Ji R, Wan W and Ma L. 2021. Recognizing global dams from high-resolution remotely sensed images using convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 6363-6371
21. 21.
    Department of Farmland Construction and Management, Ministry of Agriculture and Rural Affairs of the People’s Republic of China. 2022. Compilation of technical standards and regulatory frameworks for farmland construction. Beijing: China Agricultural Press (农业农村部农田建设管理司. 2022. 农田建设技术标准制度汇编. 北京: 中国农业出版社)
22. 22.
    Fei J B, Ling J, Wu X, Li H C, Hu J, Hu Y F and Yang R D. 2016. Analysis on construction of well-facilitated farmland based on land reclamation monitoring and supervision system. Transactions of the Chinese Society of Agricultural Engineering, 32(3): 267-274
23. 23.
    Fritz S, See L, Bayas J C L, Waldner F, Jacques D, Becker-Reshef I, Whitcraft A, Baruth B, Bonifacio R, Crutchfield J, Rembold F, Rojas O, Schucknecht A, van der Velde M, Verdin J, Wu B F, Yan N N, You L Z, Gilliams S, Mücher S, Tetrault R, Moorthy I and McCallum I. 2019. A comparison of global agricultural monitoring systems and current gaps. Agricultural Systems, 168: 258-272
24. 24.
    Gallo I, Ranghetti L, Landro N, La Grassa R and Boschetti M. 2023. In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series. ISPRS Journal of Photogrammetry and Remote Sensing, 195: 335-352
25. 25.
    Ghulam A, Qin Q M, Teyip T and Li Z L. 2007. Modified perpendicular drought index (MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2): 150-164
26. 26.
    Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113-1126
27. 27.
    Gomez C, Adeline K, Bacha S, Driessen B, Gorretta N, Lagacherie P, Roger J M and Briottet X. 2018. Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment, 204: 18-30
28. 28.
    Gruber A, De Lannoy G, Albergel C, Al-Yaari A, Brocca L, Calvet J C, Colliander A, Cosh M, Crow W, Dorigo W, Draper C, Hirschi M, Kerr Y, Konings A, Lahoz W, Mccoll K, Montzka C, Muñoz-Sabater J, Peng J, Reichle R, Richaume P, Rüdiger C, Scanlon T, van der Schalie R, Wigneron J P and Wagner W. 2020. Validation practices for satellite soil moisture retrievals: what are (the) errors? Remote Sensing of Environment, 244: 111806
29. 29.
    Han W T, Zhang L Y, Zhang H X, Shi Z Q, Yuan M C and Wang Z J. 2017. Extraction method of sublateral canal distribution information based on UAV remote sensing. Transactions of the Chinese Society for Agricultural Machinery, 48(3): 205-214
30. 30.
    Han Y, Chen Y S and Chen Z M. 2022. The process of China’s high-standard farmland construction and policy suggestions for improvement. Rural Economy, (5): 20-29
31. 31.
    Hao P Y, Zhan Y L, Wang L, Niu Z and Shakir M. 2015. Feature selection of time series MODIS data for early crop classification using random forest: a case study in Kansas, USA. Remote Sensing, 7(5): 5347-5369
32. 32.
    Hao X L, Li H B, Duan Y L, Shang G B and Yu Q Y. 2023. Research on the application of SAM-based field block extraction method in the evaluation of field leveling effectiveness. China Agricultural Informatics, 35(5): 1-10
33. 33.
    Hu C J. 2016. A study on the farming system of Medieval Western Europe. Journal of Tangshan Normal University, 38(1): 114-116
34. 34.
    Hu Q, Wu W B, Song Q, Yu Q Y, Yang P and Tang H J. 2015. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 48(10): 1900-1914
35. 35.
    Hu Y M, Yang H, Zou R Y, Shi Z, Wu W B, Wu L, Wang L, Ren X N, Xie Y K, Ren S B, Zhu Z Q, Xiong C S and Xie X. 2021. Evolution and prospect of systematic cognition on the cultivated land resources. Journal of Agricultural Resources and Environment, 38(6): 937-945
36. 36.
    Huang H R, Zhuo L, Wang R R, Shang K H, Li M, Yang X and Wu P T. 2021. Agricultural infrastructure: the forgotten key driving force of crop-related water footprints and virtual water flows in China. Journal of Cleaner Production, 309: 127455
37. 37.
    Huang J X, Gómez-Dans J L, Huang H Y, Ma H L, Wu Q, Lewis P E, Liang S L, Chen Z X, Xue J H, Wu Y T, Zhao F, Wang J and Xie X H. 2019. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276-277: 107609
38. 38.
    Huang J X, Huang H, Ma H Y, Zhuo W, Huang R, Gao X R, Liu J M, Su W, Li L, Zhang X D and Zhu D H. 2018. Review on data assimilation of remote sensing and crop growth models. Transactions of the Chinese Society of Agricultural Engineering, 34(21): 144-156
39. 39.
    Huang M H. 2015. An exploration of agricultural irrigation in ancient Egypt. Journal of History and Chorography, (3): 23-26
40. 40.
    Janečková Molnárová K, Sklenička P, Bohnet I C, Lowther-Harris F, van den Brink A, Movahhed Moghaddam S, Fanta V, Zástěra V and Azadi H. 2023. Impacts of land consolidation on land degradation: a systematic review. Journal of Environmental Management, 329: 117026
41. 41.
    Jiang H, Hu H, Zhong R H, Xu J F, Xu J L, Huang J F, Wang S W, Ying Y B and Lin T. 2020. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: a case study of the US Corn Belt at the county level. Global Change Biology, 26(3): 1754-1766
42. 42.
    Jiang Y F, Tang Y T, Long H L and Deng W. 2022. Land consolidation: a comparative research between Europe and China. Land Use Policy, 112: 105790.
43. 43.
    Jiao Y M, Cheng G D and Xiao D N. 2002. A study on the cultural landscape of Hani’s terrace and its protection. Geographical Research, 21(6): 733-741
44. 44.
    Joshi A, Pradhan B, Gite S and Chakraborty S. 2023. Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: a systematic review. Remote Sensing, 15(8): 2014
45. 45.
    Kornelsen K C and Coulibaly P. 2013. Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. Journal of Hydrology, 476: 460-489
46. 46.
    Lawes R, Hochman Z, Jakku E, Butler R, Chai J, Chen Y, Waldner F, Mata G, Donohue R. 2022. Graincast^TM: monitoring crop production across the Australian grainbelt. Crop and Pasture Science, 74(6): 509-523
47. 47.
    Lei S J, Sun H, Liu H, Jiang F G, Chen S, Wu T and Xie Y. 2020. Study on extraction method of farmland shelterbelt supported by Landsat 8 time series image. Journal of Central South University of Forestry and Technology, 40(4): 57-63
48. 48.
    Li Y F. 1993. The Significance of Dujiangyan irrigation project in the history of science and technology. Journal of Sichuan University (Philosophy and Social Science Edition), (2): 88-96
49. 49.
    Li Z, Liu S L, Sun R H and Liu W Z. 2018. Identifying the temporal-spatial pattern evolution of the multiple cropping index in the Huang-Huai-Hai region. 2018. Acta Ecologica Sinica, 38(12): 4454-4460
50. 50.
    Li Z, Wen Y R and Yu Q Y. 2023a. Evaluation of China’s cropland construction benefits. Big Earth Data in Support of the Sustainable Development Goals. Beijing: International Research Center of Big Data for Sustainable Development Goals
51. 51.
    Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing (in Chinese), 20(5): 899-920
52. 52.
    Li Z L, Leng P, Zhou C, Chen K S, Zhou F C and Shang G F. 2021. Soil moisture retrieval from remote sensing measurements: current knowledge and directions for the future. Earth-Science Reviews, 218: 103673
53. 53.
    Li Z L, Wu H, Duan S B., Zhao W, Ren H Z, Liu X Y, Leng P, Tang R L, Ye X, Zhu J S, Sun Y W, Si M L, Liu M, Li J H, Zhang X, Shang G F, Tang B H, Yan G J and Zhou C H. 2023b. Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications. Reviews of Geophysics, 61(1): e2022RG000777
54. 54.
    Liu C, Zhang Q, Tao S Q, Qi J G, Ding M J, Guan Q H, Wu B F, Zhang M, Nabil M, Tian F Y, Zeng H W, Zhang N, Bavuudorj G, Rukundo E, Liu W J, Bofana J, Beyene A N and Elnashar A. 2020a. A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication. Remote Sensing of Environment, 251: 112095
55. 55.
    Liu F, Geng X Y, Zhu A X, Fraser W and Waddell A. 2012a. Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS. Geoderma, 171-172: 44-52
56. 56.
    Liu F, Wu H Y, Zhao Y G, Li D C, Yang J L, Song X D, Shi Z, Zhu A X and Zhang G L. 2022a. Mapping high resolution National Soil Information Grids of China. Science Bulletin, 67(3): 328-340
57. 57.
    Liu L, Xiao X M, Qin Y W, Wang J, Xu X L, Hu Y M and Qiao Z. 2020b. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 239: 111624
58. 58.
    Liu Q, Yan C Y, Xiao Q, Yan G J and Fang L. 2012b. Separating vegetation and soil temperature using airborne multiangular remote sensing image data. International Journal of Applied Earth Observation and Geoinformation, 17: 66-75
59. 59.
    Liu Y, Chen S C, Yu Q Y, Cai Z J, Zhou Q B, Bellingrath-Kimura S D and Wu W B. 2023. Improving digital mapping of soil organic matter in cropland by incorporating crop rotation. Geoderma, 438: 116620
60. 60.
    Liu Y, Yu Q Y, Zhou Q B, Wang C, Bellingrath-Kimura S D and Wu W B. 2022b. Mapping the complex crop rotation systems in southern China considering cropping intensity, crop diversity, and their seasonal dynamics. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 9584-9598
61. 61.
    Loiseau T, Chen S, Mulder V L, Román Dobarco M, Richer-de-Forges A C, Lehmann S, Bourennane H, Saby N P A, Martin M P, Vaudour E, Gomez C, Lagacherie P and Arrouays D. 2019. Satellite data integration for soil clay content modelling at a national scale. International Journal of Applied Earth Observation and Geoinformation, 82: 101905
62. 62.
    Long J, Li M M, Wang X Q and Stein A. 2022. Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images. International Journal of Applied Earth Observation and Geoinformation, 112: 102871
63. 63.
    Lu X Y, Zhong Y F, Zheng Z and Zhang L P. 2021. GAMSNet: Globally aware road detection network with multi-scale residual learning. ISPRS Journal of Photogrammetry and Remote Sensing, 175: 340-352
64. 64.
    Lv Y H, Zhang C, Yun W J, Li P S, Sang L L and Chen Y Y. 2018. Automatic recognition of farmland shelterbelts in high spatial resolution remote sensing data. Transactions of the Chinese Society for Agricultural Machinery, 49(1): 157-163
65. 65.
    Ma H Z, Liu Q H, Yang L, Du H J and Shi J. 2011. Algorithm of reconstructing bare soil temperature time series by combining thermal infrared and passive microwave remote sensing technology. Infrared and Laser Engineering, 40(3): 418-423
66. 66.
    Ma Y T, Zhou Z, She X X, Zhou L Y, Ren T, Liu S S and Lu J W. 2022. Identifying dike-pond system using an improved cascade R-CNN model and High-Resolution satellite images. Remote Sensing, 14(3): 717
67. 67.
    Mao K B, Shi J C, Li Z L, Qin Z H, Li M C and Xu B. 2007. A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data. Science in China Series D: Earth Sciences, 50(7): 1115-1120
68. 68.
    Mardan M and Ahmadi S. 2021. Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images. GIScience and Remote Sensing, 58(8): 1276-1299
69. 69.
    McFarland M J, Miller R L and Neale C M U. 1990. Land surface temperature derived from the SSM/I passive microwave brightness temperatures. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 839-845
70. 70.
    Meng X T, Bao Y L, Wang Y A, Zhang X L and Liu H J. 2022. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms. Remote Sensing of Environment, 280: 113166
71. 71.
    Ministry of Agriculture and Rural Affairs of the People’s Republic of China. 2021. National Well-Facilitated Farmland Construction Plan (2021-2030) (Ministry of Agriculture and Rural Affairs of the People’s Republic of China
72. 72.
    Mirzaeitalarposhti R, Shafizadeh-Moghadam H, Taghizadeh-Mehrjardi R and Demyan M S. 2022. Digital soil texture mapping and spatial transferability of machine learning models using sentinel-1, sentinel-2, and terrain-derived covariates. Remote Sensing, 14(23): 5909
73. 73.
    Mishra V, Cruise J F and Mecikalski J R. 2021. Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over southeast United States. European Journal of Agronomy, 123: 126208
74. 74.
    Nguyen H H, Cho S and Choi M. 2022. Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology, 312: 108719
75. 75.
    Peng J, Biswas A, Jiang Q S, Zhao R Y, Hu J, Hu B F and Shi Z. 2019. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337: 1309-1319
76. 76.
    Persello C, Tolpekin V A, Bergado J R and de By R A. 2019. Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote Sensing of Environment, 231: 111253
77. 77.
    Poggio L, De Sousa L M, Batjes N H, Heuvelink G B M, Kempen B, Ribeiro E and Rossiter D. 2021. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil, 7(1): 217-240
78. 78.
    Qin Z, Karnieli A and Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18): 3719-3746.
79. 79.
    Qiu B W, Hu X, Chen C C, Tang Z H, Yang P, Zhu X L, Yan C and Jian Z Y. 2022. Maps of cropping patterns in China during 2015-2021. Scientific Data, 9(1): 479
80. 80.
    Sadeghi M, Babaeian E, Tuller M and 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
81. 81.
    Senthilnath J, Rajeshwari M and Omkar S N. 2009. Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method. Journal of the Indian Society of Remote Sensing, 37(3): 351-361
82. 82.
    Shahriari M, Delbari M, Afrasiab P and Pahlavan-Rad M R. 2019. Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. CATENA, 182: 104149
83. 83.
    Shen G, Yu Q Y, Zhou Q B, Wang C and Wu W B. 2023. From multiple cropping frequency to multiple cropping system: a new perspective for the characterization of cropland use intensity. Agricultural Systems, 204: 103535
84. 84.
    Shi Z, Liang Z Z, Yang Y Y and Guo Y. 2015. Status and prospect of agricultural remote sensing. Transactions of the Chinese Society for Agricultural Machinery, 46(2): 247-260
85. 85.
    Sippel S R. 2024. Infrastructures of farmland valuation in Australia. Ethnos, 89(2): 219-236.
86. 86.
    Sobrino J A, Sòria G and Prata A J. 2004. Surface temperature retrieval from along track scanning radiometer 2 data: Algorithms and validation. Journal of Geophysical Research: Atmospheres, 109(D11): 11101
87. 87.
    Song Q, Hu Q, Lu M, Yu Q Y, Yang P, Shi Y, Duan Y L and Wu W B. 2020. Prospect of crop mapping. Chinese Journal of Agricultural Resources and Regional Planning, 41(6): 57-65
88. 88.
    Song Q, Zhou Q B, Wu W B, Hu Q, Yu Q Y and Tang H J. 2015. Recent progresses in research of integrating multi-source remote sensing data for crop mapping. Scientia Agricultura Sinica, 48(6): 1122-1135
89. 89.
    State Administration for Market Regulation, Standardization Administration of the People’s Republic of China. 2022. GB/T 30600-2022 Well-facilitated farmland construction—General rules. Beijing: Standards Press of China
90. 90.
    Tang H J, Wu W B, Yang Peng, Zhou Q B and Chen Z X. 2010. Recent progresses in monitoring crop spatial patterns by using remote sensing technologies. Scientia Agricultura Sinica, 43(14): 2879-2888
91. 91.
    Tang H J, Wu W B, Yu Q Y, Xia T, Yang P and Li Z G. 2015. Key research priorities for agricultural land system studies. Scientia Agricultura Sinica, 48(5): 900-910
92. 92.
    Tang P Q, Wu W B, Yao Y M and Yang P. 2011. New method for extracting multiple cropping index of North China Plain based on wavelet transform. Transactions of the Chinese Society of Agricultural Engineering, 27(7): 220-225
93. 93.
    Tian F Y, Wu B F, Zeng H W, Zhang M, Hu Y R, Xie Y, Wen C C, Wang Z D, Qin X L, Han W and Yang H H. 2023. A shape-attention Pivot-Net for identifying central pivot irrigation systems from satellite images using a cloud computing platform: an application in the contiguous US. GIScience and Remote Sensing, 60(1): 2165256
94. 94.
    Tsokas A, Rysz M, Pardalos P M and Dipple K. 2022. SAR data applications in earth observation: An overview. Expert Systems with Applications, 205: 117342
95. 95.
    Vaudour E, Gomez C, Fouad Y and Lagacherie P. 2019. Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sensing of Environment, 223: 21-33
96. 96.
    Waldner F and Diakogiannis F I. 2020. Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network. Remote Sensing of Environment, 245: 111741
97. 97.
    Wang N, Chen S C, Huang J Y, Frappart F, Taghizadeh R, Zhang X L, Wigneron J P, Xue J, Xiao Y, Peng J and Shi Z. 2024. Global soil salinity estimation at 10 m using multi-source remote sensing. Journal of Remote Sensing, 4: 0130
98. 98.
    Wang N, Peng J, Xue J, Zhang X L, Huang J Y, Biswas A, He Y and Shi Z. 2022. A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network. Geoderma, 409: 115656
99. 99.
    Wang X W, Zhao Q Z, Tian W Z, Long X and Jiang P. 2021. Influence of U-Net model on the accuracy of shelter forest extraction with different spatial resolutions. Bulletin of Surveying and Mapping, (6): 39-43
100. 100.
     Wang Y L and He L Y. 2005. A review on the research and application of crop simulation model. Journal of Huazhong Agricultural University, 24(5): 529-535
101. 101.
     Wei Y B, Lu M, Yu Q Y, Xie A K, Hu Q and Wu W B. 2021. Understanding the dynamics of integrated rice–crawfish farming in Qianjiang county, China using Landsat time series images. Agricultural Systems, 191: 103167
102. 102.
     Wiseman G, Kort J and Walker D. 2009. Quantification of shelterbelt characteristics using high-resolution imagery. Agriculture, Ecosystems and Environment, 131(1-2): 111-117
103. 103.
     Wu B F, Zhang M, Zeng H W, Tian F Y, Potgieter A B, Qin X L, Yan N N, Chang S, Zhao Y, Dong Q H, Boken V, Plotnikov D, Guo H D, Wu F M, Zhao H, Deronde B, Tits L and Loupian E. 2023. Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4): nwac290
104. 104.
     Wu Q Y, Yu Q Y, Duan Y L and Wu W B. 2023. Data-driven field sample location approach for crop classification using remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 39(6): 214-223
105. 105.
     Wu S R, Yang P, Ren J Q, Chen Z X and Li H. 2021. Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Remote Sensing of Environment, 255: 112276
106. 106.
     Wu W B, Yu Q Y, Lu M, Xiang M T, Xie A K, Yang P and Tang H J. 2018. Key research priorities for multiple cropping systems. Scientia Agricultura Sinica, 51(9): 1681-1694
107. 107.
     Wu W B, Yu Q Y, You L Z, Chen K, Tang H J and Liu J G. 2018. Global cropping intensity gaps: Increasing food production without cropland expansion. Land Use Policy, 76: 515-525
108. 108.
     Xiang M T, Yu Q Y and Wu W B. 2019. From multiple cropping index to multiple cropping frequency: Observing cropland use intensity at a finer scale. Ecological Indicators, 101: 892-903
109. 109.
     Xie Y and Huang J X. 2021. Integration of a crop growth model and deep learning methods to improve satellite-based yield estimation of winter wheat in Henan Province, China. Remote Sensing, 13(21): 4372
110. 110.
     Xing Z F, Li Yi, Deng R X, Zhu H L and Fu B L. 2016. Extracting farmland shelterbelt automatically based on ZY-3 remote sensing images. Scientia Silvae Sinicae, 52(4): 11-20
111. 111.
     Xiong J H, Wu H, Gao Y, Cai S, Liang D and Yu W P. 2023. Ten years of remote sensing science: NSFC program fundings, progress and challenges. National Remote Sensing Bulletin, 27(4): 821-830
112. 112.
     Yan H M, Du W P, Zhou Y, Luo L and Niu Z. 2022. Satellite-based evidences to improve cropland productivity on the high-standard farmland project regions in Henan Province, China. Remote Sensing, 14(7): 1724
113. 113.
     Yang M Q, Zhao C L, Zhang S, Yu Q Y, Duan Y L, Dong J W, Shang G B and Wu W B. 2023. Analysis of cultivation status of Well-facilitated farmland in Northeast China. China Agricultural Informatics, 35(4): 49-58
114. 114.
     Yang R Y, Zhong Y F, Liu Y H, Lu X Y and Zhang L P. 2024. Occlusion-aware road extraction network for high-resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 62: 5619316
115. 115.
     Yu Q Y, Duan Y L, Wu Q Y, Liu Y, Wen C Y, Qian J P, Song Q, Li W J, Sun J and Wu W B. 2023. An interactive and iterative method for crop mapping through crowdsourcing optimized field samples. International Journal of Applied Earth Observation and Geoinformation, 122: 103409
116. 116.
     Yu Q Y, Xiang M T, Xie A K, Shen G, Yang P and Wu W B. 2018. Cropping intensity gap: a review. China Agricultural Informatics, 30(5): 1-12
117. 117.
     Yu W P, Tan J L, Ma M G, Li X L, She X J and Song Z J. 2019. An effective similar-pixel reconstruction of the high-frequency cloud-covered areas of southwest China. Remote Sensing, 11(3): 336
118. 118.
     Zha Y, Wu W B, Yu Q Y, Liang S F, Lu M, Qian J P and Tang H J. 2022. Strategic issues of monitoring and information services of agricultural water and land resources in China. Strategic Study of CAE, 24(1): 64-72
119. 119.
     Zhan W F, Chen Y H, Zhou J and Li J. 2011. An algorithm for separating soil and vegetation temperatures with sensors featuring a single thermal channel. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1796-1809
120. 120.
     Zhang M, Wu B F, Zeng H W, He G J, Liu C, Tao S Q, Zhang Q, Nabil M, Tian F Y, Bofana J, Beyene A N, Elnashar A, Yan N N, Wang Z D and Liu Y L. 2021. GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery. Earth System Science Data, 13(10): 4799-4817
121. 121.
     Zhang Y H, Xia G H, Wang J G and Lha D. 2019. A multiple feature fully convolutional network for road extraction from high-resolution remote sensing image over mountainous areas. IEEE Geoscience and Remote Sensing Letters, 16(10): 1600-1604
122. 122.
     Zhang Y M, Lei M, Lan X M, Zhang X Y, Fan S G and Gao J. 2024. The multiple effects of farmland infrastructure investment on agrifood systems in China—an interdisciplinary model analysis. China Agricultural Economic Review, 16(2): 320-339
123. 123.
     Zhao L C, Liu Y, Wen C Y, Zhang S B, Wu Q Y, Liang S F, Zhang S X, Wang Q F, You Z Y, Shi Y, Wu W B, Wang C, Duan Y L, Song Q, Lu M and Yu Q Y. 2022. Intelligent remote-sensing-based method for crop identification by crowdsource and genetic programming. China Agricultural Informatics, 34(1): 27-40
124. 124.
     Zhao W and Duan S B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sensing of Environment, 247: 111931
125. 125.
     Zhou L, Mu H W, Ma H J and Chen G X. 2019. Remote sensing estimation on yield of winter wheat in North China based on convolutional neural network. Transactions of the Chinese Society of Agricultural Engineering, 35(15): 119-128.
126. 126.
     Zhou X J, Zhang Y, Wang P X, Zhang S Y, Li H M and Tian H R. 2022. Reconstruction of time series LAI and winter wheat yield estimation at field scales based on sentinel satellites. Transactions of the Chinese Society for Agricultural Machinery, 53(8): 173-185
127. 127.
     Zhu Q Q, Zhang Y N, Wang L Z, Zhong Y F, Guan Q F, Lu X Y, Zhang L P and Li D R. 2021. A global context-aware and batch-independent network for road extraction from VHR satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 175: 353-365
128. 128.
     Zhuo W, Huang H, Gao X R, Li X C and Huang J X. 2023. An improved approach of winter wheat yield estimation by jointly assimilating remotely sensed leaf area index and soil moisture into the WOFOST model. Remote Sensing, 15(7): 1825