143 citations as recorded by crossref.

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3. Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2 J. Han et al. 10.3390/rs13010105
4. Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers J. Zhang et al. 10.3390/rs14174189
5. The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter H. Huang et al. 10.1109/TGRS.2023.3259742
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7. Moderately reducing N input to mitigate heat stress in maize Y. Zhou et al. 10.1016/j.scitotenv.2024.173143
8. An integrated approach to obtain high-precision regional root water uptake maps Y. Liu et al. 10.1016/j.jhydrol.2024.131771
9. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain J. Li & H. Lei 10.1088/2515-7620/ac814c
10. Increased Grain Crop Production Intensifies the Water Crisis in Northern China X. Li et al. 10.1029/2023EF003608
11. Evaluating the 2019 NARO-APCC Joint Crop Forecasting Service Yield Forecasts for Northern Hemisphere Countries T. Iizumi et al. 10.1175/WAF-D-20-0149.1
12. Mapping annual center-pivot irrigated cropland in Brazil during the 1985–2021 period with cloud platforms and deep learning X. Liu et al. 10.1016/j.isprsjprs.2023.10.007
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15. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China M. Cheng et al. 10.1016/j.agrformet.2022.109057
16. Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources Y. Zou et al. 10.3390/rs16040701
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19. Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method X. Wang et al. 10.1080/15481603.2024.2333587
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23. Patterns and causes of winter wheat and summer maize rotation area change over the North China Plain Z. Liu et al. 10.1088/1748-9326/ac6006
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26. Fuzzy clustering for the within-season estimation of cotton phenology V. Sitokonstantinou et al. 10.1371/journal.pone.0282364
27. The combined effects of VPD and soil moisture on historical maize yield and prediction in China F. Zhao et al. 10.3389/fenvs.2023.1117184
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29. Seasonal variations and driving mechanisms of CO2 fluxes over a winter-wheat and summer-maize rotation cropland in the North China plain Z. Yue et al. 10.1016/j.agrformet.2023.109699
30. Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China Z. Liu et al. 10.3390/agriculture14040638
31. TPE-CatBoost: An adaptive model for soil moisture spatial estimation in the main maize-producing areas of China with multiple environment covariates J. Yu et al. 10.1016/j.jhydrol.2022.128465
32. A novel approach to detect the spring corn phenology using layered strategy Y. Ma et al. 10.1016/j.jag.2023.103422
33. Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios H. Chen et al. 10.1016/j.envexpbot.2024.105938
34. Updated loss factors and high-resolution spatial variations for reactive nitrogen losses from Chinese rice paddies Y. Shang et al. 10.1016/j.jenvman.2024.120752
35. A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets M. Bai et al. 10.3390/land11101686
36. Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches J. Cao et al. 10.1016/j.agrformet.2020.108275
37. Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning Y. Sun et al. 10.3390/agriculture12050571
38. Mapping the spatiotemporal patterns of tillage practices across Chinese croplands with Google Earth Engine Y. Wang et al. 10.1016/j.compag.2023.108509
39. ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. 10.5194/essd-16-1689-2024
40. Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize Y. Liu et al. 10.1016/j.agwat.2024.109056
41. Mapping annual 10-m maize cropland changes in China during 2017–2021 X. Li et al. 10.1038/s41597-023-02665-3
42. An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping J. Yang et al. 10.1016/j.rse.2024.114387
43. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation Y. He et al. 10.3390/rs15020414
44. FARM: A fully automated rice mapping framework combining Sentinel-1 SAR and Sentinel-2 multi-temporal imagery Y. Gao et al. 10.1016/j.compag.2023.108262
45. A novel soybean mapping index within the global optimal time window G. Xiao et al. 10.1016/j.isprsjprs.2024.08.006
46. Weather Index Insurance Can Offset Heat‐Induced Rice Losses Under Global Warming J. Zhang et al. 10.1029/2021EF002534
47. A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020 S. He et al. 10.5194/essd-14-5463-2022
48. ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China Y. Zhao et al. 10.5194/essd-15-4047-2023
49. Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework C. Du et al. 10.1016/j.scitotenv.2024.176293
50. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine J. Cao et al. 10.1016/j.eja.2020.126204
51. Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning L. Zhang et al. 10.1016/j.agrformet.2021.108666
52. Quantitative prediction of dynamic HTO migration behavior in the soil and non-negligible evapotranspiration effect B. Nie et al. 10.1016/j.jhazmat.2021.127772
53. Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
54. Food-water-land-ecosystem nexus in typical Chinese dryland under different future scenarios M. Shi et al. 10.1016/j.scitotenv.2023.163183
55. Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China L. Wang et al. 10.1177/03091333231188814
56. Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area Y. Chen & F. Tao 10.1016/j.fcr.2021.108398
57. Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data Z. Zhang et al. 10.1016/j.agrformet.2020.107993
58. Assessment of bioenergy plant locations using a GIS-MCDA approach based on spatio-temporal stability maps of agricultural and livestock byproducts: A case study Z. Shi et al. 10.1016/j.scitotenv.2024.174665
59. Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold Y. Ma et al. 10.3390/rs16050826
60. Concurrent Precipitation Extremes Modulate the Response of Rice Transplanting Date to Preseason Temperature Extremes in China Y. Liu et al. 10.1029/2022EF002888
61. Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models D. Yin et al. 10.1007/s00376-023-2234-3
62. Phenomenon of Non-Grain Production of Cultivated Land Has Become Increasingly Prominent over the Last 20 Years: Evidence from Guanzhong Plain, China Z. Zhu et al. 10.3390/agriculture12101654
63. The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data X. Xu et al. 10.1038/s41597-024-03866-0
64. Should phenological information be applied to predict agronomic traits across growth stages of winter wheat? Y. Zhao et al. 10.1016/j.cj.2022.08.003
65. Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain H. Zhuang et al. 10.1016/j.agrformet.2024.109909
66. ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018 F. Cheng et al. 10.5194/essd-15-395-2023
67. Irrigation plays significantly different roles in influencing hydrological processes in two breadbasket regions Y. Wang et al. 10.1016/j.scitotenv.2022.157253
68. Ozone pollution threatens the production of major staple crops in East Asia Z. Feng et al. 10.1038/s43016-021-00422-6
69. Human activities are the key driver of water erosion changes in northeastern China Q. Tang et al. 10.1002/ldr.4897
70. Retrospective Study on the Seasonal Forecast-Based Disease Intervention of the Wheat Blast Outbreaks in Bangladesh K. Kim & E. Choi 10.3389/fpls.2020.570381
71. Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability L. Yang et al. 10.3390/rs15092443
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73. Designing wheat cultivar adaptation to future climate change across China by coupling biophysical modelling and machine learning F. Tao et al. 10.1016/j.eja.2022.126500
74. Advancement and Applications of Forest Remote Sensing in Korea: Past, Present, and Future Perspectives K. Kim et al. 10.7780/kjrs.2024.40.5.2.8
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77. Applying double cropping and interactive irrigation in the North China Plain using WRF4.5 Y. Fan et al. 10.5194/gmd-17-6929-2024
78. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing M. Cheng et al. 10.1038/s41597-022-01761-0
79. Sun-induced chlorophyll fluorescence is superior to satellite vegetation indices for predicting summer maize yield under drought conditions Y. Wang et al. 10.1016/j.compag.2023.107615
80. Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region Z. Shi et al. 10.1016/j.jclepro.2024.140621
81. Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model X. Wang et al. 10.1016/j.agwat.2023.108294
82. Reduced actual vapor pressure exerts a significant influence on maize yield through vapor pressure deficit amid climate warming Y. Zhang et al. 10.1007/s00484-024-02727-0
83. Heat stress may cause a significant reduction of rice yield in China under future climate scenarios Q. Sun et al. 10.1016/j.scitotenv.2021.151746
84. Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine F. Zhi et al. 10.3390/rs14164028
85. Vulnerability of Wheat Crops to Flooding Outweighs Benefits from Precision Farming and Agroecology Practices: A Case Study in Central Italy E. Santangelo et al. 10.3390/land12040915
86. A coupled framework for estimating pollutant emissions from open burning of specific crop residue: A case study for wheat Y. Zhou et al. 10.1016/j.scitotenv.2022.156731
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88. Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models L. Pan et al. 10.1016/j.isprsjprs.2024.09.023
89. City-level decoupling between livestock and crop production and its effect on fertilizer usage: Evidence from China X. Kang et al. 10.1016/j.scitotenv.2023.167115
90. Predicting China’s Maize Yield Using Multi-Source Datasets and Machine Learning Algorithms L. Miao et al. 10.3390/rs16132417
91. Mapping irrigation regimes in Chinese paddy lands through multi-source data assimilation Y. Wang et al. 10.1016/j.agwat.2024.109083
92. Mapping of Phenological Traits in Northeast China Maize (Zea mays L.) X. Wang et al. 10.3390/agronomy12102585
93. The divergent response of vegetation phenology to urbanization: A case study of Beijing city, China Y. Zhang et al. 10.1016/j.scitotenv.2021.150079
94. Gaps between Rice Actual and Potential Yields Based on the VPM and GAEZ Models in Heilongjiang Province, China L. Pu et al. 10.3390/agriculture14020277
95. Prediction of winter wheat yield at county level in China using ensemble learning Y. Zhang et al. 10.1177/03091333221088018
96. Blaming the wind? The impact of wind turbine on bird biodiversity L. Meng et al. 10.1016/j.jdeveco.2024.103402
97. Have the agricultural production systems in the North China Plain changed towards to climate smart agriculture since 2000? Y. Xin & F. Tao 10.1016/j.jclepro.2021.126940
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116. Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning Y. Ren et al. 10.3390/plants12030446
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131. Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use S. Bajocco et al. 10.3390/land10060656
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137. Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China R. Zhang et al. 10.3390/rs14061396
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139. Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches L. Zhang et al. 10.3390/rs12010021
140. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China M. Cheng et al. 10.1016/j.agrformet.2022.109057
141. Multi-Source Data Modeling of the Spatial Distribution of Winter Wheat Yield in China from 2000 to 2015 D. Han et al. 10.3390/su12135436
142. Applying double cropping and interactive irrigation in the North China Plain using WRF4.5 Y. Fan et al. 10.5194/gmd-17-6929-2024
143. Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China S. Chen et al. 10.1016/j.jhazmat.2023.133385