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The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

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Early detection and quantification of plants' response to disease and water shortage conditions are very important for the agricultural management. This study represents the first utilization of Normalized Difference Latent Heat Index (NDLI) as a dimensionless indicator to assess plant health. By integrating NDLI with thermal infrared and surface energy balance (SEB) components, we aim to enhance the analysis of crop conditions and water scarcity in rice-growing areas. The integration between NDLI and land surface temperature exhibits a strong correlation (r = −0.82) with crop evapotranspiration (ET) derived from the widely used residual Surface Energy Balance Algorithm for Land model. Besides, the performance of NDLIand SEB-based ET method proved its ability to provide the precise information of paddy field conditions by showing the significant correlations with the crop canopy biophysical properties that are traditionally represented and inferred by the multispectral remote sensing indices. The correlation coefficients of NDLIand SEB-derived ET with Normalized Difference Vegetation Index (NDVI), Normalize Difference Water Index (NDWI), and Optimization of the Soil Adjusted Vegetation Index (OSAVI) were 0.84, 0.55, and 0.84, respectively. Also, NDLI-and SEB-derived ET exhibits a high degree of consistency with the ET determined through the SEBAL method, with difference less than 10% of the observations over 98.1% of the paddy fields of concern. Interestingly, the abnormally low ET signatures over the confirmed disease-infected regions of paddy fields are obviously observed in the NDLI-and SEB-derived ET maps, but not in the SEBAL-derived ET map. The findings of this work suggest that NDLI can be considered as a valuable indicator to provide information of the water stress status and health of the crop plants for advanced food-supply management.INDEX TERMS Normalized difference latent heat index (NDLI), land surface temperature (LST), evapotranspiration (ET), surface energy balance (SEB).