27 citations as recorded by crossref.

1. Remote sensing for agricultural applications: A meta-review M. Weiss et al. https://doi.org/10.1016/j.rse.2019.111402
2. Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach A. Amazirh et al. https://doi.org/10.3390/rs13163181
3. Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region L. Olivera-Guerra et al. https://doi.org/10.1016/j.rse.2019.111627
4. Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data L. Song et al. https://doi.org/10.3390/rs70505828
5. Spatiotemporal variations of albedo in managed agricultural landscapes: inferences to global warming impacts (GWI) P. Sciusco et al. https://doi.org/10.1007/s10980-020-01022-8
6. The MODIS (collection V006) BRDF/albedo product MCD43D: Temporal course evaluated over agricultural landscape M. Mira et al. https://doi.org/10.1016/j.rse.2015.09.021
7. Integrating thermal stress indexes within Shuttleworth–Wallace model for evapotranspiration mapping over a complex surface J. Elfarkh et al. https://doi.org/10.1007/s00271-020-00701-3
8. Evapotranspiration in Semi-Arid Climate: Remote Sensing vs. Soil Water Simulation H. Chakroun et al. https://doi.org/10.3390/s23052823
9. Combining Landsat optical/thermal and LiDAR High Definition data to estimate turbulent fluxes over Mediterranean forests V. Penot & O. Merlin https://doi.org/10.1016/j.srs.2025.100323
10. Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites H. Zhou et al. https://doi.org/10.3390/rs11050547
11. Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data L. Olivera-Guerra et al. https://doi.org/10.1016/j.agwat.2018.06.014
12. Estimating evapotranspiration in mountainous water-limited regions from thermal infrared data: Comparison of two approaches based on energy balance and evaporative fraction B. Sebbar et al. https://doi.org/10.1016/j.rse.2024.114481
13. Consistency between In Situ, Model-Derived and High-Resolution-Image-Based Soil Temperature Endmembers: Towards a Robust Data-Based Model for Multi-Resolution Monitoring of Crop Evapotranspiration V. Stefan et al. https://doi.org/10.3390/rs70810444
14. Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data A. Amazirh et al. https://doi.org/10.1016/j.isprsjprs.2019.02.004
15. Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach O. Merlin et al. https://doi.org/10.1002/2015WR018233
16. Modified Penman–Monteith equation for monitoring evapotranspiration of wheat crop: Relationship between the surface resistance and remotely sensed stress index A. Amazirh et al. https://doi.org/10.1016/j.biosystemseng.2017.09.015
17. Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil A. Amazirh et al. https://doi.org/10.1016/j.rse.2018.04.013
18. A phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data O. Merlin et al. https://doi.org/10.1016/j.agrformet.2018.04.010
19. Estimating the Water Deficit Index of a Mediterranean Holm Oak Forest From Landsat Optical/Thermal Data: A Phenomenological Correction for Trees Casting Shadow Effects V. Penot & O. Merlin https://doi.org/10.1109/JSTARS.2023.3288360
20. Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval S. Mi et al. https://doi.org/10.3390/rs70810856
21. Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco Y. Malbéteau et al. https://doi.org/10.1016/j.rse.2016.11.010
22. The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields M. Fontanet et al. https://doi.org/10.5194/hess-22-5889-2018
23. An image-based four-source surface energy balance model to estimate crop evapotranspiration from solar reflectance/thermal emission data (SEB-4S) O. Merlin et al. https://doi.org/10.1016/j.agrformet.2013.10.002
24. A moisture-based triangle approach for estimating surface evaporative fraction with time-series of remotely sensed data R. Tang et al. https://doi.org/10.1016/j.rse.2022.113212
25. Remote Sensing of Water Resources in Semi-Arid Mediterranean Areas: the joint international laboratory TREMA L. Jarlan et al. https://doi.org/10.1080/01431161.2015.1093198
26. Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas A. de Tomás et al. https://doi.org/10.1016/j.rse.2014.06.028
27. Estimation and evaluation of high spatial resolution surface soil moisture using multi-sensor multi-resolution approach S. Koley & C. Jeganathan https://doi.org/10.1016/j.geoderma.2020.114618