Spatial autocorrelation and the violation of the assumption of independency between observations in traditional statistical tests

Posted on 3 January 201917 March 2023 by CoxSunsetBeach

Spatial autocorrelation of agronomic and environmental variables Fig. 1 NDVI spatial pattern within a field In the fields of agronomy and environment, spatial observations generally exhibit some sort of spatial correlation, to a greater or lesser extent. Spatial data effectively share more similar characteristics with neighbouring observations than with other far apart. This concept has been Read more about Spatial autocorrelation and the violation of the assumption of independency between observations in traditional statistical tests[…]

Variogram and spatial autocorrelation

Posted on 3 January 20194 January 2022 by CoxSunsetBeach

Introducing the variogram It is nearly impossible to talk about the analysis of Precision Agriculture data without mentioning the variogram. Be aware that some people will refer to the term semi-variogram instead. There is some kind of confusion between these two terms (Bachmaier et Backes, 2008). Some authors talk about semi-variogram because of the factor 2 at the denominator Read more about Variogram and spatial autocorrelation[…]

Spatial data interpolation : TIN, IDW, kriging, block kriging, co-kriging…. What are the differences ?

Posted on 3 January 201917 March 2023 by CoxSunsetBeach

Interpolation is the process of mapping a variable at unsampled locations using a set of samples of known location and value (Fig. 1). These samples can come from a field campaign or can be the information measured by fixed or mobile sensors inside a field. More than often, in Precision Agriculture studies, one has a subset Read more about Spatial data interpolation : TIN, IDW, kriging, block kriging, co-kriging…. What are the differences ?[…]

Optimizing field sampling : smart sampling

Posted on 3 October 20184 January 2022 by CoxSunsetBeach

Field campaigns are a fundamental element to many precision agriculture studies. Indeed, sampling is necesary to calibrate a agronomic model or to evaluate the spatial structure of valuable and non accessible (or at least with difficulty) information via remote or proxy sensing (soil water status, leaf nitrogen content…) [Fig. 1]. The location in space of the Read more about Optimizing field sampling : smart sampling[…]