03387nam a2200397 a 450000100080000000500110000800800410001902400440006010000200010424502710012426001260039549000340052152020100055565000190256565000150258465000150259965000140261465000120262865000090264065300180264965300240266765300210269165300220271265300130273465300140274770000310276170000260279270000260281870000270284470000210287170000160289270000180290870000230292670000230294970000170297221614062024-01-30       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1117/12.26803282DOI1 aCARNEIRO, B. M.  aFeasibility analysis of using Sentinel-1 images to phenologically differentiate the areas of soybean seed and sub-irrigated bean planting in the period of sanitary void in the tropical floodplains of the Formoso River basin, Tocantins, Brazil.h[electronic resource]  aIn: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 25., 2023, Amsterdam. Proceedings... Amsterdam: SPIEc2023  a(SPIE proceedings, v. 12727).  aFood production is one of the significant challenges for the world's population. Countries like Brazil, with a vast territorial dimension and good availability of resources, stand out in the production of grains, especially soy. Soy cultivation requires care and management to ensure phytosanitary production and reduce the risk of diseases such as Asian Soybean Rust (ASR) caused by the fungus Phakopsora pachyrhizi. In Brazil, soy cultivation occurs in the spring/summer (September/March), with greater solar energy and rainfall in the country. Brazil has established a fallow period to reduce the risk of ASR, which prohibits planting outside the agricultural calendar. However, there is the possibility of authorizing planting in the floodplains of the tropical plains of the Formoso River basin, Tocantins, Brazil. The government of the State of Tocantins created the State Program for the Control of ASR, authorizing the planting of soybeans during the dry season (April to September) through registration and monitoring of areas. However, other plantings, such as beans, with a shorter cycle and less water demand, also occur. This study aims to monitor the soybean crop development phases considering data collected in the field by the Agricultural Defense Agency (ADAPEC) and digital processing using deep-learning techniques of Sentinel-1 image time series. The phenological differences of cultivation farms enabled agricultural mapping and the fight against ASR. The digital processing steps of the Sentinel-1 time series dataset (10 m resolution) consisted of image pre- processing using Sentinel Application Platform (SNAP); time series filtering using Savitzky-Golay; evaluation of deep learning methods (Long Short-Term Memory - LSTM, Bidirectional LSTM - Bi-LSTM, Gated Recurrent Unit - GRU, and Bidirectional GRU - Bi-GRU); and accuracy analysis. However, the classification has some erroneous portions that can be improved by increasing the number of classes and samples in future works.  aDigital images  aMonitoring  aPhakopsora  aFenologia  aSemente  aSoja  aDeep learning  aFormoso river basin  aMachine learning  aPlant phenotyping  aSentinel  aTocantins1 aCARVALHO JÚNIOR, O. A. de1 aCARVALHO, O. L. F. de1 aALBUQUERQUE, A. O. de1 aCASTRO FILHO, H. C. de1 aRODRIGUES, V. S.1 aLIMA, A. M.1 aANTONY, D. S.1 aEVANGELISTA, B. A.1 aOLIVEIRA, M. C. de1 aPINTO, C. B.