156 citations as recorded by crossref.

1. Remote Sensing-Based Long-Term Assessment of Water Dynamics and Influencing Factors in Abaya and Chamo Lakes, East African Rift Valley, Ethiopia B. Mulu et al. 10.1177/11786221241299932
2. Assessment of Three Methods for Near Real-Time Estimation of Leaf Area Index From AVHRR Data S. Kandasamy et al. 10.1109/TGRS.2016.2626307
3. Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US L. Zhong et al. 10.1016/j.rse.2019.111411
4. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency N. Madani et al. 10.1002/2014JG002709
5. Remote sensing for agricultural applications: A meta-review M. Weiss et al. 10.1016/j.rse.2019.111402
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7. Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS) J. Zhou et al. 10.1016/j.rse.2015.03.018
8. A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River L. Farhat et al. 10.3390/rs15215122
9. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
10. Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI E. Amin et al. 10.3390/rs14081812
11. Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow T. Vandal & R. Nemani 10.1109/TNNLS.2021.3101742
12. Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series N. Karasiak et al. 10.3390/rs11212512
13. A method for reconstructing NDVI time-series based on envelope detection and the Savitzky-Golay filter X. Liu et al. 10.1080/17538947.2022.2044397
14. Grassland Yield Estimation Using Transfer Learning from Remote Sensing Data E. Eder et al. 10.1007/s13218-023-00814-9
15. Usable observations over Europe: evaluation of compositing windows for Landsat and Sentinel-2 time series K. Lewińska et al. 10.1080/22797254.2024.2372855
16. Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau N. Li et al. 10.3390/rs12203383
17. Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover M. Kalumba et al. 10.3390/land11050618
18. Observing glacial isostatic adjustment by PSInSAR in southern Hudson Bay C. Wang et al. 10.1016/j.rse.2024.114023
19. An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions R. Jing et al. 10.3390/rs14071632
20. Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment L. Sun & K. Schulz 10.3390/rs9020144
21. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL N. Khanal et al. 10.3390/rs12182888
22. Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data X. Ni et al. 10.3390/cli5020037
23. Grassland mowing event detection using combined optical, SAR, and weather time series A. Holtgrave et al. 10.1016/j.rse.2023.113680
24. Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China H. Sun & Q. Xu 10.3390/rs13142848
25. Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series A. Diouf et al. 10.3390/rs70709122
26. Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images N. Siabi et al. 10.1016/j.compag.2021.106619
27. Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. 10.1029/2023JD039099
28. Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products M. Marshall et al. 10.5194/bg-13-625-2016
29. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine L. Liu et al. 10.1016/j.rse.2019.111624
30. From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Y. Hamrouni et al. 10.1016/j.isprsjprs.2020.10.018
31. Insuring Alpine Grasslands against Drought-Related Yield Losses Using Sentinel-2 Satellite Data M. Castelli et al. 10.3390/rs15143542
32. Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION A. Verger et al. 10.4995/raet.2016.5718
33. Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration J. Reyes-Garcia et al. 10.1109/ACCESS.2018.2853701
34. Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers Y. Yuan et al. 10.1007/s00484-021-02110-3
35. A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting T. Chen et al. 10.3390/atmos15030252
36. Imputation of Missing Parts in UAV Orthomosaics Using PlanetScope and Sentinel-2 Data: A Case Study in a Grass-Dominated Area F. Pereira et al. 10.3390/ijgi12020041
37. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection S. Belda et al. 10.1016/j.envsoft.2020.104666
38. Vegetation baseline phenology from kilometric global LAI satellite products A. Verger et al. 10.1016/j.rse.2016.02.057
39. The Value of L-Band Soil Moisture and Vegetation Optical Depth Estimates in the Prediction of Vegetation Phenology B. Li et al. 10.3390/rs13071343
40. Exploring denoising diffusion probabilistic model for daily streamflow gap filling in Central Asia typical watersheds G. He et al. 10.1016/j.ejrh.2024.101701
41. Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. 10.5194/bg-19-2805-2022
42. Unraveling the Multiple Drivers of Greening-Browning and Leaf Area Variability in a Socioeconomically Sensitive Drought-Prone Region K. Bageshree et al. 10.3390/cli10050070
43. Recurrent-based regression of Sentinel time series for continuous vegetation monitoring A. Garioud et al. 10.1016/j.rse.2021.112419
44. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al. 10.1029/2018RG000608
45. Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria M. Dhillon et al. 10.3390/rs14030677
46. Monitoring cropland LAI using Gaussian Process Regression and sentinel – 2 surface reflectance data in Google Earth Engine R. Sahoo et al. 10.1080/01431161.2024.2370503
47. The application of polynomial analyses to detect global vegetation dynamics during 1982–2012 Y. Liu et al. 10.1080/01431161.2016.1142688
48. A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations C. Xing et al. 10.3390/rs9050484
49. Long-gap Filling Method for the Coastal Monitoring Data H. Cho et al. 10.9765/KSCOE.2021.33.6.333
50. Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images J. Celis et al. 10.3390/rs16010046
51. Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring S. Belda et al. 10.3390/agronomy10050618
52. Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data E. Pignède et al. 10.3390/atmos12111459
53. Global Variability of Simulated and Observed Vegetation Growing Season D. Peano et al. 10.1029/2018JG004881
54. Satellite Image Time Series Decomposition Based on EEMD Y. Kong et al. 10.3390/rs71115583
55. Deep learning based multi-temporal crop classification L. Zhong et al. 10.1016/j.rse.2018.11.032
56. Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data Z. Cai et al. 10.3390/rs9121271
57. A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000–2018 S. Wang et al. 10.1038/s41597-021-00807-z
58. An open software package for data reconciliation and gap filling in preparation of Water and Resource Recovery Facility Modeling C. De Mulder et al. 10.1016/j.envsoft.2018.05.015
59. Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data W. Yu et al. 10.3390/rs13030484
60. Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series A. Gnilke & T. Sanders 10.3389/fpls.2022.863116
61. Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran M. Taie Semiromi & M. Koch 10.1080/02626667.2019.1669793
62. Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data R. Houborg & M. McCabe 10.3390/rs10060890
63. Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests K. Bórnez et al. 10.1016/j.jag.2019.101974
64. Evaluation of Air and Soil Temperatures for Determining the Onset of Growing Season R. Leeper et al. 10.1029/2020JG006171
65. An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20year NOAA AVHRR data over Canada S. Kandasamy & R. Fernandes 10.1016/j.rse.2015.04.014
66. Evaluating Visible–Infrared Imaging Radiometer Suite Imagery for Developing Near-Real-Time Nationwide Vegetation Cover Monitoring in Indonesia Y. Setiawan et al. 10.3390/rs16111958
67. A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery J. Brandt & F. Stolle 10.1080/01431161.2020.1841324
68. Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series E. Perry et al. 10.3390/rs14133071
69. Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data M. Kahiu & N. Hanan 10.1002/2017JG004105
70. Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data N. Siabi et al. 10.1016/j.jenvman.2020.110228
71. Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea C. Lim et al. 10.3390/rs12203282
72. From reanalysis to satellite observations: gap-filling with imbalanced learning J. Lu et al. 10.1007/s10707-020-00426-7
73. Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach F. Waseel et al. 10.1016/j.idm.2024.02.010
74. A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution D. Consoli et al. 10.7717/peerj.18585
75. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands C. Ding et al. 10.3390/rs9090968
76. Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing T. Mugiraneza et al. 10.3390/rs12182883
77. Global Gap-Free MERIS LAI Time Series (2002–2012) M. Tum et al. 10.3390/rs8010069
78. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products S. Mayr et al. 10.3390/rs11222616
79. Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine D. Kovács et al. 10.3390/rs15133404
80. GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products A. Verger et al. 10.1016/j.jag.2023.103479
81. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources S. Salcedo-Sanz et al. 10.1016/j.inffus.2020.07.004
82. Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco W. Nie et al. 10.5194/hess-26-2365-2022
83. Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images D. Pan et al. 10.3390/rs15225413
84. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud Á. Moreno-Martínez et al. 10.1016/j.rse.2020.111901
85. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine L. Pan et al. 10.1016/j.jag.2021.102376
86. Crop type mapping in the central part of the North China Plain using Sentinel-2 time series and machine learning K. Luo et al. 10.1016/j.compag.2022.107577
87. On the performance of remote sensing time series reconstruction methods – A spatial comparison J. Zhou et al. 10.1016/j.rse.2016.10.025
88. Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes L. Pipia et al. 10.1016/j.rse.2019.111452
89. Not the LAAST word, a response to Liebl's critique T. Niiler 10.1016/j.gaitpost.2021.11.001
90. Evapotranspiration Estimation Using Surface Energy Balance System Model: A Case Study in the Nagqu River Basin L. Zhong et al. 10.3390/atmos10050268
91. Modelling asynchrony in phenology considering a dynamic representation of meteorological variables R. de la Torre Cerro et al. 10.7717/peerj.18653
92. GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010 A. Verger et al. 10.1016/j.rse.2015.05.027
93. A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data J. Dong et al. 10.1080/01431161.2020.1755738
94. Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms M. Castelli 10.3390/rs13214316
95. Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits T. Musavi et al. 10.1002/ece3.2479
96. Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories R. Shang et al. 10.3390/rs10010122
97. Development of Spatio-Temporal Gap-Filling Technique for NDVI Images S. Kim et al. 10.7780/kjrs.2024.40.6.1.7
98. Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options M. Meroni et al. 10.1016/j.rse.2018.11.041
99. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability R. Liu et al. 10.1016/j.rse.2016.11.023
100. Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information H. Zhao et al. 10.3390/rs13142790
101. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al. 10.1016/j.rse.2019.111511
102. A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation B. Qiu et al. 10.1016/j.jag.2015.11.009
103. Mapping Croplands in the Granary of the Tibetan Plateau Using All Available Landsat Imagery, A Phenology-Based Approach, and Google Earth Engine Y. Di et al. 10.3390/rs13122289
104. Using MODIS Data to Predict Regional Corn Yields H. Ban et al. 10.3390/rs9010016
105. A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data C. Santana et al. 10.3390/rs16142520
106. Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species J. Gonçalves et al. 10.1007/s10531-016-1206-7
107. Remotely sensed soil moisture to estimate savannah NDVI N. Boke-Olén et al. 10.1371/journal.pone.0200328
108. Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine Y. Guo et al. 10.3390/rs14041004
109. Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis M. Zempila et al. 10.1016/j.jqsrt.2016.06.003
110. Annual Sea Level Amplitude Analysis over the North Pacific Ocean Coast by Ensemble Empirical Mode Decomposition Method W. Lan et al. 10.3390/rs13040730
111. Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale M. Cheng et al. 10.1186/s12870-025-06146-0
112. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques S. Li et al. 10.1016/j.jag.2021.102640
113. Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison H. Xu et al. 10.1016/j.agrformet.2023.109376
114. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery X. Zhang et al. 10.1016/j.agrformet.2018.03.003
115. Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion Y. Wang et al. 10.1007/s11356-021-15387-z
116. Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine Y. Guo et al. 10.3390/ijgi10090587
117. Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea S. Jeong et al. 10.3390/rs10101665
118. Evaluation of MODIS-Landsat and AVHRR-Landsat NDVI data fusion using a single pair base reference image: a case study in a tropical upstream catchment on Java, Indonesia A. Rustanto & M. Booij 10.1080/17538947.2021.2018057
119. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring S. Mondal et al. 10.1080/15481603.2017.1286726
120. Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm X. Guo et al. 10.1016/j.agrformet.2024.109962
121. Monitoring mangrove traits through optical Earth observation: Towards spatio-temporal scalability using cloud-based Sentinel-2 continuous time series N. An Binh et al. 10.1016/j.isprsjprs.2024.06.007
122. Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles K. Bai et al. 10.5194/amt-13-1213-2020
123. Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia H. Zandler et al. 10.1038/s41598-020-79480-y
124. Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data Y. Ma et al. 10.3390/rs13010086
125. Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine L. Pan et al. 10.3390/rs13132510
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127. The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps X. Qi et al. 10.1109/JSTARS.2022.3220698
128. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity L. Kooistra et al. 10.5194/bg-21-473-2024
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130. Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images M. Maleki et al. 10.3390/rs12132104
131. Status of Phenological Research Using Sentinel-2 Data: A Review G. Misra et al. 10.3390/rs12172760
132. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. 10.1111/gcb.12950
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134. Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series Z. Tang et al. 10.3390/rs14010172
135. Reconstructing the Seasonality and Trend in Global Leaf Area Index During 2001–2017 for Prognostic Modeling M. Wang et al. 10.1029/2020JG005698
136. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia S. Arjasakusuma et al. 10.3390/ijgi9110663
137. Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires A. McFadden et al. 10.3390/fire7060179
138. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series X. Zhu et al. 10.1109/TGRS.2021.3095535
139. In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model M. Mao et al. 10.3390/agronomy13071723
140. HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 K. Yan et al. 10.1038/s41597-025-04391-4
141. Leveraging browse and grazing forage estimates to optimize index-based livestock insurance N. Kahiu et al. 10.1038/s41598-024-62893-4
142. Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record K. Lewińska et al. 10.1016/j.srs.2021.100035
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144. Discussion of the paper “analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan” O. Nicolis & J. Mateu 10.1007/s10260-015-0311-1
145. Local species assemblages are influenced more by past than current dissimilarities in photosynthetic activity M. Jung et al. 10.1111/ecog.04031
146. Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa H. De Lemos et al. 10.3390/rs12233927
147. Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data C. Doherty & M. Mauter 10.1109/JSTARS.2024.3517415
148. Forecasting PM10 Concentrations Using Artificial Neural Network in Imphal City N. Premananda Singh & R. Laishram 10.38124/ijisrt/IJISRT24JUL154
149. Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework M. Wang et al. 10.1515/geo-2022-0369
150. Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence–Sequence Model T. Peng et al. 10.1109/TGRS.2022.3208926
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