| Title | Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing |
| Abstract | Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute.
Maps of bulk soil electrical conductivity (ECa) recorded with EMI sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk ECa survey, has been applied in an agricultural field in Apulia region (south-eastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries and preliminary observations. Three optimization criteria were used: the first criterion (MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid ECa data as weighting function, and the third criterion (MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil moisture estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time and the cooling law, which can be an invaluable support to the process of sampling design.
The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk ECa gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels. |
| Source | Environmental monitoring and assessment (Dordr., Online) 187 (1), pp. 1–11 |
| Keywords | SamplingEMI sensorbulk electrical conductivityspatial simulated annealingspatial variabilitysoil moisture assessment |
| Journal | Environmental monitoring and assessment (Dordr., Online) |
| Editor | Kluwer, Dordrecht, Paesi Bassi |
| Year | 2015 |
| Type | Articolo in rivista |
| DOI | 10.1007/s10661-015-4570-y |
| Authors | Emanuele Barca, Annamaria Castrignanò, Gabriele Buttafuoco, Daniela De Benedetto, Giuseppe Passarella |
| Text | 331160 2015 10.1007/s10661 015 4570 y ISI Web of Science WOS 000357340500030 Scopus 2 s2.0 84931261022 Sampling EMI sensor bulk electrical conductivity spatial simulated annealing spatial variability soil moisture assessment Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing Emanuele Barca, Annamaria Castrignano, Gabriele Buttafuoco, Daniela De Benedetto, Giuseppe Passarella Emanuele Barca, Giuseppe Passarella, CNR IRSA Annamaria Castrignano, CRA SCA e CNR ISAFOM Gabriele Buttafuoco, CNR ISAFOM Daniela De Benedetto, CRA SCA Soil survey is generally time consuming, labour intensive and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity ECa recorded with EMI sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field scale bulk ECa survey, has been applied in an agricultural field in Apulia region south eastern Italy . Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries and preliminary observations. Three optimization criteria were used the first criterion MMSD optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion MWMSD is a weighted version of the MMSD, which uses the digital gradient of the grid ECa data as weighting function, and the third criterion MAOKV minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil moisture estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk ECa gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels. 187 Published version 26/04/2015 Barca et al Environ Monit Assess 2015 Barca_et_al_Environ_Monit_Assess_2015.pdf Articolo in rivista Kluwer 1573 2959 Environmental monitoring and assessment Dordr., Online Environmental monitoring and assessment Dordr., Online Environ. monit. assess. Dordr., Online Environmental monitoring and assessment. Dordr., Online annamariacastrignano CASTRIGNANO ANNA MARIA giuseppe.passarella PASSARELLA GIUSEPPE gabriele.buttafuoco BUTTAFUOCO GABRIELE emanuele.barca BARCA EMANUELE TA.P04.005.008 Integrazione di metodologie per il monitoraggio e la modellizzazione per la gestione delle risorse idriche AG.P04.019.006 Sostenibilita e sicurezza delle produzioni agroalimentari |
