Journal of Degraded and Mining Lands Management · 2020 · 11 citations · 17 references
Natural SciencesEarth SciencesHydrologyDigital Elevation ModelingRemote SensingSoil-water RelationsAgricultural ApplicationsIrrigationPhotogrammetryRemote Sensing Image AnalysisWater Resource MonitoringWater Status MonitoringMap ScaleRemote Sensing In HydrologyRemote Sensing Of WatershedsSoil Water Content CharacterizationUnmanned Aerial Vehicle ImagingUnmanned Aerial Vehicle PhotogrammetryUnmanned Aerial Vehicle Remote SensingUnmanned Aerial Vehicle Sensing
The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).
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Novel algorithms for remote estimation of vegetation fraction
Anatoly A. Gitelson, Yoram J. Kaufman, Robert J. Stark et al. · Remote Sensing of Environment · 2002 · 2.1K citations
Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
E. Raymond Hunt, W. Dean Hively, S. Fujikawa et al. · Remote Sensing · 2010 · 486 citations · Full text
Agriculture, Agronomy, Crop Sciences +17