Reliability of using high-resolution aerial photography (red, green and blue bands) for detecting available soil water in agricultural land


  • Aditya Nugraha Putra Soil Science Department, Faculty of Agriculture, Brawijaya University
  • Istika Nita Soil Science Department, Faculty of Agriculture, Brawijaya University



aerial photography, available soil water, geo statistic, precision agriculture, UAV


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).

Author Biography

Aditya Nugraha Putra, Soil Science Department, Faculty of Agriculture, Brawijaya University

Soil Science Department Brawijaya University, Malang


Boori, M.S., Choudhary, K., Paringer, R.A. and Evers, M. 2017. Food vulnerability analysis in the central dry zone of Myanmar. ÐšÐ¾Ð¼Ð¿ÑŒÑŽÑ‚ÐµÑ€Ð½Ð°Ñ ÐžÐ¿Ñ‚Ð¸ÐºÐ° 41(4).

Borra-Serrano, I., Peña, J.M., Torres-Sánchez, J., Mesas-Carrascosa, F.J. and López-Granados F. 2015. spatial quality evaluation of resampled unmanned aerial vehicle-imagery for weed mapping. Sensors 15(8):19688–708.

Claessens, L., Heuvelink, G.B.M., Schoorl, J.M. and Veldkamp, A. 2005. DEM Resolution effects on shallow landslide hazard and soil redistribution modelling. Earth surface processes and landforms. The Journal of the British Geomorphological Research Group 30(4):461–477.

Dane, J.H., Topp, G.C. and Campbell, G.S. 2002. Methods of Soil Analysis. Physical Methods. Soil Science Society of America book series. 1692p.

Dani, O. and Wrath, J.M. 2000. Water Movement in Soil. Handbook of Soil Science. CRC Press, Boca Raton-London-New York-Washington DC p. A53-A86.

Danoedoro, P., Hidayati, I.N. and Widayani, P. 2010. The Effect of Scales in the Analysis of Agricultural Land-Use Fragmentation Based on Satellite Images of Semarang Area, Indonesia. Proceedings of the SEAGA 2010. 23-26 November 2010. Hanoi, Vietnam, pp 1-7.

De Benedetto, D., Castrignanò, A. and Quarto, R. 2013. A geostatistical approach to estimate soil moisture as a function of geophysical data and soil attributes. Procedia Environmental Sciences 19:436–445.

De Clercq, M., Vats, A. and Biel, A. 2018. Agriculture 4.0: The Future of Farming Technology. Proceedings of the World Government Summit, Dubai, UAE 11–13.

Gitelson, A.A., Kaufman, Y.J., Stark, R. and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80(1):76–87.

Govender, M., Dye, P.J., Weiersbye, I.M., Witkowski, E.T.F. F. Ahmed, F. 2009. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water 35(5): 741-752.

Hassan-Esfahani, L., Torres-Rua, A. and McKee, M. 2015. Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data. Agricultural Water Management 153:42–50.

Hunt, E., Raymond, W., Hively, D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T. and McCarty, G.W. 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2(1):290–305.

Krenz, J., Greenwood, P. and Kuhn, N.J. 2019. Soil degradation mapping in drylands using Unmanned Aerial Vehicle (UAV) data. Soil Systems 3(2):33.

Mello, C.R., Ãvila, L.F., Norton, L.D., da Silva, A.M., de Mello, J.M. and Beskow, S. 2011. Spatial distribution of top soil water content in an experimental catchment of Southeast Brazil. Scientia Agricola 68(3):285–94.

Mutmainna, N.D., Achmad, M. and Suhardi. 2017. Estimation of Inceptisol soil moisture in horticultural plants using Landsat Image 8. Jurnal Agritechno 135–151 (in Indonesian).

Nathans, L.L., Oswald, F.L.and Nimon, K. 2012. Interpreting multiple linear regression: a guidebook of variable importance. Practical Assessment, Research & Evaluation 17: 123-136.

Nita, I., Listyarini, E. and Kusuma, Z. 2014. A Study of available moisture on toposekuen north slope G. Kawi Malang Regency, East Java. Jurnal Tanah dan Sumberdaya Lahan 1(2):53–62 (in Indonesian).

Padarian, J., Minasny, B. and McBratney, A.B. 2017. Chile and the Chilean soil grid: a contribution to global soil map. Geoderma Regional 9:17–28.

Padarian, J., Minasny, B., McBratney, A.B. and Dalgliesh, N. 2014. Predicting and mapping the soil available water capacity of Australian wheatbelt. Geoderma Regional 2–3:110–118.

Petropoulos, G., Carlson, T.N., Wooster, M.J. and Islam, S. 2009. A Review of Ts/VI remote sensing-based methods for the retrieval of land surface energy fluxes and soil surface moisture. Progress in Physical Geography: Earth and Environment 33(2):224–250.

Purinton, B. and Bookhagen, B. 2017. Validation of Digital Elevation Models (DEMs) and comparison of geomorphic metrics on the Southern Central Andean Plateau. Earth Surface Dynamics 5(2):211–237.

Sà nchez-Marrè, M., Béjar, J., Comas, J., Brocca, L., Melone, F. and Moramarco, T. 2008. Soil Moisture Monitoring at Different Scales for Rainfall-Runoff Modelling. pp. 7–10.

Shafian, S. and Maas, S.J. 2015. Index of soil moisture using raw landsat image digital count data in Texas High Plains. Remote Sensing 7(3):2352–2372.

Sills, E.O., Atmadja, S.S., de Sassi, C., Duchelle, A.E., Kweka, Ida D.L., Resosudarmo, A.P. and Sunderlin, W.D. 2014. REDD+ on the Ground: A Case Book of Subnational Initiatives across the Globe. CIFOR.

Tóth, B., Makó, A., Guadagnini, A. and Tóth, G. 2012. Water retention of salt-affected soils: quantitative estimation using soil survey information. Arid Land Research and Management 26(2):103–121.

Virgawati, S. 2011. Centre of Farmers Learning Activity Based on ESD as the Media for Precision Agriculture Technology Implementation (Social Experiment on Community-Based Precision Paddy in Central Java, Indonesia). 6.

Wilson, J.P. 2012. Digital terrain modeling. Geomorphology 137(1):107–121.

Zaman, B., McKee, M. and Neale, C.M.U. 2012. Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines. International Journal of Remote Sensing 33(20):6516–6552.








How to Cite

Putra, A. N., & Nita, I. (2020). Reliability of using high-resolution aerial photography (red, green and blue bands) for detecting available soil water in agricultural land. Journal of Degraded and Mining Lands Management, 7(3), 2221–2232.



Research Article