Analysis and Prediction of Water Availability Criteria in Potato Using High-Resolution Aerial Photography
DOI:
https://doi.org/10.23960/jtepl.v15i1.198-212
Abstract View: 29
Keywords:
Boundary line analysis, NDWI, Potato cultivation, Soil water availability, UAV remote sensingAbstract
Indonesia’s potato fields are typically small and fragmented, making coarse resolution moisture products prone to spatial mismatch and limiting their usefulness for precision water management. This study developed plot scale, water based suitability information for potato by integrating UAV multispectral imagery with field measurements of soil water availability and plant height response. UAV imagery was processed into four vegetation indices, namely NDWI, SAVI, MSAVI, and SR, followed by geostatistical mapping. Relationships between indices and measured water availability were evaluated using correlation, linear regression, paired t test, and principal component analysis to examine inter index structure and redundancy. NDWI showed the most consistent performance, with a moderate positive correlation with measured water availability (r = 0.47), while SAVI and MSAVI were negatively correlated (r = −0.46) and SR showed the weakest association (r = −0.33). The NDWI based regression for water availability estimation was y = 0.50x + 29.68 with R² = 0.22. The paired t test indicated no significant difference between NDWI based estimates and field measurements, with mean values of 30.09 percent and 30.52 percent, respectively, across 17 observations. Water based land suitability classes were then refined using boundary line analysis linking water availability to plant height response, producing plot scale criteria suitable for precision zoning rather than landscape level evaluation.
Downloads
References
AbdelRahman, M.A.E., Yossif, T.M.H., & Metwaly, M.M. (2025). Enhancing land suitability assessment through integration of AHP and GIS-based for efficient agricultural planning in arid regions. Scientific Reports, 15, 31370. https://doi.org/10.1038/s41598-025-14051-7
Anurogo, W., Lubis, M.Z., & Mufida, M.K. (2018). Modified soil-adjusted vegetation index in multispectral remote sensing data for estimating tree canopy cover density at rubber plantation. Journal of Geoscience Engineering, Environment and Technology, 3(1), 15-21. https://doi.org/10.24273/jgeet.2018.3.01.1003
Benedetto, D.D., Castrignanò, A., & 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. https://doi.org/10.1016/j.proenv.2013.06.050
Brahmantara, R.P., & Kustiyo. (2017). Pengukuran kualitas geometri hasil orthorektifikasi citra WorldView-2. In Prosiding Seminar Nasional Penginderaan Jauh 2017 (Depok, 17 Oct 2017). Lembaga Penerbangan dan Antariksa Nasional.
BPS (Badan Pusat Statistik). (2019). Statistik Tanaman Buah-Buahan dan Sayuran Tahunan Indonesia 2018. Badan Pusat Statistik. https://www.bps.go.id/publication/2019/10/07/1846605363955649c9f6dd6d/statistik-tanaman-buah-buahan-dan-sayuran-tahunan-indonesia-2018.html
Broto, W., Setyabudi, D.A., Sunarmani, Qanytah, & Jamal, B. (2018). Storage technology of potato tuber (Solanum tuberosum L.) GM-05 variety with lighting engineering to maintain freshness. Jurnal Penelitian Pascapanen Pertanian, 14(2), 116–124. https://doi.org/10.21082/jpasca.v14n2.2017.116-124
Casamitjana, M., Torres-Madroñero, M.C., Bernal-Riobo, J., & Varga, D. (2020). Soil moisture analysis by means of multispectral images according to land use and spatial resolution on andosols in the colombian andes. Applied Sciences, 10(16), 5540. https://doi.org/10.3390/app10165540
Djaenudin, D., Marwan, H., Subagjo, H., & Hidayat, A. (2011). Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian (2nd ed.). Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian, Badan Penelitian dan Pengembangan Pertanian. ISBN 978-602-8977-31-9
Dobriyal, P., Qureshi, A., Badola, R., & Hussain, S.A. (2012). A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 458–459, 110–117. https://doi.org/10.1016/j.jhydrol. 2012.06.021
Gardner, W.H. (1986). Water content. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods, 5.1 (2nd ed.). Published by American Society of Agronomy (ASA), Madison, Wisconsin, USA: 493-544. https://doi.org/10.2136/ sssabookser5.1.2ed.c21
Guan, Y., & Grote, K. (2024). Assessing the potential of UAV-based multispectral and thermal data to estimate soil water content using geophysical methods. Remote Sensing, 16(1), 61. https://doi.org/10.3390/rs16010061
Guo, F., Feng, Q., Yang, S., & Yang, W. (2024). Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning. Frontiers in Plant Science, 15, 1458589. https://doi.org/10.3389/fpls. 2024.1458589
Huete, A.R. (1988). A soil- adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Imtiaz, F., Farooque, A.A., Randhawa, G.S., Wang, X., Esau, T.J., Acharya, B., & Hashemi Garmdareh, S.E. (2024). An inclusive approach to crop soil moisture estimation: Leveraging satellite thermal infrared bands and vegetation indices on Google Earth Engine. Agricultural Water Management, 306, 109172. https://doi.org/10.1016/j.agwat.2024.109172
Jordan, C.F. (1969). Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 50(4), 663-666.
Khose, S.B., & Mailapalli, D.R. (2024). Spatial mapping of soil moisture content using very high resolution UAV-based multispectral image analytics. Smart Agricultural Technology, 8, 100467. https://doi.org/10.1016/j.atech.2024.100467
Lamadi, F. S., Musaad, I., & Taberima, S. (2025). Pengembangan kesesuaian lahan pala fakfak (Myristica Argante Warb) untuk peningkatan produksi. Jurnal Locus Penelitian dan Pengabdian, 4(4), 1516-1531.
Liu, M., Yu, T., Gu, X., Sun, Z., Yang, J., Zhang, Z., Mi, X., Cao, W., & Li, J. (2020). The impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas based on unmanned aerial vehicle hyperspectral images. Remote Sensing, 12(1), 146. https://doi.org/10.3390/rs12010146
Manzione, R.L., & Castrignanò, A. (2019). A geostatistical approach for multi-source data fusion to predict water table depth. Science of the Total Environment, 696, 133763. https://doi.org/10.1016/j.scitotenv.2019.133763
Martiningrum, Y. (2017). Analisis tutupan mangrove melalui perhitungan indeks vegetasi yang berbeda dengan menggunakan citra satelit Landsat-8 Oli di Pesisir, Kabupaten Sidoarjo [Undergraduated Thesis]. Universitas Brawijaya. Retrieved from http://repository.ub.ac.id/id/eprint/7664
McFeeters, S.K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
Meyer, L.H., Heurich, M., Beudert, B., Premier, J., & Pflugmacher, D. (2019). Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sensing, 11(10), 1160. https://doi.org/10.3390/rs11101160
Mohamed, E.S., Ali, A., El-Shirbeny, M., Abutaleb, K., & Shaddad, S.M. (2020). Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region. The Egyptian Journal of Remote Sensing and Space Science, 23(3), 347–353. https://doi.org/10.1016/j.ejrs.2019.04.003
Mukiibi, A., Machakaire, A.T.B., Franke, A.C., & Steyn, J.M. (2025). A systematic review of vegetation indices for potato growth monitoring and tuber yield prediction from remote sensing. Potato Research, 68, 409–448. https://doi.org/10.1007/s11540-024-09748-7
Peng, J., Manevski, K., Kørup, K., Parsons, D., & Andersen, M.N. (2026). Agro-environmental profile of potato cultivation under water and nitrogen managements – A case study in Denmark. European Journal of Agronomy, 172, 127874. https://doi.org/10.1016/j.eja.2025.127874
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. https://doi.org/10.15243/jdmlm.2020.073.2221
Ruwaimana, M., Atmaja, N., & Yuda, I.P. (2017). Resolusi spasial optimum pada citra drone untuk klasifikasi spesies mangrove dengan metode maximum likelihood. Biota, 2(2), 68–76. https://doi.org/10.24002/biota.v2i2.1659
Sareh, A.F.F., & Rayes, M.L. (2019). Evaluasi kesesuaian lahan padi pada sawah irigasi di Kecamatan Junrejo, Kota Batu. Jurnal Tanah dan Sumberdaya Lahan, 6(1), 1193–1200. https://doi.org/10.21776/ub.jtsl.2019.006.1.18
Selmy, S.A.H., Jimenez-Ballesta, R., Kucher, D.E., Sayed, A.S.A., García-Navarro, F.J., Yang, Y., & Yousif, I.A.H. (2024). Land suitability assessment and crop water requirements for twenty selected crops in an arid land environment. Agronomy, 14(11), 2601. https://doi.org/10.3390/agronomy14112601
Serrano, J., Shahidian, S., & da-Silva, J.M. (2019). Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a mediterranean agro-silvo-pastoral system. Water, 11(1), 62. https://doi.org/10.3390/w11010062
Badan Pusat Statistik Kota Batu. (2017). Kota Batu dalam angka 2017. Badan Pusat Statistik Kota Batu. Retrieved from https://batukota.bps.go.id/publication/2017/08/11/854e67badaf27f24c61b2ae8/kota-batu-dalam-angka-2017.html
Suhairi, T.A.S.T.M., Jahanshiri, E., & Nizar, N.M.M. (2018). Multicriteria land suitability assessment for growing underutilised crop, bambara groundnut in Peninsular Malaysia. IOP Conference Series: Earth and Environmental Science, 169, 012044. https://doi.org/10.1088/1755-1315/169/1/012044
Sun, X.-L., Wang, H.-L., Zhao, Y.-G., Zhang, C., & Zhang, G.-L. (2017). Digital soil mapping based on wavelet decomposed components of environmental covariates. Geoderma, 303, 118–132. https://doi.org/10.1016/j.geoderma.2017.05.017
Teixeira, A.C., Bakon, M., Lopes, D., Cunha, A., & Sousa, J.J. (2025). A systematic review on soil moisture estimation using remote sensing data for agricultural applications. Science of Remote Sensing, 12, 100328. https://doi.org/10.1016/j.srs.2025.10032
Venkatramanan, S., Prasanna, M.V., & Chung, S.Y. (2019). GIS and geostatistical techniques for groundwater science. Elsevier. https://doi.org/10.1016/C2017-0-02667-8
Wang, G., Miao, X., Wang, J., Tian, D., Ren, J., & Li, Z. (2025). Analysis of potato growth, water consumption characteristics and irrigation strategies in the agro-pastoral ecotone of Northwest China. Agronomy, 15(12), 2685. https://doi.org/10.3390/agronomy15122685
Yang, X., Chen, J., Lu, X., Liu, H., Liu, Y., Bai, X., Qian, L., & Zhang, Z. (2025). Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges. Plants, 14(16), 2544. https://doi.org/10.3390/plants14162544
Zhang, S., Wang, X., Lin, H., Dong, Y., & Qiang, Z. (2025). A review of the application of UAV multispectral remote sensing technology in precision agriculture. Smart Agricultural Technology, 12, 101406. https://doi.org/10.1016/j.atech.2025.101406
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Istika Nita, Aditya Nugraha Putra, Shofie Rindi Nurhutami, Michelle Talisia Sugiarto, Novandi Rizky Prasetya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International Lice that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknik Pertanian Lampung
JTEPL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
