Ullah, SSkidmore, AKRamoelo, AbelGroen, TANaeem, MAli, A2014-07-182014-07-182014-05Ullah, S, Skidmore, A.K, Ramoelo, A, Groen, T.A, Naeem, M and Ali, A. 2014. Retrieval of leaf water content spanning the visible to thermal infrared spectra. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp 56-640924-2716http://ac.els-cdn.com/S0924271614000926/1-s2.0-S0924271614000926-main.pdf?_tid=49360422-06a5-11e4-8783-00000aacb35d&acdnat=1404826949_a3a5389467b15d18c2d97d38bcf24126http://hdl.handle.net/10204/7512Copyright: 2014 Elsevier. This is the pre print version of the work. The definitive version is published in ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp 56-64The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390 µm -14.0 µm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R(sup2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR-SWIR), the most accurate spectral index yielded R(sup2) of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R(sup2) was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R(sup2)=0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R(sup2)=0.96, RMSE=4.74% and RMSE(subCV)=6.17%) followed by VNIR-SWIR reflectance spectra (R(sup2)=0.91, RMSE=6.90% and RMSE(subCV)=7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R(sup2)=0.67, RMSE=13.27% and RMSE(subCV)=16.39%). This study demonstrated that the MIR and SWIR domains were the most sensitive spectral region for the retrieval of leaf water content.enWater stressStatistical modelsLeaf water contentRemote sensingArticleUllah, S., Skidmore, A., Ramoelo, A., Groen, T., Naeem, M., & Ali, A. (2014). Retrieval of leaf water content spanning the visible to thermal infrared spectra. http://hdl.handle.net/10204/7512Ullah, S, AK Skidmore, Abel Ramoelo, TA Groen, M Naeem, and A Ali "Retrieval of leaf water content spanning the visible to thermal infrared spectra." (2014) http://hdl.handle.net/10204/7512Ullah S, Skidmore A, Ramoelo A, Groen T, Naeem M, Ali A. Retrieval of leaf water content spanning the visible to thermal infrared spectra. 2014; http://hdl.handle.net/10204/7512.TY - Article AU - Ullah, S AU - Skidmore, AK AU - Ramoelo, Abel AU - Groen, TA AU - Naeem, M AU - Ali, A AB - The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390 µm -14.0 µm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R(sup2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR-SWIR), the most accurate spectral index yielded R(sup2) of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R(sup2) was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R(sup2)=0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R(sup2)=0.96, RMSE=4.74% and RMSE(subCV)=6.17%) followed by VNIR-SWIR reflectance spectra (R(sup2)=0.91, RMSE=6.90% and RMSE(subCV)=7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R(sup2)=0.67, RMSE=13.27% and RMSE(subCV)=16.39%). This study demonstrated that the MIR and SWIR domains were the most sensitive spectral region for the retrieval of leaf water content. DA - 2014-05 DB - ResearchSpace DP - CSIR KW - Water stress KW - Statistical models KW - Leaf water content KW - Remote sensing LK - https://researchspace.csir.co.za PY - 2014 SM - 0924-2716 T1 - Retrieval of leaf water content spanning the visible to thermal infrared spectra TI - Retrieval of leaf water content spanning the visible to thermal infrared spectra UR - http://hdl.handle.net/10204/7512 ER -