Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index

Holzman, Mauro; Rivas, Raúl Eduardo; Piccolo, María Cintia

doi:10.1016/j.jag.2013.12.006

Cited by 235 publications

(169 citation statements)

References 82 publications

(139 reference statements)

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“…It is noteworthy that using the TVDI normalized for each image is more common in the literature (Gao et al 2011;Holzman et al 2014;Liang et al 2014). Moreover, this is critical to the use of TVDI for crop monitoring purposes and can sometimes be misinterpreted.…”

Section: Resultsmentioning

confidence: 99%

“…The Temperature-Vegetation Dryness Index (TVDI) has been used to determine soil moisture and water availability on a regional scale (Sandholt et al 2002;Gao et al 2011;Holzman et al 2014). It is potentially an important monitoring tool for agriculture.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between the surface temperature (T S ) and vegetation index (VI) often generates a triangular or trapezoidal shaped dispersion (Price 1990;Carlson et al 1994). The slope of the linear regression between T S and VI represents the degree of stress, given the negative relationship between these 2 parameters (Nemani and Running 1989;Carlson et al 1995;Moran et al 1996;Fensholt and Sandholt 2003;Venturini et al 2004;Wang et al 2007;Holzman et al 2014). The trapezoid or triangle shaped dispersion depends on the availability of the water content in the soil.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have using the TVDI determined from the MODIS sensor images (Chen et al 2011;Son et al 2012;Holzman et al 2014). 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.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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 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 T Smin parameters used to derive it can elucidate the patterns of crop response to water availability.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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“…This index is based on the relationship between VI and T S and the model allows parameterizing the vegetation water status between wet and dry limits of the evaporative triangle. TVDI has been applied to crop monitoring in several regions (Wang et al, 2006;Gao et al, 2011;Holzman et al, 2014;Liang et al, 2014), but it is not yet tested in large grain-producing areas in southern Brazil. Moreover, in most studies, the type of parameterization applied to TVDI and the consequences of using different approaches are not sufficiently explained.…”

Section: Introductionmentioning

confidence: 99%

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Schirmbeck

Fontana

Schirmbeck

2018

Sci. agric. (Piracicaba, Braz.)

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Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

et al. 2018

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During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index

Cited by 235 publications

References 82 publications

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

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

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

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