@Article{XuYHGMHHL:2022:EvNeTe,
               author = "Xu, Mangyuan and Yao, Ning and Hu, Annan and Gon{\c{c}}alves, 
                         Lu{\'{\i}}s Gustavo Gon{\c{c}}alves de and Mantovani, Felipe 
                         Abrah{\~a}o and Horgon, Robert and Heng, Lee and Liu, Gang",
          affiliation = "{China Agricultural University} and {Northwest Agriculture and 
                         Forestry University} and {University College London} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Lavras (UFLA)} and {Iowa State 
                         University} and {International Atomic Energy Agency} and {China 
                         Agricultural University}",
                title = "Evaluating a new temperature-vegetation-shortwave infrared 
                         reflectance dryness index (TVSDI) in the continental United 
                         States",
              journal = "Journal of Hydrology",
                 year = "2022",
               volume = "610",
                pages = "e127782",
                month = "July",
             keywords = "Continental United States, Dryness, Land surface temperature, 
                         Remote sensing, Shortwave infrared reflectance, Soil moisture, 
                         Vegetation index.",
             abstract = "Accurate dryness monitoring is important for formulating 
                         reasonable response measures to reduce social and economic losses 
                         caused by drought. The land surface temperature (LST), shortwave 
                         infrared (SWIR) reflectance, and vegetation index (VI) are popular 
                         remote sensing (RS) indices that can be individually used to 
                         characterize surface dryness. Given the interactions of these 
                         factors, limitations are inevitably associated with using a single 
                         factor. Integrated dryness indices that combine LST or SWIR 
                         reflectance with the VI have thus been successively proposed and 
                         applied for dryness monitoring and soil moisture (SM) retrieval 
                         work. However, the advantages of these three indicators have not 
                         yet been combined to construct a more comprehensive dryness index. 
                         In this study, we integrated the LST, enhanced vegetation index 
                         (EVI), and SWIR reflectance and developed an integrated 
                         satellite-based dryness index with simple calculations, called the 
                         temperature vegetation shortwave infrared reflectance dryness 
                         index (TVSDI). The proposed TVSDI was thoroughly assessed in the 
                         continental United States (CONUS) using the following data: the 
                         soil moisture active passive (SMAP) SM; six commonly used dryness 
                         indices (i.e., temperature vegetation soil moisture dryness index 
                         (TVMDI), temperature vegetation dryness index (TVDI), modified 
                         perpendicular dryness index (MPDI), perpendicular dryness index 
                         (PDI), standardized precipitation evapotranspiration index (SPEI), 
                         and standardized precipitation index (SPI)); in situ SM data 
                         collected from 24 Cosmic-ray neutron probe (CRNP) sites covering 
                         different climates, soil types, and land cover types; and the 
                         United States Drought Monitor (USDM) maps. The results 
                         demonstrated that the TVSDI was significantly correlated with SMAP 
                         SM (R = \− 0.75, p [removed] 60%. The evaluation based on 
                         in situ SM from 24 CRNP sites indicated that the TVSDI exhibited 
                         more stability and accuracy than other satellite-based 
                         agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). 
                         Moreover, the spatial patterns of TVSDI maps were not only 
                         well-matched with SMAP SM maps but also provided more detailed 
                         spatial information. TVSDI maps could capture more dryness and 
                         drought variations in natural ecosystems and areas with less 
                         intensive human activities than USDM maps. Furthermore, the 
                         application of the TVSDI for dryness monitoring in the CONUS 
                         revealed that the dryness distributions differed greatly across 
                         different geographic regions at monthly and annual scales. In 
                         conclusion, the TVSDI was found to be a reliable and accurate 
                         satellite-based dryness index.",
                  doi = "10.1016/j.jhydrol.2022.127785",
                  url = "http://dx.doi.org/10.1016/j.jhydrol.2022.127785",
                 issn = "0022-1694",
             language = "en",
           targetfile = "Xu_2022_evaluating.pdf",
        urlaccessdate = "29 jun. 2024"
}