Understanding TVDI as an index that expresses soil moisture

Lucimara Wolfarth Schirmbeck


Given the need to search for robust indicators capable of representing the surface water condition in the various agricultural production regions in the state of Rio Grande do Sul, the objective of this study was to analyze the influence of different types of vegetation cover on the definition of Temperature Vegetation Dryness Index (TVDI) as an indicator of soil moisture in agricultural areas on a local scale. A Landsat 8 - OLI image of February 7, 2015 and its Normalized Difference Vegetation Index (NDVI) product were used. The image was classified by the maximum likelihood method, after the surface temperature (TS) was obtained by the split-window algorithm, later the normalization of the TVDI model with the triangular characteristic dispersion was performed. With the data of TVDI, the different types of vegetation cover mapped were identified in the dispersion of the index. Histograms of TVDI frequencies were also made for each of the targets and the dispersion between the index and the NDVI and TS. The results showed that it was possible to differentiate the different types of soil use and cover, through the characteristic water condition of each target. With the dispersions of the targets it was possible to locate them with a certain overlap; the strong relation between the index and TS was observed. TVDI has been shown to be efficient for monitoring the variation of the water condition and can be used for monitoring and sustainable management purposes.


Landsat 8-OLI, supervised classification, split-window.

Full Text:



Carlson, T. N. 2007. An Overview of the "Triangle Method" for Estimating Surface

Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors, 7, 1612-1629.

Gao, Z., Gao, W., Chang, N. 2011. Integrating temperature vegetation dryness index

(TVDI) and regional water stress index (RWSI) for drought assessment with the aid of

LANDSAT TM/ETM+ images. International Journal of Applied Earth Observation

and Geoinformation, 13, 495-503.

Jensen, J. R. 2009. Sensoriamento Remoto do Ambiente: uma prespectiva em recursos terrestres. Tradução Epiphanio, J.C.N. (coodenador)... [et al.]. São José dos Campos.

Jiménez-Muñoz, J. C., Sobrino, J. A.; Skokovic, D., Mattar, C., Cristóbal, J. 2014. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11,1840-1843.

Wang, K., Li, Z., Cribb, M. 2006. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sensing of Environment, 102, 293-305.

Liang, L., Zhao, S., Qin, Z., He, K., Chen, C., Luo, Y., & Zhou, X. 2014. Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010. Journal of Integrative Agriculture, 13, 1501-1508.

Matzenauer, R., Bergamaschi, H., Berlato, M. A., Maluf, J. R. T., Barni, N. A., Bueno, A. C., Didoné, I. A., Anjos, C. S., Machado, F. A., Sampaio, M. R. Consumo de água e disponibilidade hídrica para milho e soja no Rio Grande do Sul, Porto Alegre: FEPAGRO, 2002. (Boletim FEPAGRO, n. 10).

Mengue, V. P., Fontana, D. C. 2016. Identification of suitable areas for irrigated rice cropping using Modis images and Hand model. Engenharia Agrícola , 36, 329-341.

Nabinger, C., Ferreira, E. T., Freitas, A. K., Carvalho, P. C. F., Sant'anna, D. M. 2009. Produção animal em campo nativo: aplicações de resultados de pesquisa. in: Pillar, V. P., Müller, S. C., Castilhos, Z. M. S., Jacques, A.V.A. (Org.). Campos sulinos: conservação e uso sustentável da biodiversidade. Brasília: Ministério do Meio Ambiente - MMA, pp 175-198.

Price, J. C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948.

Sandholt, I., Rasmusen, K., Andersen, J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Enviromenmt, 79, 213-224.

Schirmbeck, J., Rivas, R. 2007. Estimación de la radiación neta a nível del suelo a partir de datos captados por el sensor ETM+. in: XIII Simpósio Brasileiro de Sensoriamento Remoto. Anais... Florianópolis: SBSR,. p. 6159-6165.

Sentelhas, P. C., Battisti, R., Câmara, G. M. S., Farias, J. R. B., Hampf, A. C.,

Nendel, C. 2015. The soybean yield gap in Brazil: Magnitude, causes and possible solutions for sustainable production. Journal of Agricultural Science 153, 1-18.

Sobrino, J., Jimenez Muñoz. J. C., Paolini, L. 2002. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434-440.

Son, N. T., Chen, C. F., Chen, C. R., Chang, L.Y., Minh, V. Q. 2012. Monitoring agricultural drought in the Lower Mekong Basin

using MODIS NDVI and land surface temperature data. International Journal of

Applied Earth Observation and Geoinformation, 18, 417-427.

USGS (United States Geological Survey), ESPA (Center Science Processing Architecture). Availabe: https://espa.cr.usgs.gov. Access: apr, 20, 2017.

Yu, X., Guo, X., Wu, Z. 2014. Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sensing, 6, 9829-9852,

Zanon, A. J., Streck, N. A., Grassini, P. 2016. Climate and Management Factors Influence Soybean Yield Potential in a Subtropical Environment. Agronomy Journal, 108, 1447-1454.

DOI: https://doi.org/10.29150/jhrs.v7.2.p82-90

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Indexadores / Base de Dados:


Google Scholar


Journal of Hyperspectral Remote Sensing - eISSN: 2237-2202