Abstract
Comprehensive surface soil moisture (SM) monitoring is a vital task in precision agriculture applications. SM monitoring includes remote sensing imagery monitoring and in situ sensor-based observational monitoring. Cloud computing can increase computational efficiency enormously. A geographical web service was developed to assist in agronomic decision making, and this tool can be scaled to any location and crop. By integrating cloud computing and the web service-enabled information infrastructure, this study uses the cloud computing-enabled spatio-temporal cyber-physical infrastructure (CESCI) to provide an efficient solution for soil moisture monitoring in precision agriculture. On the server side of CESCI, diverse Open Geospatial Consortium web services work closely with each other. Hubei Province, located on the Jianghan Plain in central China, is selected as the remote sensing study area in the experiment. The Baoxie scientific experimental field in Wuhan City is selected as the in situ sensor study area. The results show that the proposed method enhances the efficiency of remote sensing imagery mapping and in situ soil moisture interpolation. In addition, the proposed method is compared to other existing precision agriculture infrastructures. In this comparison, the proposed infrastructure performs soil moisture mapping in Hubei Province in 1.4 min and near real-time in situ soil moisture interpolation in an efficient manner. Moreover, an enhanced performance monitoring method can help to reduce costs in precision agriculture monitoring, as well as increasing agricultural productivity and farmers’ net-income.
Highlights
Unlike traditional Soil Moisture (SM) interpretation, the model in this paper is adjusted through experimentation to improve the performance of SM management
Eligible Algorithm for precision agriculture (PA) Monitoring Based on Remote Sensing and in Situ Sensors
Eligible Algorithm for PA Monitoring Based on Remote Sensing and in Situ Sensors As almost no attempt has been made to introduce cloud computing into PA, we attempt to create the Aapsparlmoporsitatneocaotmtebminpat thioans btoeepnrmopaodseetaoninetlriogdibulceemcloonuidtocroinmgpaulgtionrgitihnmto tPoAe,fwfeectaivtteelmy pmt otonictroerattehe thSeMapcopnrodpitriioante, acsoimnbSiencatitoionns t2o.2parnodpo2s.3e.aInmeplliegmibelnetmatoionnithoraisnbgeaelngoacrihtihemvetdoienffSeeccttiivoenlsy3m.2oannitdor3.t3h.eIn SMSecctoionnd3it.i2o,nth, eastiimneSceocntisounm2p.2tioanndofSNecDtiVonI m2.a3p. pIimngplheams ebneteantidoencrheaassebdeebny aCcEhSieCvIe. dThieniSnescittiuosnen3.s2or anodbsSerevctaitoionn3s.3w. erIne aSneacltyiozned3i.2n, ntehaertriemael-tcimonesuinmSpetcitoionno3f.3N
Summary
Sensor Web and Soil Moisture (SM) Monitoring in Precision Agriculture. The concept of precision agriculture (PA) is based on the presence of temporal and spatial within-field variability in soil and crop characteristics [1,2]. The concept combines information technology with agricultural principles to manage this spatial and temporal variability in the agricultural production process [3,4]. By using more advanced technology, PA is possible and can be put into practice [5]. Soil moisture (SM) plays an important role in describing geo-gas energy transformation, water circulation and many climatic and hydrological processes [6], such as streamflow forecasting [7], runoff, erosion control [8], SM and the interactions between meteorological phenomena [9]. SM is essential to PA because the SM condition is vital to the crop quality and yield
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