143 citations as recorded by crossref.

1. 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
2. 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
3. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency N. Madani et al. 10.1002/2014JG002709
4. Remote sensing for agricultural applications: A meta-review M. Weiss et al. 10.1016/j.rse.2019.111402
5. 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
6. 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
7. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
8. 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
9. Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow T. Vandal & R. Nemani 10.1109/TNNLS.2021.3101742
10. 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
11. 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
12. Grassland Yield Estimation Using Transfer Learning from Remote Sensing Data E. Eder et al. 10.1007/s13218-023-00814-9
13. 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
14. 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
15. Observing glacial isostatic adjustment by PSInSAR in southern Hudson Bay C. Wang et al. 10.1016/j.rse.2024.114023
16. An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions R. Jing et al. 10.3390/rs14071632
17. Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment L. Sun & K. Schulz 10.3390/rs9020144
18. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL N. Khanal et al. 10.3390/rs12182888
19. Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data X. Ni et al. 10.3390/cli5020037
20. Grassland mowing event detection using combined optical, SAR, and weather time series A. Holtgrave et al. 10.1016/j.rse.2023.113680
21. 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
22. Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series A. Diouf et al. 10.3390/rs70709122
23. 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
24. Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. 10.1029/2023JD039099
25. 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
26. 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
27. 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
28. Insuring Alpine Grasslands against Drought-Related Yield Losses Using Sentinel-2 Satellite Data M. Castelli et al. 10.3390/rs15143542
29. Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION A. Verger et al. 10.4995/raet.2016.5718
30. Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration J. Reyes-Garcia et al. 10.1109/ACCESS.2018.2853701
31. 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
32. A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting T. Chen et al. 10.3390/atmos15030252
33. 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
34. 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
35. Vegetation baseline phenology from kilometric global LAI satellite products A. Verger et al. 10.1016/j.rse.2016.02.057
36. 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
37. 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
38. 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
39. 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
40. Recurrent-based regression of Sentinel time series for continuous vegetation monitoring A. Garioud et al. 10.1016/j.rse.2021.112419
41. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al. 10.1029/2018RG000608
42. Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria M. Dhillon et al. 10.3390/rs14030677
43. The application of polynomial analyses to detect global vegetation dynamics during 1982–2012 Y. Liu et al. 10.1080/01431161.2016.1142688
44. 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
45. Long-gap Filling Method for the Coastal Monitoring Data H. Cho et al. 10.9765/KSCOE.2021.33.6.333
46. 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
47. Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring S. Belda et al. 10.3390/agronomy10050618
48. Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data E. Pignède et al. 10.3390/atmos12111459
49. Global Variability of Simulated and Observed Vegetation Growing Season D. Peano et al. 10.1029/2018JG004881
50. Satellite Image Time Series Decomposition Based on EEMD Y. Kong et al. 10.3390/rs71115583
51. Deep learning based multi-temporal crop classification L. Zhong et al. 10.1016/j.rse.2018.11.032
52. Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data Z. Cai et al. 10.3390/rs9121271
53. 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
54. 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
55. Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data W. Yu et al. 10.3390/rs13030484
56. Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series A. Gnilke & T. Sanders 10.3389/fpls.2022.863116
57. 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
58. 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
59. Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests K. Bórnez et al. 10.1016/j.jag.2019.101974
60. Evaluation of Air and Soil Temperatures for Determining the Onset of Growing Season R. Leeper et al. 10.1029/2020JG006171
61. 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
62. 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
63. Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series E. Perry et al. 10.3390/rs14133071
64. Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data M. Kahiu & N. Hanan 10.1002/2017JG004105
65. 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
66. 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
67. From reanalysis to satellite observations: gap-filling with imbalanced learning J. Lu et al. 10.1007/s10707-020-00426-7
68. 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
69. 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
70. 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
71. Global Gap-Free MERIS LAI Time Series (2002–2012) M. Tum et al. 10.3390/rs8010069
72. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products S. Mayr et al. 10.3390/rs11222616
73. 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
74. 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
75. 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
76. 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
77. Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images D. Pan et al. 10.3390/rs15225413
78. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud Á. Moreno-Martínez et al. 10.1016/j.rse.2020.111901
79. 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
80. 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
81. On the performance of remote sensing time series reconstruction methods – A spatial comparison J. Zhou et al. 10.1016/j.rse.2016.10.025
82. Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes L. Pipia et al. 10.1016/j.rse.2019.111452
83. Not the LAAST word, a response to Liebl's critique T. Niiler 10.1016/j.gaitpost.2021.11.001
84. Evapotranspiration Estimation Using Surface Energy Balance System Model: A Case Study in the Nagqu River Basin L. Zhong et al. 10.3390/atmos10050268
85. 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
86. 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
87. Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms M. Castelli 10.3390/rs13214316
88. Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits T. Musavi et al. 10.1002/ece3.2479
89. 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
90. 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
91. 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
92. 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
93. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al. 10.1016/j.rse.2019.111511
94. 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
95. 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
96. Using MODIS Data to Predict Regional Corn Yields H. Ban et al. 10.3390/rs9010016
97. 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
98. Remotely sensed soil moisture to estimate savannah NDVI N. Boke-Olén et al. 10.1371/journal.pone.0200328
99. Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine Y. Guo et al. 10.3390/rs14041004
100. 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
101. Annual Sea Level Amplitude Analysis over the North Pacific Ocean Coast by Ensemble Empirical Mode Decomposition Method W. Lan et al. 10.3390/rs13040730
102. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques S. Li et al. 10.1016/j.jag.2021.102640
103. 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
104. 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
105. 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
106. 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
107. 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
108. 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
109. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring S. Mondal et al. 10.1080/15481603.2017.1286726
110. 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
111. Filling the gaps of in situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles K. Bai et al. 10.5194/amt-13-1213-2020
112. 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
113. Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data Y. Ma et al. 10.3390/rs13010086
114. 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
115. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season D. Peano et al. 10.5194/bg-18-2405-2021
116. 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
117. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity L. Kooistra et al. 10.5194/bg-21-473-2024
118. Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals Y. Lu et al. 10.1175/JHM-D-19-0130.1
119. 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
120. Status of Phenological Research Using Sentinel-2 Data: A Review G. Misra et al. 10.3390/rs12172760
121. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. 10.1111/gcb.12950
122. An evapotranspiration model driven by remote sensing data for assessing groundwater resource in karst watershed C. Ollivier et al. 10.1016/j.scitotenv.2021.146706
123. Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series Z. Tang et al. 10.3390/rs14010172
124. Reconstructing the Seasonality and Trend in Global Leaf Area Index During 2001–2017 for Prognostic Modeling M. Wang et al. 10.1029/2020JG005698
125. 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
126. 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
127. 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
128. 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
129. Wall-to-wall mapping of tree extent in the tropics with Sentinel-1 and Sentinel-2 J. Brandt et al. 10.1016/j.rse.2023.113574
130. 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
131. Local species assemblages are influenced more by past than current dissimilarities in photosynthetic activity M. Jung et al. 10.1111/ecog.04031
132. Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa H. De Lemos et al. 10.3390/rs12233927
133. 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
134. 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
135. Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation M. Hunt et al. 10.1016/j.jag.2019.05.013
136. Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks H. Ren et al. 10.5194/hess-26-1727-2022
137. The dynamics of tea plantation encroachment into forests and effect on forest landscape pattern during 1991–2021 through time series Landsat images Y. Xiong et al. 10.1016/j.ecolind.2022.109132
138. Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis B. Parmentier 10.3390/rs61212639
139. Deforestation in Michoacan, Mexico, From CYCLOPES-LAI Time Series (2000–2006) L. Valderrama-Landeros et al. 10.1109/JSTARS.2016.2597742
140. Near Real-Time Vegetation Monitoring at Global Scale A. Verger et al. 10.1109/JSTARS.2014.2328632
141. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production F. Baret et al. 10.1016/j.rse.2012.12.027
142. Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements R. Paloschi et al. 10.3390/rs13010075
143. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter Á. Moreno et al. 10.3390/rs6098238