Imputation of Missing Parts in UAV Orthomosaics Using PlanetScope and Sentinel-2 Data: A Case Study in a Grass-Dominated Area  
[PERSON]. Pereira  
1 Interdisciplinary Centre of Energy Planning, University of Campinas, Campinas 13083-896, Sao Paulo, Brazil 12 Federal Institute of Education, Science and Technology of Alagoas, Satuba 57120-000, Alagoas, Brazil 2  
[PERSON]  
1 Interdisciplinary Centre of Energy Planning, University of Campinas, Campinas 13083-896, Sao Paulo, Brazil 12 Federal Institute of Education, Science and Technology of Alagoas, Satuba 57120-000, Alagoas, Brazil 2  
[PERSON]  
3 School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Sao Paulo, Brazil 3  
[PERSON]  
3 School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Sao Paulo, Brazil 34 Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, Sao Paulo, Brazil 4  
[PERSON]  
3 School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Sao Paulo, Brazil 3  
[PERSON]  
3 School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Sao Paulo, Brazil 3  
[PERSON]  
1 Interdisciplinary Centre of Energy Planning, University of Campinas, Campinas 13083-896, Sao Paulo, Brazil 14 Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, Sao Paulo, Brazil 4  
[PERSON]  
5 University of West Carolina, Agricultural Sciences department, President Prudente Campus, President [PERSON] 19050-920, Sao Paulo, Brazil 5  
[PERSON]  
5 Correspondence: [EMAIL_ADDRESS]; Tel. : +58-82-999003322555825  
situation is authentic for using UAV remote sensing for PA applications in tropical areas, where vegetation grows fast and agriculture usually relies on rainfall. The acquisition of UAV images, even a few days apart, may result in losing valuable information about the target of interest due to everyday agriculture events, such as intensive grazing, grass moving, weed spraying, pre-harvest crop desiccation, tillage, and harvest. The ideal spatial resolutions of UAV remote sensing for PA applications are defined based on the target of interest and the possibility of machinery intervention [7]. Usually, such interventions are executed by farm equipment, which cannot deal with sub-meter spatial resolutions. However, monitoring large agricultural areas (>200 ha) using UAV imagery is challenging, even adjusting for moderate spatial resolutions suitable for typical PA applications. The UAV systems have some disadvantages, such as small ground coverage (a few km\({}^{2}\) of swath) [8; 9] due to battery capacity, flight restrictions, and limited flights under wind gust conditions. Those disadvantages may result in missing parts in UAV orthomosaics of large agricultural areas and limited data acquisition in critical crop growth stages. Imputing missing parts in UAV orthomosaics is crucial and necessary to ensure UAV imagery application in PA over large areas and in specific crop growth stages. For years, missing values in optical satellite imagery have been a challenge for the remote sensing community [8; 10; 11; 12; 13], usually caused by cloud cover, shadows, or sensor malfunctions. The proposed approaches to replace missing values in remote sensing data often rely on filling in the pixel values in data gaps [14]. Spatial gap-filling methods or spatial imputation methods are usually based on deterministic and geostatistical interpolation approaches, such as ordinary kriging and cokriging, where the information contained within surrounding (non-missing) pixels is used to interpolate the missing parts of the images [10; 14; 15; 16]. Temporal gap-filling methods predict the missing pixel values using the non-missing pixel values on images from different times. This process is usually carried out by fitting a curve on the non-missing data before and after the gap, such as the Savitzky-Golay filter [17; 18; 19; 20].