Soil properties estimation with the use of reflectance spectroscopy has met major advances over the last decades. Their non-destructive nature and their high accuracy capacity enabled a breakthrough in the efficiency of performing soil analysis against conventional laboratory techniques. As the need for rapid, low cost, and accurate soil properties’ estimations increases, micro electro mechanical systems (MEMS) have been introduced and are becoming applicable for informed decision making in various domains. This work presents the assessment of a MEMS sensor (1750–2150 nm) in estimating clay and soil organic carbon (SOC) contents. The sensor was first tested under various experimental setups (different working distances and light intensities) through its similarity assessment (Spectral Angle Mapper) to the measurements of a spectroradiometer of the full 350–2500 nm range that was used as reference. MEMS performance was evaluated over spectra measured from 102 samples in laboratory conditions. Models’ calibrations were performed using random forest (RF) and partial least squares regression (PLSR). The results provide insights that MEMS could be employed for soil properties estimation, since the RF model demonstrated solid performance over both clay (R2 = 0.85) and SOC (R2 = 0.80). These findings pave the way for supporting daily agriculture applications and land related policies through the exploration of a wider set of soil properties.
The development of a sustainable water quality monitoring system at national scale remains a big challenge until today, acting as a hindrance for the efficient implementation of the Water Framework Directive (WFD). This work provides valuable insights into the current state-of-the-art Earth Observation (EO) tools and services, proposing a synergistic use of innovative remote sensing technologies, in situ sensors, and databases, with the ultimate goal to support the European Member States in effective WFD implementation. The proposed approach is based on a recent research and scientific analysis for a six-year period (2017–2022) after reviewing 71 peer-reviewed articles in international journals coupled with the scientific results of 11 European-founded research projects related to EO and WFD. Special focus is placed on the EO data sources (spaceborne, in situ, etc.), the sensors in use, the observed water Quality Elements as well as on the computer science techniques (machine/deep learning, artificial intelligence, etc.). The combination of the different technologies can offer, among other things, low-cost monitoring, an increase in the monitored Quality Elements per water body, and a minimization of the percentage of water bodies with unknown ecological status.
In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection.
Classical linear regression has been used to measure the relationship between rainfall data and altitude in different meteorological stations, in order to evaluate a linear relation. The values of rainfall are supposed as dependent variables and the values of elevation of each station as independent variables. It has long been known that a classical statistical relationship exists between annual rainfall and the station elevation which in many cases is linear as the one examined in this article. However classical linear regression makes rigid assumptions about the statistical properties of the model, accepting the error terms as random variables, and the violation of this assumption could affect the validity of the classical linear regression. Fuzzy regression assumes ambiguous and imprecise parameters and data. For this reason it may be more effective than classical regression. In this paper we evaluate the relationship between annual rainfall data and the elevation of each station in Thessaly’s meteorological stations, using fuzzy linear regression with trapezoidal membership functions. In this possibilistic model the dependent measured elevations are crisp, and the independent observed rainfall values as well as the parameters of the model are fuzzy.
The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.
Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.
The aim of this paper is to implement the fuzzy logic theory in order to estimate the discharge for open channels, which is a well-known physical problem affected by many factors. The problem can be solved by Manning equation but the parameters present uncertainties as to their true-real values. Especially, the Manning n roughness coefficient, which is an empirically derived coefficient, presents quite high variation for different substrates. With the help of fuzzy logic and utilizing a fuzzy transformation method, it is possible to include the uncertainties of the problem in the calculation process. In this case, it is feasible to estimate the discharge, giving more emphasis on different uncertainty rates of the Manning roughness coefficient, while the rest of the parameters remain with constant or zero uncertainty level. By taking different a-cut levels, it was shown that the methodology gives realistic and reliable results, presenting with great accuracy the variations of the water discharge for trapezoidal open channels. This way, a possible underestimation or overestimation of the actual physical condition is avoided, by helping the engineers and researchers to obtain a more comprehensive view of the real physical conditions, thus making better management plans.
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