TVDI spatiotemporal pattern of a soybean growing area in humid subtropical climate

Lucimara Wolfarth Schirmbeck Denise Cybis Fontana Juliano Schirmbeck About the authors

ABSTRACT

The most southern Brazilian state, Rio Grande do Sul, is characterized by high crop yield and is, currently, the third leading soybean producer in the country. Therefore, agriculture is very important to the economy of the region. Because agriculture is highly dependent on variable weather parameters, the present study aimed to test the Temperature-Vegetation Dryness Index (TVDI) as a regional indicator of water status under the climate and soybean crop management conditions that predominate in northwestern Rio Grande do Sul. For this, soybean crop seasons with contrasting yields were selected: 2004 – 2005 (yield 0.5 t∙ha–1) and 2009 – 2010 (2.7 t∙ha–1). TVDI was obtained from the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI) images available from MODIS products, which were correlated to obtain a triangular scatter plot. Rainfall data from surface weather stations were also used. The results showed that the pattern of the TVDI is associated with rainfall variability. However, as the TVDI is based on normalization of the wet and dry edges of each image individually, the comparison of TVDI values across different images is challenging. This deficiency can be mitigated by analysis of the parameters used to derive the index. Therefore, both the TVDI and the “b” and TSmin parameters used to derive it can elucidate the patterns of crop response to water availability.

Key words
water status; NDVI; surface temperature

INTRODUCTION

The most southern state in Brasil, Rio Grande do Sul, is characterized by a significant grain production, and soybean crop, which takes place in the spring/summer period, occupies the largest area (IBGE 2016Instituto Brasileiro de Geografia e Estatística (2016). Levantamento sistemático da produção agrícola; [accessed 2016 Oct 04]. http://www.sidra.ibge.gov.br/
http://www.sidra.ibge.gov.br/...
). Despite the average annual increase of soybean production in the state, the harvest varies greatly among crop seasons. This high variability is especially determined by two main risk factors inherent to agricultural activity that are not controlled by the producer: market and climatic adversities.

Regarding to climatic adversities, several studies have shown that rainfall from December to March is the isolated weather element that has the greatest impact on soybean yield in Rio Grande do Sul (Berlato and Fontana 1999Berlato, M. A. and Fontana, D. C. (1999). Variabilidade interanual da precipitação pluvial e rendimento da soja no Estado do Rio Grande do Sul. Revista Brasileira de Agrometeorologia, 7, 119-125.; Fontana et al. 2001Fontana, D. C., Berlato, M. A., Lauschner, M. H. and Mello, R. W. (2001). Modelo de estimativa de rendimento de soja no Estado do Rio Grande do Sul. Pesquisa Agropecuária Brasileira, 36, 399-403. http://dx.doi.org/10.1590/S0100-204X2001000300001.
http://dx.doi.org/10.1590/S0100-204X2001...
). This results from the fact that, in a largely non-irrigated agriculture activity, rainfall is the only water supply to the system and, therefore, associated with water availability for the crop. Water restrictions, especially during blooming and grain filling, hinder crop development and yield (Zanon et al. 2016Zanon, A. J., Streck, N. A. and Grassini, P. (2016). Climate and Management Factors Influence Soybean Yield Potential in a Subtropical Environment. Agronomy Journal, 108, 1447-1454. http://dx.doi:10.2134/agronj2015.0535.
http://dx.doi:10.2134/agronj2015.0535...
).

The Temperature-Vegetation Dryness Index (TVDI) has been used to determine soil moisture and water availability on a regional scale (Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
; Gao et al. 2011Gao, Z., Gao, W. and 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. http://dx.doi.org/10.1016/j.jag.2010.10.005.
http://dx.doi.org/10.1016/j.jag.2010.10....
; Holzman et al. 2014Holzman, M. E., Rivas, R. E. and Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28, 181-192. http://dx.doi.org/10.1016/j.jag.2013.12.006.
http://dx.doi.org/10.1016/j.jag.2013.12....
). It is potentially an important monitoring tool for agriculture. The TVDI is calculated directly based on the synergy between surface temperature and vegetation index derived from satellite images and does not require measurements of surface weather elements. The relationship between the surface temperature (TS) and vegetation index (VI) often generates a triangular or trapezoidal shaped dispersion (Price 1990Price, J. C. (1990). Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948. http://dx.doi.org/10.1109/36.58983.
http://dx.doi.org/10.1109/36.58983...
; Carlson et al. 1994Carlson, T. N., Gillies, R. R. and Perry, E. M. (1994). A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover. Remote Sensing Reviews, 9, 161-173. http://dx.doi.org/10.1080/02757259409532220
http://dx.doi.org/10.1080/02757259409532...
). The slope of the linear regression between TS and VI represents the degree of stress, given the negative relationship between these 2 parameters (Nemani and Running 1989Nemani, R. R. and Running, S. W. (1989). Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data. Journal of Applied Meteorology, 28, 276-284. http://dx.doi.org/10.1175/1520-0450(1989)028%3C0276:EORSRT%3E2.0.CO;2.
http://dx.doi.org/10.1175/1520-0450(1989...
; Carlson et al. 1995Carlson, T. N., Gillies, R. R. and Schmugge, T. J. (1995). An interpretation of methodologies for indirect measurement of soil water content. Agricultural and Forest Meteorology, 77, 191-205. http://dx.doi.org/10.1016/0168-1923(95)02261-U.
http://dx.doi.org/10.1016/0168-1923(95)0...
; Moran et al. 1996Moran, M. S., Rahman, A. F. and Washburne, J. C. (1996). Combining the Penman–Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80, 87-109. http://dx.doi.org/10.1016/0168-1923(95)02292-9.
http://dx.doi.org/10.1016/0168-1923(95)0...
; Fensholt and Sandholt 2003Fensholt, R. and Sandholt, I. (2003). Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment. Remote Sensing of Environment, 87, 111-121. http://dx.doi.org/10.1016/j.rse.2003.07.002.
http://dx.doi.org/10.1016/j.rse.2003.07....
; Venturini et al. 2004Venturini, V., Bisht, G. and Islam, S. (2004). Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida. Remote Sensing of Enviromenmt, 93, 77-86. http://dx.doi.org/10.1016/j.rse.2004.06.020.
http://dx.doi.org/10.1016/j.rse.2004.06....
; Wang et al. 2007Wang, X., Xie, H. and Guan, H. (2007). Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 340, 12-24. http://dx.doi.org/10.1016/j.jhydrol.2007.03.022.
http://dx.doi.org/10.1016/j.jhydrol.2007...
; Holzman et al. 2014Holzman, M. E., Rivas, R. E. and Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28, 181-192. http://dx.doi.org/10.1016/j.jag.2013.12.006.
http://dx.doi.org/10.1016/j.jag.2013.12....
). The trapezoid or triangle shaped dispersion depends on the availability of the water content in the soil. The trapezoidal shape tends to occur more often in hot climates with high vegetation cover (Carlson 2007Carlson, T. N. (2007). An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors, 7, 1612-1629. http://dx.doi.org/10.3390/s7081612.
http://dx.doi.org/10.3390/s7081612...
).

The moderate resolution imaging spectroradiometer (MODIS) sensor, aboard the Terra platform, provides images of the surface temperature (MOD11A2) and vegetation index (MOD13A2) with temporal resolution of 8 and 16 days, respectively. These products enable monitoring crop development cycle over time, calculating TVDI, which can be used to define water availability. Both indicators are key to agricultural monitoring systems.

Several studies have using the TVDI determined from the MODIS sensor images (Chen et al. 2011Chen, J., Wang, C. Z., Jiang, H., Mao L. X. and Yu Z. R. (2011). Estimating soil moisture using temperature-vegetation dryness index (TVDI) in the Huang-huai-hai (HHH) plain. International Journal of Remote Sensing, 32, 1165-1177. http://dx.doi.org/10.1080/01431160903527421.
http://dx.doi.org/10.1080/01431160903527...
; Son et al. 2012Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y. and 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. http://dx.doi.org/10.1016/j.jag.2012.03.014.
http://dx.doi.org/10.1016/j.jag.2012.03....
; Holzman et al. 2014Holzman, M. E., Rivas, R. E. and Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28, 181-192. http://dx.doi.org/10.1016/j.jag.2013.12.006.
http://dx.doi.org/10.1016/j.jag.2013.12....
). The results have been used to plan and manage the water resources to mitigate impacts on agriculture, as well as for crop monitoring and forecasting. Sandholt et al. (2002)Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
proposed to use the Normalized Difference Vegetation Index (NDVI) to calculate the TVDI; and several subsequent studies have used the TS and NDVI relationship (Gao et al. 2011Gao, Z., Gao, W. and 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. http://dx.doi.org/10.1016/j.jag.2010.10.005.
http://dx.doi.org/10.1016/j.jag.2010.10....
; Chen et al. 2011Chen, J., Wang, C. Z., Jiang, H., Mao L. X. and Yu Z. R. (2011). Estimating soil moisture using temperature-vegetation dryness index (TVDI) in the Huang-huai-hai (HHH) plain. International Journal of Remote Sensing, 32, 1165-1177. http://dx.doi.org/10.1080/01431160903527421.
http://dx.doi.org/10.1080/01431160903527...
; Son et al. 2012Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y. and 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. http://dx.doi.org/10.1016/j.jag.2012.03.014.
http://dx.doi.org/10.1016/j.jag.2012.03....
).

This study aimed at testing the TVDI as an indicator of regional water availability, considering the climate and soybean crop management conditions that predominate in northwestern part of Rio Grande do Sul. The specific objectives were to characterize the shape of the TS and NDVI relationship as well as to analyze the sensitivity of the relationship parameters to calculate the TVDI and to determine water availability.

MATERIAL AND METHODS

The study area is located in the mid-northwestern Rio Grande do Sul state, which is characterized by large volume of grain production (Figure 1). Agriculture in this region is the base of the economy, with approximately 6.5 million ha of the total area according to the base map of IBGE (2010)Instituto Brasileiro de Geografia e Estatística (2010). Base de dados cartográfica; [accessed 2014 Sept 20]. ftp://geoftp.ibge.gov.br/malhas_digitais/municipio_2010/
ftp://geoftp.ibge.gov.br/malhas_digitais...
, including the municipalities with the highest yield in the state. The average grain yield was 2.6 million ha between 2000 and 2012, with an average yield of 2 t∙ha–1. Soybean is the main spring-summer crop in terms of cultivated area and economic return to farmers in this region.

Figure 1
Location of the study area and used meteorological stations. Rio Grande do Sul State, Brazil.

The study area is located in the northern half of the state, where the rainfall in the summer ranges from 350 to 550 mm, which on average is not enough to supply the water demand of soybean crops (Matzenauer et al. 2011Matzenauer, R., Radin, B. and Almeida, I. R. (2011). Atlas Climático: Rio Grande do Sul. Porto Alegre: Secretaria da Agricultura Pecuária e Agronegócio; Fundação Estadual de Pesquisa Agropecuária.). In the summer (December to March), the average temperatures (above 22 °C) are higher than in the winter (below 18 °C, but above –3 °C), from June to September. There is no dry season in the state, and rainfall is higher than 60 mm every month of the year (Figure 2). In consequence, the study area is classified as “Cfa”, according to Köppen (1948)Köppen, W. (1948). Climatologia. Mexico: Fondo de Cultura Economica. Land Processes Distributed Active Archive Center (2015). Banco de Imagens de Satélite; [accessed: 2015 Sept 20]. https://lpdaac. usgs.gov, with hot summers.

Figure 2
Normal monthly rainfall and monthly average temperature in Rio Grande do Sul, Brazil.

The TVDI was calculated (Equation 1) as proposed by Sandholt et al. (2002)Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
:

TVDI = (TS - TSmin)/(a + b × NDVI - TSmin)(1)

where: TS is the observed surface at the given pixel (K); TSmin is the minimum surface temperature (K), corresponding to wet edge of the evaporative triangle; “a” and “b” are linear and angular coefficients of the straight line representing the dry edge.

There were used TS and NDVI data originated from the MODIS, MOD11A2, and MOD13A2 products, with spatial resolution of 1,000 m corresponding to composition periods of 8 and 16 days, respectively. The data covering the study area were downloaded from the Land Processes database - Distributed Active Archive Center (LPDAAC 2015) product tile h13v11, which were transformed from the sinusoidal projection to WGS 84 geographic coordinates. The images were cut according to the boundaries of the study area, and a mask was applied to the soybean cultivation area of the equivalent crop.

The TS and NDVI images were correlated to obtain the TVDI (Figure 3), resulting in a dispersion pattern which can assume a triangular or trapezoidal shape, depending on the existing biomass and soil moisture content (Price 1990Price, J. C. (1990). Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948. http://dx.doi.org/10.1109/36.58983.
http://dx.doi.org/10.1109/36.58983...
; Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
). The index is calculated from a normalized format that varies between 0 and 1 for each image. More distant dispersion points represent a very small percentage of the entire dataset and they may correspond to anomalous surfaces including water and clouds. These points, which are close to the dry or wet edges, were discarded from the analysis as recommended by Carlson (2007)Carlson, T. N. (2007). An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors, 7, 1612-1629. http://dx.doi.org/10.3390/s7081612.
http://dx.doi.org/10.3390/s7081612...
.

Figure 3
Evaporative triangle.

The TSmin was calculated by the average of the minimum temperatures, corresponding to the wet edge. The parameters “a” and “b” are the linear and angular coefficients of the straight line, respectively, and represent the dry edge. They were obtained from the NDVI and Ts scatterplot and used to normalize the model. In this study, TSmin and the parameters “a” and “b” were determined for each one of the 12 images that include the 2 analyzed crop seasons.

The 2004 – 2005 and 2009 – 2010 crop seasons with contrasting soybeans yields of 0.5 and 2.7 t∙ha–1, respectively,

were chosen for the analysis. The lower yields recorded in 2004 – 2005 resulted from lower rainfall and average temperatures slightly higher than those registered in January and February (Table 1), compared to the Climatological Normal and those observed in 2009 – 2010.

Table 1
Average air temperature and accumulated rainfall in January and February for the 2004 – 2005 and 2009 – 2010 harvests and climatological normal.

Only the soybean crops were analyzed, so the crop area was determined by the method that utilizes the difference between the NDVI at the maximum development minus NDVI at implantation, as proposed by Santos et al. (2014)Santos, J. S., Fontana, D. C., Silva, T. F. and Rudorff, B. F. T. (2014). Identificação da dinâmica espaço-temporal para estimar área cultivada de soja a partir de imagens MODIS no Rio Grande do Sul, Revista Brasileira de Engenharia Agrícola e Ambiental, 18, 54-63. http://dx.doi.org/10.1590/S1415-43662014000100008.
http://dx.doi.org/10.1590/S1415-43662014...
. To this end, the pixels that showed in the image differences greater than 0.30 and 0.39 for the 2004 – 2005 and 2009 – 2010 harvests, respectively, were considered soybeans. The criterion for determining this threshold was to obtain a farming area nearby a crop area divulged by the IBGE (2016)Instituto Brasileiro de Geografia e Estatística (2016). Levantamento sistemático da produção agrícola; [accessed 2016 Oct 04]. http://www.sidra.ibge.gov.br/
http://www.sidra.ibge.gov.br/...
for each crop.

The rainfall data were obtained from three weather stations (Figure 1), belonging to the Instituto Nacional de Meteorologia (INMET), located in major grain producer areas. Subsequently, the data were grouped in 16-day periods to become compatible with the timescale of MODIS images.

The TVDI and NDVI data were used to analyze the results originated for the 2 crop seasons, initially, extracted from the soybean growing areas in 5 × 5 pixel windows centered on the coordinates of the 3 weather stations, and posteriorly for the entire area mapped as soybean crop in northwestern Rio Grande do Sul.

Two images with different water conditions were chosen: one, with water deficit (2/18/2005) and another, in wet conditions (3/6/2010) to analyze the TVDI pattern, the index parameters, and rainfall. Finally, the NDVI, TVDI and “b” and TSmin parameters were correlated with rainfall by scatterplots.

RESULTS AND DISCUSSION

The dynamics of the soybean crop development in the study area can be evaluated looking at the pattern of NDVI over time (Figure 4). The observed pattern is typical of annual crops and consistent with what is expected for the region (Fontana et al. 2015Fontana, D. C., Pinto, D. G., Junges, A. H. and Bremm, C. (2015). Inferências sobre o calendário agrícola a partir de perfis temporais de NDVI/MODIS. Bragantia, 74, 350-358. http://dx.doi.org/10.1590/1678-4499.0439.
http://dx.doi.org/10.1590/1678-4499.0439...
), allowing to track the differences regarding the temporal evolution of the green biomass of soybean over the 2 crop seasons. The analysis of crop development against the critical periods of the cycle allows interpreting the responses of soybean plants to environmental conditions.

Figure 4
Temporal profile of the Temperature-Vegetation Dryness Index (TVDI) and the Normalized Difference Vegetation Index (NDVI) in northwestern Rio Grande do Sul and rainfall in the weather stations of Cruz Alta, São Luiz Gonzaga, and Passo Fundo, Brazil, in the 2004 – 2005 (a) and 2009 – 2010 (b) crops.

NDVI was low in November, associated with low green biomass coverage upon crop implantation (recommended sowing data is between October 11 and December 31, according Cunha et al. 2001Cunha, G. R., Barni, N. A., Haas, J. C., Maluf, J. R. T., Matzenauer, R., Pasinato, A., Pimentel, M. B. M. and Pires, J. L. F. (2001). Zoneamento agrícola e época de semeadura para soja no Rio Grande do Sul. Revista Brasileira de Agrometeorologia, 9, 446-459.). NDVI increased gradually as the biomass also increased up to the blooming stage, reducing again at the end of the cycle. These data are coherent since the average crop cicle is about 140 days (Thomas and Costa 2010Thomas, A. L. and Costa, J. A. (2010). Desenvolvimento da planta de soja e o potencial de rendimento de grãos. In: Evangraf. SOJA: manejo para alta produtividade de grãos. Porto Alegre: Evangraf.). The highest NDVI values were observed from January to March, associated with the higher density of crops green biomass. At blooming, soybeans reach maximum leaf area index, maximum capacity to intercept solar radiation, and photosynthesis, thus causing NDVI to peak. In the 2004 – 2005 and 2009 – 2010 crop seasons, the highest NDVI were 0.79 and 0.85, respectively, both in February, coinciding with blooming that usually occurs in February (Fontana et al. 2015Fontana, D. C., Pinto, D. G., Junges, A. H. and Bremm, C. (2015). Inferências sobre o calendário agrícola a partir de perfis temporais de NDVI/MODIS. Bragantia, 74, 350-358. http://dx.doi.org/10.1590/1678-4499.0439.
http://dx.doi.org/10.1590/1678-4499.0439...
). The variations observed between years indicate different conditions for crop growth and development and may be used for estimating yield models. The highest NDVI values in the 2009 – 2010 crop were consistent with the highest yield of 2.7 t∙ha–1 compared to 0.5 t∙ha–1 in the 2004 – 2005 crop.

On the other hand, the TVDI (Figure 4) showed a similar temporal pattern for both crops seasons. High values were observed at the beginning and end of the cycles, contrary to the NDVI pattern, which are partly due to the biomass capacity to control the evapotranspiration process. The values greater than 0.8 observed during these periods can be attributed to bare soil (beginning of cycle) and/or senescent vegetation (end of cycle) that cause lower evaporation rate and higher soil temperature (Wang et al. 2007Wang, X., Xie, H. and Guan, H. (2007). Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 340, 12-24. http://dx.doi.org/10.1016/j.jhydrol.2007.03.022.
http://dx.doi.org/10.1016/j.jhydrol.2007...
). On the other hand, TVDI decreased during the higher vegetative growth period (blooming and grain formation) which, associated with an increased transport of water by the plants into the atmosphere, indicated more appropriate water conditions, but with important differences between the 2 seasons.

The rainfall was higher in the 2009 – 2010 crop season (Figure 4b), which can influence the TVDI, especially during the period of higher vegetative growth. The index dropped more sharply in January in the 2009 – 2010 crop compared to the 2004 – 2005 one, reaching a minimum 0.45 and 0.52, respectively. The index increased rapidly in February in the 2004 – 2005 crop season (Figure 4a). This increase coincides with the period when large water deficit is observed more often in the region, as a result of increased atmospheric evaporative demand and higher water consumption by the plants (Matzenauer et al. 2002Matzenauer, 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. and Sampaio, M. R. (2002). Boletim FEPAGRO, Consumo de água e disponibilidade hídrica para milho e soja no Rio Grande do Sul, n. 10.; Mundstock and Thomas 2005Mundstock, C. and Thomas, A. L. (2005). Soja: Fatores que afetam o crescimento e o rendimento de grãos. Porto Alegre: Departamento de Plantas de Lavoura da Universidade Federal do Rio Grande do Sul; Evangraf.).

Therefore, TVDI responded to the biomass variability in the study area while the rainfall caused fluctuations of the TVDI profile along soybean development period. Nevertheless, the scatter-plot analysis between the cumulative rainfall and TVDI (Figure 5) presented high variability while TVDI did not display significant increasing trend with decreasing rainfall.

Figure 5
Dispersion between the Temperature-Vegetation Dryness Index (TVDI) and rainfall accumulated in 16 days in Cruz Alta, São Luiz Gonzaga, and Passo Fundo, Brazil.

This apparent inconsistency can be explained by the fact that the TVDI calculation is based on the normalization of wet and dry edges that are applied individually for each image, making the comparison of the index values between different images and/or crops more challenging.

It is noteworthy that using the TVDI normalized for each image is more common in the literature (Gao et al. 2011Gao, Z., Gao, W. and 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. http://dx.doi.org/10.1016/j.jag.2010.10.005.
http://dx.doi.org/10.1016/j.jag.2010.10....
; Holzman et al. 2014Holzman, M. E., Rivas, R. E. and Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28, 181-192. http://dx.doi.org/10.1016/j.jag.2013.12.006.
http://dx.doi.org/10.1016/j.jag.2013.12....
; Liang et al. 2014Liang, L., Zhao, S., Qin, Z., He, K., Chen, C., Luo, Y. and 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. http://dx.doi.org/10.1016/S2095-3119(14)60813-3.
http://dx.doi.org/10.1016/S2095-3119(14)...
). Moreover, this is critical to the use of TVDI for crop monitoring purposes and can sometimes be misinterpreted. This deficiency, however, can be minimized by analyzing the parameters that determine the dry and wet edges of the evaporative triangle, which are rather comparable between different images or crop seasons.

As an example, Figure 6 shows the triangles obtained in two different days, with low (2/18/2005) and high (3/6/2010) water availability. On 2/18/2005, the greater negative slope of the dry edge corresponds to higher temperature pixels for each NDVI (Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
). The NDVI/T S slope has been associated with the resistance of the stomata and evapotranspiration surface (Nemani and Running 1989Nemani, R. R. and Running, S. W. (1989). Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data. Journal of Applied Meteorology, 28, 276-284. http://dx.doi.org/10.1175/1520-0450(1989)028%3C0276:EORSRT%3E2.0.CO;2.
http://dx.doi.org/10.1175/1520-0450(1989...
). Steepening can be interpreted as a greater contrast between dry (nonirrigated, higher temperature) and wet (irrigated, lower temperature) areas.

Figure 6
Dispersion between the Normalized Difference Vegetation Index (NDVI) and surface temperature (TS) on 2/18/2005 (a) and 3/6/2010 (b) in northwestern Rio Grande do Sul, Brazil.

The straight line of the wet threshold corresponds to the number of cooler pixels, which is a result of increased water content (Carlson et al. 1994Carlson, T. N., Gillies, R. R. and Perry, E. M. (1994). A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover. Remote Sensing Reviews, 9, 161-173. http://dx.doi.org/10.1080/02757259409532220
http://dx.doi.org/10.1080/02757259409532...
; Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
). As expected, on 2/18/2005 (Figure 6a), the average minimum temperature was close to 24 °C, higher than that observed for more humid conditions (Figure 6b) of about 19 °C, demonstrating the relationship between water and thermal conditions.

The TVDI distribution in the study area for these 2 images can be seen in Figure 7. Red represents the highest indices worst water conditions, which predominate in much of the northwestern area of the state. It is important to emphasize that, when using normalization by image, it is possible to differentiate spatially different water conditions in the soy-producing region of the studied areas, but they should not be compared between dates and harvests.

Figure 7
Temperature-Vegetation Dryness Index (TVDI) on 2/18/2005 (a) and 3/6/2010 (b) in northwestern Rio Grande do Sul, Brazil.

The dispersion between rainfall and TSmin data and the slope of the straight-line “b” (Figure 8) shows a reverse trend, as TSmin decreases and “b” increases, rainfall increases as well. The increase of “b”, dry edge straight line with less negative slope, is due to the fact that for higher rainfall, the incidence of warmer pixels decreases especially for smaller vegetation cover (NDVI) (Carlson et al. 1994Carlson, T. N., Gillies, R. R. and Perry, E. M. (1994). A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover. Remote Sensing Reviews, 9, 161-173. http://dx.doi.org/10.1080/02757259409532220
http://dx.doi.org/10.1080/02757259409532...
; Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
). The lower TSmin results from the greater water availability in the soil, enabling the vegetation to control the temperature better (Sandholt et al. 2002Sandholt, I., Rasmusen, K. and 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. http://dx.doi.org/10.1016/S0034-4257(01)00274-7.
http://dx.doi.org/10.1016/S0034-4257(01)...
).

Figure 8
Dispersion between rainfall and “b” and TSmin parameters used to obtain the Temperature-Vegetation Dryness Index in the weatherstations of Cruz Alta, São Luiz Gonzaga, and Passo Fundo, Brazil.

It is noteworthy that, contrary to what is often seen in the literature, it is not appropriate to use the parameterized TVDI for image to compare water availability in different dates, an important limitation of the index. However, it is possible to minimize this limitation by using the TVDI calculated parameters. However, in this case, there is another important constraint, associated with loss of information related to the spatial variability of the index in the area to be monitored, which is certainly one of the main advantages of using satellite images.

CONCLUSION

In the rainy subtropical climate conditions that prevail in Rio Grande do Sul, the dispersion between TS and NDVI assumes a triangular shape (the evaporative triangle). The parameters defining this triangle, which are used to calculate the TVDI, are associated with water conditions prevalent in regions.

In each image or the date, the TVDI highlights the differences in water availability that occur in the assessed region, which are partly associated with the spatiotemporal variability of rainfall.

However, the TVDI is based on the parameterization of the wet and dry edges that are applied to each image individually, the comparison of index values between different dates or crop seasons becomes complex.

In the comparisons between dates or crops, it is recommended to use the information contained in the parameters “b” and TSmin, used to calculate the index, which can be indicators of water availability over time.

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Publication Dates

  • Publication in this collection
    Jul-Sep 2017

History

  • Received
    18 May 2016
  • Accepted
    08 Nov 2016
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