183 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.
2. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency N. Madani et al.
3. Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification A. González-Ramírez et al.
4. 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.
5. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al.
6. Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI E. Amin et al.
7. A method for reconstructing NDVI time-series based on envelope detection and the Savitzky-Golay filter X. Liu et al.
8. Observing glacial isostatic adjustment by PSInSAR in southern Hudson Bay C. Wang et al.
9. An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions R. Jing et al.
10. Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data X. Ni et al.
11. Grassland mowing event detection using combined optical, SAR, and weather time series A. Holtgrave et al.
12. Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China H. Sun & Q. Xu
13. Phenology-preserving temporal reconstruction of satellite-derived NDVI via morphological operations for unsupervised vegetation clustering H. Hosseini et al.
14. Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al.
15. Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products M. Marshall et al.
16. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine L. Liu et al.
17. More Accurate and Reliable Phenology Retrieval in Southwest China: Multi-Method Comparison and Uncertainty Analysis F. Tang et al.
18. A New Addition to Global Soil Moisture Mapping: CFOSAT Scatterometer Algorithm Development and Validation P. Duan et al.
19. Insuring Alpine Grasslands against Drought-Related Yield Losses Using Sentinel-2 Satellite Data M. Castelli et al.
20. Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration J. Reyes-Garcia et al.
21. A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting T. Chen et al.
22. Enhanced Crop Mapping Using Polarimetric SAR Features and Time Series Deep Learning: A Case Study in Bei’an, China N. Liu et al.
23. 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.
24. Vegetation baseline phenology from kilometric global LAI satellite products A. Verger et al.
25. Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al.
26. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al.
27. Monitoring cropland LAI using Gaussian Process Regression and sentinel – 2 surface reflectance data in Google Earth Engine R. Sahoo et al.
28. The application of polynomial analyses to detect global vegetation dynamics during 1982–2012 Y. Liu et al.
29. Long-gap Filling Method for the Coastal Monitoring Data H. Cho et al.
30. Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring S. Belda et al.
31. Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data E. Pignède et al.
32. Global Variability of Simulated and Observed Vegetation Growing Season D. Peano et al.
33. Satellite Image Time Series Decomposition Based on EEMD Y. Kong et al.
34. A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000–2018 S. Wang et al.
35. Satellite-based species-level monitoring of mangrove phenology and climate responses using EVI and lagged environmental correlations (2013–2024) W. Koedsin et al.
36. 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
37. Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data N. Siabi et al.
38. From reanalysis to satellite observations: gap-filling with imbalanced learning J. Lu et al.
39. Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach F. Waseel et al.
40. Development of an enhanced hybrid piecewise logistic model for retrieving land surface phenology in drylands Y. Ye et al.
41. Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing T. Mugiraneza et al.
42. Global Gap-Free MERIS LAI Time Series (2002–2012) M. Tum et al.
43. Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine D. Kovács et al.
44. GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products A. Verger et al.
45. Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images D. Pan et al.
46. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud Á. Moreno-Martínez et al.
47. Crop type mapping in the central part of the North China Plain using Sentinel-2 time series and machine learning K. Luo et al.
48. Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes L. Pipia et al.
49. A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data J. Dong et al.
50. Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms M. Castelli
51. Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits T. Musavi et al.
52. 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.
53. Development of Spatio-Temporal Gap-Filling Technique for NDVI Images S. Kim et al.
54. Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options M. Meroni et al.
55. 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.
56. Adaptive Gap-Filling of Multispectral Images at Coarse and Fine Spatial Resolution S. Afsharipour et al.
57. A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation B. Qiu et al.
58. 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.
59. Using MODIS Data to Predict Regional Corn Yields H. Ban et al.
60. A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data C. Santana et al.
61. Observational uncertainties on past and future sea level rise for Marseille and Brest tide gauges J. Chenal
62. Spatiotemporal Vegetation Dynamics, Forest Loss, and Recovery: Multidecadal Analysis of the U.S. Triple Crown National Scenic Trail Network A. Ignatius et al.
63. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques S. Li et al.
64. Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison H. Xu et al.
65. Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion Y. Wang et al.
66. Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea S. Jeong et al.
67. Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data Y. Ma et al.
68. Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine L. Pan et al.
69. The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps X. Qi et al.
70. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity L. Kooistra et al.
71. 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.
72. Status of Phenological Research Using Sentinel-2 Data: A Review G. Misra et al.
73. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al.
74. An evapotranspiration model driven by remote sensing data for assessing groundwater resource in karst watershed C. Ollivier et al.
75. Reconstructing the Seasonality and Trend in Global Leaf Area Index During 2001–2017 for Prognostic Modeling M. Wang et al.
76. 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.
77. Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires A. McFadden et al.
78. Mapping global leaf inclination angle (LIA) based on field measurement data S. Li & H. Fang
79. In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model M. Mao et al.
80. A New Flexible Approach for Reconstructing Satellite-Based Land Surface Temperature Images: A Case Study With MODIS Data S. Afsharipour et al.
81. Wall-to-wall mapping of tree extent in the tropics with Sentinel-1 and Sentinel-2 J. Brandt et al.
82. Discussion of the paper “analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan” O. Nicolis & J. Mateu
83. Local species assemblages are influenced more by past than current dissimilarities in photosynthetic activity M. Jung et al.
84. Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa H. De Lemos et al.
85. Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data C. Doherty & M. Mauter
86. Forecasting PM10 Concentrations Using Artificial Neural Network in Imphal City N. Premananda Singh & R. Laishram
87. Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework M. Wang et al.
88. Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence–Sequence Model T. Peng et al.
89. Data gaps and outliers distort critical-slowing-down-based resilience indicators T. Liu et al.
90. Assessment of Three Methods for Near Real-Time Estimation of Leaf Area Index From AVHRR Data S. Kandasamy et al.
91. Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US L. Zhong et al.
92. Remote sensing for agricultural applications: A meta-review M. Weiss et al.
93. Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS) J. Zhou et al.
94. Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow T. Vandal & R. Nemani
95. Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series N. Karasiak et al.
96. Grassland Yield Estimation Using Transfer Learning from Remote Sensing Data E. Eder et al.
97. Usable observations over Europe: evaluation of compositing windows for Landsat and Sentinel-2 time series K. Lewińska et al.
98. Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau N. Li et al.
99. Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover M. Kalumba et al.
100. Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment L. Sun & K. Schulz
101. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL N. Khanal et al.
102. Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series A. Diouf et al.
103. Influences of noise on paddy chlorophyll mapping and associated uncertainty across heterogeneous landscapes using Sentinel-2 and hybrid models G. Thao et al.
104. 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.
105. Abrupt transitions in ecosystem service interactions driven by climate variability and human activities across the Loess Plateau, China P. Dong et al.
106. Fine-scale classification of horticultural crops using Sentinel-2 time-series images in Linyi country, China R. Chen et al.
107. From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Y. Hamrouni et al.
108. Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION A. Verger et al.
109. Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers Y. Yuan et al.
110. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection S. Belda et al.
111. The Value of L-Band Soil Moisture and Vegetation Optical Depth Estimates in the Prediction of Vegetation Phenology B. Li et al.
112. Exploring denoising diffusion probabilistic model for daily streamflow gap filling in Central Asia typical watersheds G. He et al.
113. Unraveling the Multiple Drivers of Greening-Browning and Leaf Area Variability in a Socioeconomically Sensitive Drought-Prone Region K. Bageshree et al.
114. Recurrent-based regression of Sentinel time series for continuous vegetation monitoring A. Garioud et al.
115. Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria M. Dhillon et al.
116. A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations C. Xing et al.
117. Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images J. Celis et al.
118. A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects C. Atzberger et al.
119. ICIS: Intelligent and Label-Free crop identifying using Multi-Source time series and model transfer S. Rina et al.
120. Spatio-Temporal Gap Filling of Sentinel-2 NDI45 Data Using a Variance-Weighted Kalman Filter and LSTM Ensemble I. Haidu et al.
121. Deep learning based multi-temporal crop classification L. Zhong et al.
122. Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data Z. Cai et al.
123. An open software package for data reconciliation and gap filling in preparation of Water and Resource Recovery Facility Modeling C. De Mulder et al.
124. Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data W. Yu et al.
125. Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series A. Gnilke & T. Sanders
126. 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
127. Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data R. Houborg & M. McCabe
128. Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests K. Bórnez et al.
129. Evaluation of Air and Soil Temperatures for Determining the Onset of Growing Season R. Leeper et al.
130. Influence of Pixel Quality, Land Cover, and Hydroclimatic Cycle on Moderate Resolution Imaging Spectroradiometer Inundation Monitoring Performance in the Pantanal, Brazil S. Silveira et al.
131. Evaluating Visible–Infrared Imaging Radiometer Suite Imagery for Developing Near-Real-Time Nationwide Vegetation Cover Monitoring in Indonesia Y. Setiawan et al.
132. A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery J. Brandt & F. Stolle
133. Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series E. Perry et al.
134. Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data M. Kahiu & N. Hanan
135. Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea C. Lim et al.
136. 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.
137. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands C. Ding et al.
138. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products S. Mayr et al.
139. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources S. Salcedo-Sanz et al.
140. 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.
141. Advancing UAV-based wheat phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation Z. Feng et al.
142. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine L. Pan et al.
143. On the performance of remote sensing time series reconstruction methods – A spatial comparison J. Zhou et al.
144. Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al.
145. Not the LAAST word, a response to Liebl's critique T. Niiler
146. Evapotranspiration Estimation Using Surface Energy Balance System Model: A Case Study in the Nagqu River Basin L. Zhong et al.
147. Modelling asynchrony in phenology considering a dynamic representation of meteorological variables R. de la Torre Cerro et al.
148. GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010 A. Verger et al.
149. Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information H. Zhao et al.
150. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al.
151. Spectral indices in remote sensing of soil: definition, popularity, and issues. A critical overview Q. Chen et al.
152. Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species J. Gonçalves et al.
153. Remotely sensed soil moisture to estimate savannah NDVI N. Boke-Olén et al.
154. Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine Y. Guo et al.
155. Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis M. Zempila et al.
156. Annual Sea Level Amplitude Analysis over the North Pacific Ocean Coast by Ensemble Empirical Mode Decomposition Method W. Lan et al.
157. Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale M. Cheng et al.
158. A decade-long seamless-continuity daily L-band soil moisture product derived from SMOS observations since 2010 Y. Bai et al.
159. Plant phenology evaluation of CRESCENDO land surface models using satellite-derived Leaf Area Index – Part 2: Seasonal trough, peak, and amplitude D. Peano et al.
160. A framework to identify winter fallow arable lands through machine learning methods D. Li et al.
161. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery X. Zhang et al.
162. 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.
163. 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
164. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring S. Mondal et al.
165. Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm X. Guo et al.
166. Monitoring mangrove traits through optical Earth observation: Towards spatio-temporal scalability using cloud-based Sentinel-2 continuous time series N. An Binh et al.
167. 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.
168. Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia H. Zandler et al.
169. Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity H. Noorazar et al.
170. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season D. Peano et al.
171. 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.
172. Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series Z. Tang et al.
173. Robust County-Level Corn Yield Estimation Using Ensemble Machine Learning and Multisource Remote Sensing A. Vafaeinejad et al.
174. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series X. Zhu et al.
175. HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023 K. Yan et al.
176. Leveraging browse and grazing forage estimates to optimize index-based livestock insurance N. Kahiu et al.
177. Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record K. Lewińska et al.
178. Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation M. Hunt et al.
179. Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks H. Ren et al.
180. 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.
181. Downscaling Method for Crop Yield Statistical Data Based on the Standardized Deviation from the Mean of the Comprehensive Crop Condition Index K. Luo et al.
182. Robust reconstruction of seamless daily VIIRS nighttime light imagery with cloud mask refinement and multi-strategy spatiotemporal gap-filling Q. Zheng et al.
183. Divergent responses of canopy structure and productivity to drought and their driving mechanisms in northern China’s grasslands M. Huang et al.