Input parameters selection for soil moisture retrieval using an artificial neural network

Factors other than soil moisture which influence the intensity of microwave emission from the soil include surface temperature, surface roughness, vegetation cover and soil texture which make this a non-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions to this type of problem. Since an ANN is a data driven model, proper input selection is a crucial step in its implementation as the presence of redundant or unnecessary inputs can severely impair the ability of the network to learn the target patterns. In this paper, the input parameters are chosen in combination with the brightness temperatures and are based on the use of incremental contributions of the variables towards soil moisture retrieval. Field experiment data obtained during the National Airborne Field Experiment 2005 (NAFE'05) are used. The retrieval accuracy with the input parameters selected is compared with the use of only brightness temperature as input and the use of brightness temperature in conjunction with a range of available parameters. Note that this research does not aim at selecting the best features for all ANN soil moisture retrieval problems using passive microwave. The paper shows that, depending on the problem and the nature of the data, some of the data available are redundant as the input of ANN for soil moisture retrieval. Importantly the results show that with the appropriate choice of inputs, the soil moisture retrieval accuracy of ANN can be significantly improved

Gjennomgang av presisjonslandbruk med bruk av droner for jordfuktighet estimering – Mot et mer bærekraftig landbruk

This master thesis is reviewing the latest published research on remote sensing technology in the agricultural sector, for soil moisture estimations towards a more sustainable precision agriculture. Modern, exciting new technological innovations will also be presented, along with the sustainable aspect of conventional agriculture with more precise agricultural practices. The synergy between UAS, SMC and sustainability are the focus of attention for this review thesis, as the possibilities and opportunities this can open for us can be of significant advancement in profitability and precision agriculture. As precision agriculture evolves and grows, the potential and opportunities also follow. The new field of unmanned aerial systems demonstrates this. There are several sectors the unmanned aerial vehicle is being welcomed with open arms, only within the agricultural sector, it has shown to be of great value for crop yield and biomass estimation. It takes little energy to run and operate and it can be from a green power source. As we all should move towards a more sustainable and eco-friendly lifestyle, industries, businesses and corporations are no exceptions. Agriculture is a major contributor to the climate change and environmental destruction, we should make a change to a more sustainable method of farming, with precision agriculture we are making this shift. The objective of this thesis is to contribute to the fundamental research for future implementation and introduction of remote sensing technology with a UAV. This thesis highlights these areas, to assist in closing the gap between researchers and endusers. By increasing the precision and applying inputs like artificial fertiliser and pesticides/herbicides at a correctly variable amount and time, a reduction of the inputs and the environmental disruption should follow, which results in an increase in the profitability for the farmers, and less environmental damages.Denne masteroppgaven gjennomgår den siste publiserte forskning av fjernmålings teknologi i landbrukssektoren, av jordfuktighets beregninger for ett mer bærekraftig presisjons jordbruk. Moderne spennende nye teknologiske utviklinger vil også bli presentert, sammen med det bærekraftig aspekt av konvensjonelt landbruk med mer nøyaktig jordbrukspraksis. Samarbeidet mellom UAS, SMC og bærekraft er i fokus i denne avhandlingen, som diskuterer mulighetene dette kan åpne for. Ved at presisjons jordbruk utvikler seg og vokser, følge også nye muligheter og metoder for utførelse av arbeidsoppgaver. Det nye fagfeltet av ubemannede luft systemer (UAS) demonstrerer dette. Det er flere sektorer som ønsker UAS velkommen, bare innenfor landbrukssektoren har det vist seg å være av stor verdi for vanningsanlegg planlegging og inspisering, avling og biomasse estimering. Det tar lite energi å operere og betjene systemet, energikilden kan være fornybar. Vi skal alle bevege oss mot ett mer bærekraftig og miljøvennlig livsstil, bransjer, bedrifter og selskaper er ingen unntak. Landbruket er en stor bidragsyter til klimaendringer og miljøskader, vi bør ta et skifte til en mer bærekraftig utvikling for landbruket, presisjon landbruk kan bidra med dette. Målet med denne avhandlingen er å bidra til grunnleggende forskning for fremtidig implementering og innføring av fjernmåling teknologi med en UAV. Denne oppgaven belyser disse områdene, og bidra i å lukke gapet mellom forskere og forbrukere. Ved å forbedre presisjonen på midler som kunstgjødsel eller sprøytemidler, på riktig tidspunkt med riktig mengde, vil resultere i en redusert menge utførelse av midler som vil igjen gi bonden større profittmargin, og mindre konsekvenser på miljøet

An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

Soil moisture prediction with feature selection using a neural network

For the problem of soil moisture prediction, existing approaches in literature [6, 11] usually utilize as many decision factors as possible, e.g. rainfall, solar irradiance, drainage, etc. However, the redundancy aspect of the decision factors has not been studied rigorously. Previous research work in data mining has shown that removing redundant features improves rather than deteriorates the prediction accuracy. In this paper, we propose an approach to the problem of soil moisture prediction, which integrates two components: feature selection and prediction model: A method is proposed for feature selection that effectively removes the redundant decision factors; This is followed by a feedforward neural network to make prediction based on the retained (i.e. non-redundant) decision factors. Empirical simulations demonstrate the effectiveness of the proposed approach. In particular, with the help of the proposed feature selection component to remove redundant decision factors, the proposed approach is shown to give better prediction accuracy with lower data collection cost