150 citations as recorded by crossref.

1. Adapting to climate change precisely through cultivars renewal for rice production across China: When, where, and what cultivars will be required? L. Zhang et al. 10.1016/j.agrformet.2022.108856
2. Mapping ecological resource use and supply-consumption difference in China from 2000 to 2020 F. Lyu et al. 10.1016/j.resconrec.2024.107879
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
6. Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images X. Zhao et al. 10.5194/essd-16-3893-2024
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
13. 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
14. A 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
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
17. Consistency and uncertainty of remote sensing-based approaches for regional yield gap estimation: A comprehensive assessment of process-based and data-driven models J. Wang et al. 10.1016/j.fcr.2023.109088
18. Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 W. Xie et al. 10.1080/10106049.2020.1822928
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
20. The challenge of chilling injury amid shifting maize planting boundaries: A case study of Northeast China S. Guga et al. 10.1016/j.agsy.2024.104166
21. Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes Q. Wang et al. 10.1038/s41598-024-59336-5
22. Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data Z. Li et al. 10.1016/j.agsy.2021.103141
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
24. Feature-based algorithm for large-scale rice phenology detection based on satellite images X. Zhao et al. 10.1016/j.agrformet.2022.109283
25. County-level intensity of carbon emissions from crop farming in China during 2000–2019 C. Li et al. 10.1038/s41597-024-03296-y
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
28. Spatio-temporal patterns and drivers of irrigation water requirement in mainland China X. Shi & W. Zhu 10.1016/j.agwat.2024.109283
29. Exploring China's food security evolution from a local perspective X. Liang et al. 10.1016/j.apgeog.2024.103427
30. 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
31. 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
32. 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
33. A novel approach to detect the spring corn phenology using layered strategy Y. Ma et al. 10.1016/j.jag.2023.103422
34. 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
35. 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
36. Revealing Climate-Induced Patterns in Crop Yields and the Water-Energy-Food-Carbon Nexus: Insights from the Pearl River Basin C. Ye et al. 10.3390/w16243693
37. 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
38. 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
39. Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning Y. Sun et al. 10.3390/agriculture12050571
40. Mitigating climate change and ozone pollution will improve Chinese food security S. Li et al. 10.1016/j.oneear.2024.12.002
41. Mapping the spatiotemporal patterns of tillage practices across Chinese croplands with Google Earth Engine Y. Wang et al. 10.1016/j.compag.2023.108509
42. Development of a high-spatial-resolution annual emission inventory of greenhouse gases from open straw burning in Northeast China from 2001 to 2020 Z. Song et al. 10.5194/acp-24-13101-2024
43. ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. 10.5194/essd-16-1689-2024
44. 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
45. Mapping annual 10-m maize cropland changes in China during 2017–2021 X. Li et al. 10.1038/s41597-023-02665-3
46. 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
47. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation Y. He et al. 10.3390/rs15020414
48. 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
49. A novel soybean mapping index within the global optimal time window G. Xiao et al. 10.1016/j.isprsjprs.2024.08.006
50. Weather Index Insurance Can Offset Heat‐Induced Rice Losses Under Global Warming J. Zhang et al. 10.1029/2021EF002534
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
58. Food-water-land-ecosystem nexus in typical Chinese dryland under different future scenarios M. Shi et al. 10.1016/j.scitotenv.2023.163183
59. 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
60. 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
61. 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
62. 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
63. 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
64. Impacts of changes in climate extremes on maize yields over Mainland China S. Deng et al. 10.1007/s12571-024-01501-9
65. Concurrent Precipitation Extremes Modulate the Response of Rice Transplanting Date to Preseason Temperature Extremes in China Y. Liu et al. 10.1029/2022EF002888
66. Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models D. Yin et al. 10.1007/s00376-023-2234-3
67. 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
68. 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
69. 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
70. 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
71. 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
72. Irrigation plays significantly different roles in influencing hydrological processes in two breadbasket regions Y. Wang et al. 10.1016/j.scitotenv.2022.157253
73. Ozone pollution threatens the production of major staple crops in East Asia Z. Feng et al. 10.1038/s43016-021-00422-6
74. Human activities are the key driver of water erosion changes in northeastern China Q. Tang et al. 10.1002/ldr.4897
75. 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
76. Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability L. Yang et al. 10.3390/rs15092443
77. A review of remote sensing estimation of crop water productivity: definition, methodology, scale, and evaluation M. Cheng et al. 10.1080/01431161.2023.2240523
78. Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations X. Chen et al. 10.3389/fenvs.2024.1503141
79. 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
80. 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
81. Global data on fertilizer use by crop and by country C. Ludemann et al. 10.1038/s41597-022-01592-z
82. Improving the matching degree between remotely sensed phenological dates and physiological growing stages of soybean by a dynamic offset-adjustment strategy S. Chen et al. 10.1016/j.scitotenv.2023.167783
83. Applying double cropping and interactive irrigation in the North China Plain using WRF4.5 Y. Fan et al. 10.5194/gmd-17-6929-2024
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015 H. Wu et al. 10.5194/essd-15-791-2023
94. 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
95. 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
96. Predicting China’s Maize Yield Using Multi-Source Datasets and Machine Learning Algorithms L. Miao et al. 10.3390/rs16132417
97. Mapping irrigation regimes in Chinese paddy lands through multi-source data assimilation Y. Wang et al. 10.1016/j.agwat.2024.109083
98. Mapping of Phenological Traits in Northeast China Maize (Zea mays L.) X. Wang et al. 10.3390/agronomy12102585
99. 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
100. 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
101. Prediction of winter wheat yield at county level in China using ensemble learning Y. Zhang et al. 10.1177/03091333221088018
102. Blaming the wind? The impact of wind turbine on bird biodiversity L. Meng et al. 10.1016/j.jdeveco.2024.103402
103. 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
104. Integration of prognostic sowing and harvesting schemes to enhance crop dynamic growth simulation in Noah-MP-Crop model F. Wang et al. 10.1016/j.ecoinf.2024.102785
105. Effects of climate change, crop planting structure, and agricultural management on runoff, sediment, nitrogen and phosphorus losses in the Hai-River Basin since the 1980s N. Ding et al. 10.1016/j.jclepro.2022.132066
106. Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data Y. Yang et al. 10.1016/j.isprsjprs.2020.10.005
107. Changes in Extreme Climate Events in Rice-Growing Regions Under Different Warming Scenarios in China J. Chou et al. 10.3389/feart.2021.655128
108. A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang Province as a test case X. Li et al. 10.1080/01431161.2021.1957177
109. A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
110. Compound drought and hot stresses projected to be key constraints on maize production in Northeast China under future climate C. Zhang et al. 10.1016/j.compag.2024.108688
111. Evaluation and Analysis of the County-Level Sustainable Development Process in Guangxi, China in 2014–2020 L. Shao et al. 10.3390/su16041641
112. Mapping rapeseed planting areas using an automatic phenology- and pixel-based algorithm (APPA) in Google Earth Engine J. Han et al. 10.1016/j.cj.2022.04.013
113. Simulating the effects of low-temperature stress on wheat biomass growth and yield L. Xiao et al. 10.1016/j.agrformet.2022.109191
114. Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations G. Zhao et al. 10.1016/j.agee.2023.108651
115. Searching for “Win-Win” solutions for food-water-GHG emissions tradeoffs across irrigation regimes of paddy rice in China Z. Tian et al. 10.1016/j.resconrec.2020.105360
116. Improved ammonia emission inventory of fertilizer application for three major crops in China based on phenological data Y. Zhao et al. 10.1016/j.scitotenv.2023.165225
117. GCPE: The global dataset of crop phenological events for agricultural and earth system modeling A. MORI et al. 10.2480/agrmet.D-23-00004
118. Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors Y. Huang et al. 10.3390/rs14174415
119. Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the Northern Hemisphere L. Zhou et al. 10.1016/j.ecolind.2022.108579
120. A dataset of social-economic and environmental parameters at township and county levels in Guangxi Zhuang Autonomous Region L. SHAO et al. 10.11922/11-6035.csd.2024.0049.zh
121. NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019 J. Han et al. 10.5194/essd-13-5969-2021
122. 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
123. Monitoring and Assessing Gross Primary Productivity of Paddy Rice (Oryza sativa L.) Cropland in Southern China Between 2000 and 2015 H. Wang et al. 10.1007/s42106-022-00215-2
124. A 30 m Resolution Distribution Map of Maize for China Based on Landsat and Sentinel Images R. Shen et al. 10.34133/2022/9846712
125. Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest: A Case Study in Henan Province, China X. Shi et al. 10.1007/s11769-024-1421-1
126. Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources Y. Luo et al. 10.1088/1748-9326/ab80f0
127. Multi-model ensemble of CMIP6 projections for future extreme climate changes in wheat production regions of China Z. Shi et al. 10.1007/s00382-024-07151-z
128. A biophysical model to simulate seasonal variations of soil respiration in agroecosystems in China S. Chen et al. 10.1016/j.agrformet.2023.109524
129. Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate C. Zhang et al. 10.1002/jsfa.13247
130. Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning L. Zhang et al. 10.1088/1748-9326/ac32fd
131. Mapping annual 10 m rapeseed extent using multisource data in the Yangtze River Economic Belt of China (2017–2021) on Google Earth Engine W. Liu & H. Zhang 10.1016/j.jag.2023.103198
132. High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms X. Jin et al. 10.1109/MGRS.2020.2998816
133. Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios D. Wang et al. 10.1016/j.scitotenv.2023.166158
134. Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method C. Hai et al. 10.3390/rs15174216
135. Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events Y. Zhao et al. 10.3390/rs16071259
136. The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data J. Han et al. 10.5194/essd-13-2857-2021
137. 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
138. Climatic impacts induced by winter wheat irrigation over North China simulated by the nonhydrostatic RegCM4.7 J. Wu et al. 10.1016/j.accre.2024.04.002
139. Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model Y. Luo et al. 10.1007/s11430-020-9821-0
140. A twenty-year dataset of high-resolution maize distribution in China Q. Peng et al. 10.1038/s41597-023-02573-6
141. Assessing Vegetation Canopy Growth Variations in Northeast China L. Lu et al. 10.3390/plants14010143
142. Projecting the impact of climate change and elevated CO2 concentration on rice irrigation water requirement in China B. Sun et al. 10.1016/j.scitotenv.2023.168489
143. Mapping tea plantation area using phenology algorithm, time-series Sentinel-2 and Landsat images H. Xia et al. 10.1080/01431161.2023.2208713
144. Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China R. Zhang et al. 10.3390/rs14061396
145. Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China J. Han et al. 10.3390/rs12020236
146. 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
147. 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
148. 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
149. Applying double cropping and interactive irrigation in the North China Plain using WRF4.5 Y. Fan et al. 10.5194/gmd-17-6929-2024
150. 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