The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

References

• Agam, N., W. P. Kustas, M. C. Anderson, F. Li, and P. D. Colaizzi. 2007a. “Utility of Thermal Sharpening over Texas High Plains Irrigated Agricultural Fields.” Journal of Geophysical Research: Atmospheres 112: D19. doi:10.1029/2007JD008407.
   Web of Science ®Google Scholar
• Agam, N., W. P. Kustas, M. C. Anderson, F. Li, and C. M. U. Neale. 2007b. “A Vegetation Index Based Technique for Spatial Sharpening of Thermal Imagery.” Remote Sensing of Environment 107 (4): 545–558. doi:10.1016/j.rse.2006.10.006.
   Web of Science ®Google Scholar
• Agam, N., W. P. Kustas, M. C. Anderson, F. Li, and P. D. Colaizzi. 2008. “Utility of Thermal Image Sharpening for Monitoring Field-scale Evapotranspiration over Rainfed and Irrigated Agricultural Regions.” Geophysical Research Letters 35: 2. doi:10.1029/2007GL032195.
   Web of Science ®Google Scholar
• Amazirh, A., O. Merlin, and S. Er-Raki. 2019. “Including Sentinel-1 Radar Data to Improve the Disaggregation of MODIS Land Surface Temperature Data.” ISPRS Journal of Photogrammetry and Remote Sensing 150: 11–26. doi:10.1016/j.isprsjprs.2019.02.004.
   Web of Science ®Google Scholar
• Anderson, M. C., R. G. Allen, A. Morse, and W. P. Kustas. 2012. “Use of Landsat Thermal Imagery in Monitoring Evapotranspiration and Managing Water Resources.” Remote Sensing of Environment 122: 50–65. doi:10.1016/j.rse.2011.08.025.
   Web of Science ®Google Scholar
• Bayala, M. I., and R. E. Rivas. 2014. “Enhanced Sharpening Procedures on Edge Difference and Water Stress Index Basis over Heterogeneous Landscape of Sub-humid Region.” The Egyptian Journal of Remote Sensing and Space Science 17 (1): 17–27. doi:10.1016/j.ejrs.2014.05.002.
   Google Scholar
• Bisquert, M., J. M. Sánchez, R. López-Urrea, and V. Caselles. 2016. “Estimating High Resolution Evapotranspiration from Disaggregated Thermal Images.” Remote Sensing of Environment 187: 423–433. doi:10.1016/j.rse.2016.10.049.
   Web of Science ®Google Scholar
• Bonafoni, S. 2016. “Downscaling of Landsat and MODIS Land Surface Temperature over the Heterogeneous Urban Area of Milan.” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal Of 9 (5): 2019–2027. doi:10.1109/JSTARS.2016.2514367.
   Web of Science ®Google Scholar
• Chen, X., L. Wentao, J. Chen, Y. Rao, and Y. Yamaguchi. 2014. “A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery.” Remote Sensing 6 (4): 2845–2863. doi:10.3390/rs6042845.
   Web of Science ®Google Scholar
• Djamai, N., R. Magagi, K. Goita, O. Merlin, Y. Kerr, and A. Walker. 2015. “Disaggregation of SMOS Soil Moisture over the Canadian Prairies.” Remote Sensing of Environment 170: 255–268. doi:10.1016/j.rse.2015.09.013.
   Web of Science ®Google Scholar
• Dominguez, A., J. Kleissl, J. C. Luvall, and D. L. Rickman. 2011. “High-resolution Urban Thermal Sharpener (HUTS).” Remote Sensing of Environment 115 (7): 1772–1780. doi:10.1016/j.rse.2011.03.008.
   Web of Science ®Google Scholar
• Duan, S. B., and Z. L. Li. 2016. “Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China.” IEEE Transactions on Geoscience and Remote Sensing 54 (11): 6458–6469. doi:10.1109/TGRS.2016.2585198.
   Web of Science ®Google Scholar
• Ebrahimy, H., and M. Azadbakht. 2019. “Downscaling MODIS Land Surface Temperature over a Heterogeneous Area: An Investigation of Machine Learning Techniques, Feature Selection, and Impacts of Mixed Pixels.” Computers & Geosciences 124: 93–102. doi:10.1016/j.cageo.2019.01.004.
   Web of Science ®Google Scholar
• Eswar, R., M. Sekhar, and B. K. Bhattacharya. 2016. “Disaggregation of LST over India: Comparative Analysis of Different Vegetation Indices.” International Journal of Remote Sensing 37 (5): 1035–1054. doi:10.1080/01431161.2016.1145363.
   Web of Science ®Google Scholar
• Gao, F., W. Kustas, and M. Anderson. 2012. “A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land.” Remote Sensing 4 (12): 3287–3319. doi:10.3390/rs4113287.
   Web of Science ®Google Scholar
• Gomis-Cebolla, J., J. C. Jimenez, and J. A. Sobrino. 2018. “LST Retrieval Algorithm Adapted to the Amazon Evergreen Forests Using MODIS Data.” Remote Sensing of Environment 204: 401–411. doi:10.1016/j.rse.2017.10.015.
   Web of Science ®Google Scholar
• Huang, C., X. Li, and L. Lu. 2008. “Retrieving Soil Temperature Profile by Assimilating MODIS LST Products with Ensemble Kalman Filter.” Remote Sensing of Environment 112 (4): 1320–1336. doi:10.1016/j.rse.2007.03.028.
   Web of Science ®Google Scholar
• Hutengs, C., and M. Vohland. 2016. “Downscaling Land Surface Temperatures at Regional Scales with Random Forest Regression.” Remote Sensing of Environment 178: 127–141. doi:10.1016/j.rse.2016.03.006.
   Web of Science ®Google Scholar
• Immerzeel, W. W., M. M. Rutten, and P. Droogers. 2009. “Spatial Downscaling of TRMM Precipitation Using Vegetative Response on the Iberian Peninsula.” Remote Sensing of Environment 113 (2): 362–370. doi:10.1016/j.rse.2008.10.004.
   Web of Science ®Google Scholar
• Jacobs, J. M., B. P. Mohanty, E.-C. Hsu, and D. Miller. 2004. “SMEX02: Field Scale Variability, Time Stability and Similarity of Soil Moisture.” Remote Sensing of Environment 92 (4): 436–446. doi:10.1016/j.rse.2004.02.017.
   Web of Science ®Google Scholar
• Kustas, W. P., and J. M. Norman. 2000. “A Two-Source Energy Balance Approach Using Directional Radiometric Temperature Observations for Sparse Canopy Covered Surfaces.” Agronomy Journal 92 (5): 847–854. doi:10.2134/agronj2000.925847x.
   Web of Science ®Google Scholar
• Kustas, W. P., J. M. Norman, M. C. Anderson, and A. N. French. 2003. “Estimating Subpixel Surface Temperatures and Energy Fluxes from the Vegetation Index–Radiometric Temperature Relationship.” Remote Sensing of Environment 85 (4): 429–440. doi:10.1016/S0034-4257(03)00036-1.
   Web of Science ®Google Scholar
• Li, F., T. J. Jackson, W. P. Kustas, T. J. Schmugge, A. N. French, M. H. Cosh, and R. Bindlish. 2004. “Deriving Land Surface Temperature from Landsat 5 and 7 during SMEX02/SMACEX.” Remote Sensing of Environment 92 (4): 521–534. doi:10.1016/j.rse.2004.02.018.
   Web of Science ®Google Scholar
• Li, X., C. Zhang, W. Li, R. O. Anyah, and J. Tian. 2019. “Exploring the Trend, Prediction and Driving Forces of Aerosols Using Satellite and Ground Data, and Implications for Climate Change Mitigation.” Journal of Cleaner Production 223: 238–251. doi:10.1016/j.jclepro.2019.03.121.
   Web of Science ®Google Scholar
• Li, Z.-L., B.-H. Tang, H. Wu, H. Ren, G. Yan, Z. Wan, I. F. Trigo, and J. A. Sobrino. 2013. “Satellite-derived Land Surface Temperature: Current Status and Perspectives.” Remote Sensing of Environment 131: 14–37. doi:10.1016/j.rse.2012.12.008.
   Web of Science ®Google Scholar
• Liu, K., H. Su, X. Li, S. Chen, R. Zhang, W. Wang, L. Yang, H. Liang, and Y. Yang. 2018a. “A Thermal Disaggregation Model Based on Trapezoid Interpolation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, no. 99: 1–13. doi:10.1109/JSTARS.2018.2790002.
   Web of Science ®Google Scholar
• Liu, K., H. Su, X. Li, S. Chen, R. Zhang, W. Wang, L. Yang, H. Liang, and Y. Yang. 2018b. “A Thermal Disaggregation Model Based on Trapezoid Interpolation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (3): 808–820. doi:10.1109/JSTARS.2018.2790002.
   Web of Science ®Google Scholar
• Liu, K., S. Hongbo, J. Tian, L. Xueke, W. Wang, L. Yang, and H. Liang. 2018. “Assessing a Scheme of Spatial-temporal Thermal Remote-sensing Sharpening for Estimating Regional Evapotranspiration.” International Journal of Remote Sensing 39 (10): 3111–3137. doi:10.1080/01431161.2018.1434326.
   Web of Science ®Google Scholar
• Liu, K., S. Hongbo, and L. Xueke. 2016. “Estimating High-Resolution Urban Surface Temperature Using a Hyperspectral Thermal Mixing (HTM) Approach.” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal Of 9 (4): 804–815. doi:10.1109/JSTARS.2015.2459375.
   Web of Science ®Google Scholar
• Long, D., and V. P. Singh. 2012. “A Two-source Trapezoid Model for Evapotranspiration (TTME) from Satellite Imagery.” Remote Sensing of Environment 121: 370–388. doi:10.1016/j.rse.2012.02.015.
   Web of Science ®Google Scholar
• Lopatin, J., K. Dolos, H. J. Hernández, M. Galleguillos, and F. E. Fassnacht. 2016. “Comparing Generalized Linear Models and Random Forest to Model Vascular Plant Species Richness Using LiDAR Data in a Natural Forest in Central Chile.” Remote Sensing of Environment 173: 200–210. doi:10.1016/j.rse.2015.11.029.
   Web of Science ®Google Scholar
• Merlin, O., B. Duchemin, O. Hagolle, F. Jacob, B. Coudert, G. Chehbouni, G. Dedieu, J. Garatuza, and Y. Kerr. 2010. “Disaggregation of MODIS Surface Temperature over an Agricultural Area Using a Time Series of Formosat-2 Images.” Remote Sensing of Environment 114 (11): 2500–2512. doi:10.1016/j.rse.2010.05.025.
   Web of Science ®Google Scholar
• Merlin, O., F. Jacob, J.-P. Wigneron, J. Walker, and G. Chehbouni. 2012. “Multidimensional Disaggregation of Land Surface Temperature Using High-resolution Red, Near-infrared, Shortwave-infrared, and microwave-L Bands.” IEEE Transactions on Geoscience and Remote Sensing 50 (5): 1864–1880. doi:10.1109/TGRS.2011.2169802.
   Web of Science ®Google Scholar
• Moosavi, V., A. Talebi, M. H. Mokhtari, S. R. F. Shamsi, and Y. Niazi. 2015. “A Wavelet-artificial Intelligence Fusion Approach (WAIFA) for Blending Landsat and MODIS Surface Temperature.” Remote Sensing of Environment 169: 243–254. doi:10.1016/j.rse.2015.08.015.
   Web of Science ®Google Scholar
• Norman, J. M., W. P. Kustas, and K. S. Humes. 1995. “Source Approach for Estimating Soil and Vegetation Energy Fluxes in Observations of Directional Radiometric Surface Temperature.” Agricultural and Forest Meteorology 77 (3): 263–293. doi:10.1016/0168-1923(95)02265-Y.
   Web of Science ®Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• Pan, X., X. Zhu, Y. Yang, C. Cao, X. Zhang, and L. Shan. 2018. “Applicability of Downscaling Land Surface Temperature by Using Normalized Difference Sand Index.” Scientific Reports 8 (1): 9530. doi:10.1038/s41598-018-27905-0.
   PubMed Web of Science ®Google Scholar
• Patel, N. R., R. Anapashsha, S. Kumar, S. K. Saha, and V. K. Dadhwal. 2009. “Assessing Potential of MODIS Derived Temperature/vegetation Condition Index (TVDI) to Infer Soil Moisture Status.” International Journal of Remote Sensing 30 (1): 23–39. doi:10.1080/01431160802108497.
   Web of Science ®Google Scholar
• Quan, J., W. Zhan, T. Ma, Y. Du, Z. Guo, and B. Qin. 2018. “An Integrated Model for Generating Hourly Landsat-like Land Surface Temperatures over Heterogeneous Landscapes.” Remote Sensing of Environment 206: 403–423. doi:10.1016/j.rse.2017.12.003.
   Web of Science ®Google Scholar
• Rahimzadeh-Bajgiran, P., K. Omasa, and Y. Shimizu. 2012. “Comparative Evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (itvdi) for Water Stress Detection in Semi-arid Regions of Iran.” ISPRS Journal of Photogrammetry and Remote Sensing 68: 1–12. doi:10.1016/j.isprsjprs.2011.10.009.
   Web of Science ®Google Scholar
• Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and P. Prabhat. 2019. “Deep Learning and Process Understanding for Data-driven Earth System Science.” Nature 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1.
   PubMed Web of Science ®Google Scholar
• Sandholt, I., K. Rasmussen, and J. Andersen. 2002. “A Simple Interpretation of the Surface Temperature/vegetation Index Space for Assessment of Surface Moisture Status.” Remote Sensing of Environment 79 (2): 213–224. doi:10.1016/S0034-4257(01)00274-7.
   Web of Science ®Google Scholar
• Semmens, K. A., M. C. Anderson, W. P. Kustas, F. Gao, J. G. Alfieri, L. McKee, J. H. Prueger, et al. 2016. “Monitoring Daily Evapotranspiration over Two California Vineyards Using Landsat 8 in a Multi-sensor Data Fusion Approach.” Remote Sensing of Environment 185 :155–170. doi:10.1016/j.rse.2015.10.025.
   Web of Science ®Google Scholar
• Shen, H., L. Huang, L. Zhang, P. Wu, and C. Zeng. 2016. “Long-term and Fine-scale Satellite Monitoring of the Urban Heat Island Effect by the Fusion of Multi-temporal and Multi-sensor Remote Sensed Data: A 26-year Case Study of the City of Wuhan in China.” Remote Sensing of Environment 172: 109–125. doi:10.1016/j.rse.2015.11.005.
   Web of Science ®Google Scholar
• Su, H., J. Tian, S. Chen, R. Zhang, Y. Rong, Y. Yang, X. Tang, and J. Garcia. 2011. “A New Algorithm to Automatically Determine the Boundary of the Scatter Plot in the Triangle Method for Evapotranspiration Retrieval.” Paper presented at the 2011 IEEE International Geoscience and Remote Sensing Symposium, Sendai, Japan, July 24–29.
   Google Scholar
• Tang, R., Z.-L. Li, and B. Tang. 2010. “An Application of the Ts–VI Triangle Method with Enhanced Edges Determination for Evapotranspiration Estimation from MODIS Data in Arid and Semi-arid Regions: Implementation and Validation.” Remote Sensing of Environment 114 (3): 540–551. doi:10.1016/j.rse.2009.10.012.
   Web of Science ®Google Scholar
• Tran, H., D. Uchihama, S. Ochi, and Y. Yasuoka. 2006. “Assessment with Satellite Data of the Urban Heat Island Effects in Asian Mega Cities.” International Journal of Applied Earth Observation and Geoinformation 8 (1): 34–48. doi:10.1016/j.jag.2005.05.003.
   Web of Science ®Google Scholar
• Weng, Q., P. Fu, and F. Gao. 2014. “Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data.” Remote Sensing of Environment 145: 55–67. doi:10.1016/j.rse.2014.02.003.
   Web of Science ®Google Scholar
• Wood, E. F., J. K. Roundy, T. J. Troy, L. P. H. van Beek, F. P. Marc, E. B. Bierkens, A. de Roo, et al. 2011. “Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth’s Terrestrial Water.” Water Resources Research 47 (5): n/a-n/a. doi:10.1029/2010WR010090.
   PubMed Web of Science ®Google Scholar
• Wood, S. N. 2003. “Thin Plate Regression Splines.” Journal of the Royal Statistical Society: Series B (statistical Methodology) 65 (1): 95–114. doi:10.1111/1467-9868.00374.
   Web of Science ®Google Scholar
• Wu, B., N. Yan, W. G. M. Jun Xiong, W. Z. Bastiaanssen, and A. Stein. 2012. “Validation of ETWatch Using Field Measurements at Diverse Landscapes: A Case Study in Hai Basin of China.” Journal of Hydrology 436–437: 67–80. doi:10.1016/j.jhydrol.2012.02.043.
   Web of Science ®Google Scholar
• Wu, H., and W. Li. 2019. “Downscaling Land Surface Temperatures Using a Random Forest Regression Model with Multitype Predictor Variables.” IEEE Access 7: 21904–21916. doi:10.1109/ACCESS.2019.2896241.
   Web of Science ®Google Scholar
• Wu, P., H. Shen, L. Zhang, and F.-M. Göttsche. 2015. “Integrated Fusion of Multi-scale Polar-orbiting and Geostationary Satellite Observations for the Mapping of High Spatial and Temporal Resolution Land Surface Temperature.” Remote Sensing of Environment 156: 169–181. doi:10.1016/j.rse.2014.09.013.
   Web of Science ®Google Scholar
• Xia, H., Y. Chen, Y. Li, and J. Quan. 2019. “Combining Kernel-driven and Fusion-based Methods to Generate Daily High-spatial-resolution Land Surface Temperatures.” Remote Sensing of Environment 224: 259–274. doi:10.1016/j.rse.2019.02.006.
   Web of Science ®Google Scholar
• Yang, Y., H. Su, R. Zhang, J. Tian, and L. Li. 2015. “An Enhanced Two-source Evapotranspiration Model for Land (ETEML): Algorithm and Evaluation.” Remote Sensing of Environment 168: 54–65. doi:10.1016/j.rse.2015.06.020.
   Web of Science ®Google Scholar
• Zhan, W., Y. Chen, J. Zhou, J. Wang, W. Liu, J. Voogt, X. Zhu, J. Quan, and J. Li. 2013. “Disaggregation of Remotely Sensed Land Surface Temperature: Literature Survey, Taxonomy, Issues, and Caveats.” Remote Sensing of Environment 131: 119–139. doi:10.1016/j.rse.2012.12.014.
   Web of Science ®Google Scholar
• Zhang, D., R. Tang, B.-H. Tang, H. Wu, and Z.-L. Li. 2015. “A Simple Method for Soil Moisture Determination from LST–VI Feature Space Using Nonlinear Interpolation Based on Thermal Infrared Remotely Sensed Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2): 638–648. doi:10.1109/JSTARS.2014.2371135.
   Web of Science ®Google Scholar
• Zhang, X., H. Zhao, and J. Yang. 2019. “Spatial Downscaling of Land Surface Temperature in Combination with TVDI and Elevation.” International Journal of Remote Sensing 40 (5–6): 1875–1886. doi:10.1080/01431161.2018.1489164.
   Web of Science ®Google Scholar
• Zhou, D., S. Zhao, S. Liu, L. Zhang, and C. Zhu. 2014. “Surface Urban Heat Island in China’s 32 Major Cities: Spatial Patterns and Drivers.” Remote Sensing of Environment 152: 51–61. doi:10.1016/j.rse.2014.05.017.
   Web of Science ®Google Scholar
• Zhu, W., S. Jia, and A. Lv. 2017. “A Time Domain Solution of the Modified Temperature Vegetation Dryness Index (MTVDI) for Continuous Soil Moisture Monitoring.” Remote Sensing of Environment 200: 1–17. doi:10.1016/j.rse.2017.07.032.
   Web of Science ®Google Scholar