Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T : The Cook Agronomy Farm data set

The paper describes a framework for modeling dynamic soil properties in 3-dimensions and time (3D + T) using soil data collected with automated sensor networks as a case study. Two approaches to geostatistical modeling and spatio-temporal predictions are described: (1) 3D + T predictive modeling using random forests algorithms, and (2) 3D + T kriging model after detrending the observations for depth-dependent seasonal effects. All the analyses used data from the Cook Agronomy Farm (37 ha), which includes hourly measurements of soil volumetric water content, temperature, and bulk electrical conductivity at 42 stations and five depths (0.3, 0.6, 0.9, 1.2, and 1.5 m), collected over five years. This data set also includes 2- and 3-dimensional, temporal, and spatio-temporal covariates covering the same area. The results of (strict) leave-one-station-out cross-validation indicate that both models accurately predicted soil temperature, while predictive power was lower for water content, and lowest for electrical conductivity. The kriging model explained 37%, 96%, and 18% of the variability in water content, temperature, and electrical conductivity respectively versus 34%, 93%, and 5% explained by the random forests model. A less rigorous simple cross-validation of the random forests model indicated improved predictive power when at least some data were available for each station, explaining 86%, 97%, and 88% of the variability in water content, temperature, and electrical conductivity respectively. The high difference between the strict and simple cross-validation indicates high temporal auto-correlation of values at measurement stations. Temporal model components (i.e. day of the year and seasonal trends) explained most of the variability in observations in both models for all three variables. The seamless predictions of 3D + T data produced from this analysis can assist in understanding soil processes and how they change through a season, under different land management scenarios, and how they relate to other environmental processes.</p

The Tasseled Cap Transformation for RapidEye data and the estimation of vital and senescent crop parameters

The retrieval of crop biophysical parameters using spectral indices obtained from high temporal and spatial resolution satellite data, is a valuable tool to monitor crop growth and status. Tasseled Cap Features (TCFs) for RapidEye data were derived from spectral variances typically present in agricultural scenes. The TCF Greenness (GRE) was aligned to the spectral variance of vital vegetation, and therefore, it represents the typical reflectance characteristics of green vegetation, with relatively higher reflectance at the nearinfrared (NIR) range. The TCF Yellowness (YEL) was aligned to correspond to the reflectance characteristics of senescent crops, with relatively higher reflectance in the visible portion of the spectrum due to chlorophyll breakdown, and lower reflectance in the NIR range due to cell structure decomposition compared to vital green vegetation. The goal of this work was to assess the potential of RapidEye’s TCFs for the prediction of green leaf area index (LAI), plant chlorophyll (Chl), and nitrogen (N) concentration, as well as the identification of senescence patterns. The linear relationships between the biophysical parameters and the TCFs were compared to the performance of the widely used indices NDVI and PSRI. Preliminary results indicate that GRE is strongly related to LAI in vital crops and suggests a higher predictive power than NDVI. YEL demonstrated a strong linear relation and a higher potential to estimate Chl and N concentration in senescent soft white winter wheat (Triticum aestivum L.) in comparison to PSRI. PSRI showed a stronger correlation to Chl in senescent soft white spring wheat (Triticum aestivum L.), compared to YEL. Results indicate that YEL may be used to characterize the variability in senescence status within fields. This information, in conjunction with soil fertility and yield maps, can potentially be used to designate precision management zones