Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (potential crop yield). Hence, the detection of soil moisture content (SMC) is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. A method to estimate SMC from optical and thermal spectral information of METEOSAT imagery based on thermal inertia (TI) is presented. Minimum and maximum TI values from time series are combined in the Soil Moisture Saturation Index (SMSI). To convert surface to soil profile values, a Markov type filter is used, based on a simple two layer water balance equation (the surface layer and the reservoir below) and an autocorrelation function. Ten-daily SMC values are compared with up-scaled (using AVHRR/NDVI) observations on 10 EUROFLUX sites in Europe for the 1997 growing season (March-October). Moreover, the thermal inertia approach is compared for 1997, with ERS Scatterometer data for eight EUROFLUX sites. METEOSAT pixels are up-scaled to accommodate the ERS Scatterometer spatial resolution. The regression coefficients (slope, intercept and R²) of the thermal inertia approach versus the up-scaled soil moisture observations from EUROFLUX sites vary between 0.811-1.148, − 0.0029-0.66 and 0.544-0.877, respectively, with a RRMSE range of 3.9% to 35.7%. The regression coefficients of the comparison of ERS Scatterometer derived Soil Water Index (SWI) versus the up-scaled Soil Moisture Saturation Index for the pooled case (binning the eight EUROFLUX sites) are 0.587, 0.105 and 0.441, respectively, with a RRMSE of 38%. A simple error propagation model applied for the thermal inertia approach reveals that the absolute and relative errors of the obtained soil moisture content is at least 0.010 m³ m^− 3 or 2.0% with a SMC of 0.203 m³ m^− 3. Recommendations are made to test and implement the TI methodology using NOAA/AVHRR imagery.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Hyperspectral indices to diagnose leaf biotic stress of apple plants,considering leaf phenology

Novel and existing hyperspectral vegetation indices were evaluated in this study, with the aim of assessing their utility for accurate tracking of leaf spectral changes due to differences in biophysical indicators caused by apple scab. Novel indices were extracted from spectral profiles by means of narrow‐waveband ratioing of all possible two‐band combinations between 350 nm and 2500 nm at nanometer intervals (2 311 250 combinations) and all possible two‐band derivative combinations. Narrow‐waveband ratios consisting of wavelengths of approximately 1500 nm and 2250 nm, associated with water content, have proven to be the most appropriate for detecting apple scab at early developmental stages. Logistic regression c‐values ranged from 0.80 to 0.88. At a more developed infection stage, vegetation indices such as R₄₄₀/R₆₉₀ and R₆₉₅/R₇₆₀ exhibited superior distinction between non‐infected and infected leaves. Identified derivative indices were located in similar regions. It therefore was concluded that the most appropriate indices at early stages of infection are ratios of wavelengths situated at the water band slopes. The choice of appropriate indices and their discriminatory performances, however, depended on the phenological stage of the leaves. Hence, an undisturbed 20‐day growth profile was examined to assess the effect of physiological changes on spectral variations at consecutive growth stages of leaves. Results suggested that an accurate distinction could be made between different leaf developmental stages using the 570 nm, 1460 nm, 1940 nm and 2400 nm wavelengths, and the red‐edge inflection point. These results are useful to crop managers interested in an early warning system to aid proactive system management and steering.     

Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderate–high R ²?=?0.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI–dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was nonlinear, while the GeoCBI–dNDVI (differenced NDVI) relationship appeared linear.     

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.     

On what we cannot learn from proximity data.

Different issues raised in the commentaries are elaborated in this reply. The focus was on the following five topics: (a) the nature of storage deficits in the patient groups and the different meanings that can be attached to it, (b) the possible explanations for the disordered responses of the patients, (c) suggestions for the use of other techniques to study semantic deficits, (d) the Warrington and Shallice criteria to distinguish between access and storage problems, and (e) some technical (statistical) issues raised in the commentaries. The reply concludes by highlighting the key findings reported in the target article that seem to be accepted by the large majority of the commentators. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The deficiency of the ‘economic optimum’ application for evaluating models which predict crop yield response to nitrogen fertiliser

Much effort is devoted to testing N-fertiliser models against measurements of yield in N-fertiliser experiments. We show that the economic optimum application of N-fertiliser can often only be determined very imprecisely from such measurements, even when they are accurate. Hence any attempt to evaluate a simulation model by comparing simulated with experimentally determined optima, or to relate these experimental optima to other factors thought to influence yield, will be uninformative. Big differences between simulated and measured optima will occur even with good models, which may explain the apparent poor performance of some simulation models in the literature.We suggest, instead, the systematic inspection of the differences between simulated and observed yields at the levels of N-fertiliser applied in an experiment. A study of the relationship of these differences to the applied level of N-fertiliser and to other factors of interest will be a more informative method of evaluating a model, and of suggesting other factors for inclusion in it. An example of the use of the method is given.     

Pyrolysis of waste using a hybrid argon–water stabilized torch

An experimental plasmachemical reactor equipped with the novel IPP-ASCR hybrid gas–water stabilized DC torch (160 kW) has recently been started at IPP Prague for the innovative and environmentally friendly plasma treatment of waste streams with a view to their sustainable energetic and chemical valorization and to a reduction of the emission of greenhouse gases. Since the process energy is provided by direct heat transfer from plasma, gases of widely varying chemical composition may be used. The use of electrical energy also reduces the gas flows and requirements for exhaust-gas treatment, and offers control over the chemistry. Pyrolysis of biomass was experimentally studied using wood chips as a model substance. Syngas with a high content of hydrogen and CO was produced. The influence of adding CO₂ for increase of oxygen content in the reactor was investigated.     

Response of winter wheat to N-fertiliser: Dynamic model

A concise computer simulation model is described for calculating the growth and N-content of winter-wheat. The validity of the model was tested by means of a new application of statistical theory against the results of nationwide fertiliser experiments having different designs. There was agreement within the limits of experimental error between the measured and simulated values of both total plant and of grain dry weight over the entire range of N-fertiliser treatments in the different experiments. The model also gave good estimates of the %N in the grain provided N-fertilizer levels were not high. Response curves, calculated from the model for grain yield, grain %N, are given for different combinations of potential yield, mineralisation rate and the distribution of inorganic-N down the soil profile.     

Response of winter wheat to N-fertiliser: Quantitative relations for components of growth

Measurements were made of yield of dry matter, plant-N content, and the distribution of mineral-N down the soil profile in 10 fertiliser-N experiments. In one of them detailed measurements were made throughout growth. Rate of N-uptake by the crop was unaffected by the amount of mineral-N in the upper 90 cm of soil when it was above about 30 kg N ha^–1. The %N in plants that received ample N-fertiliser declined with increase in plant mass according to a previously derived equation. During senescence there was an apparent loss of N from the crop.N-nutrition in the different experiments had little effect on the partition of assimilate between grain and straw. At harvest grain and straw weights were well related by a linear model which had the same gradient but different intercepts for each experiment. Grain %N was about four times greater than straw %N. Regression analysis supported the view that high evaporative conditions or temperatures during the growing period induced earlier harvest dates, less grain relative to straw, and a higher %N in the plant when ample N-fertiliser was applied but not when N-fertiliser was withheld.Other analyses indicated that cereal roots were generally unable to extract mineral nitrogen from the soil when the concentration was less than about 0.18 kg N ha^–1 cm^–1, that at low levels of N-nutrition the recovery of available inorganic-N from soil by the grain and straw was about 80%, and that the average mineralisation rates from early spring to shortly after harvest date varied between 0.22 and 0.88 kg N ha^–1 d^–1 from site to site.