PENDUGAAN KELEMBAPAN TANAH DENGAN MENGGUNAKAN METODE SOIL MOISTURE INDEX (SMI) DI KEBUN KOPI BANGELAN, KABUPATEN MALANG, JAWA TIMUR
DOI:
https://doi.org/10.21776/ub.jtsl.2022.009.1.1Keywords:
coffee, remote sensing, soil, soil moisture, soil moisture indexAbstract
Water is one of the limiting factors in the growth of coffee plants. If plants experience a lack of water, it can inhibit plant growth and, at a critical level, can lead to drought stress and plant damages. The available soil water to plants can be estimated from the level of soil moisture index. The monitoring of soil moisture status can be used in improving the management of coffee plantations. Soil Moisture Index (SMI) is a method that can be used to estimate the level of soil moisture using remote sensing technology using NDVI and LST values. The purpose of this study was to analyze the status and distribution of soil moisture at the coffee plantation; analyze the relationship between vegetation index and soil moisture; and analyzed the relationship between soil moisture status using the SMI method and soil moisture measured in coffee plantations. Results showed that the soil moisture index obtained from Landsat 8 OLI/TIRS image processing had an average value of 0.60. The average soil moisture index at the study site is 1.05. Soil moisture index from the Landsat 8 OLI/TIRS image has a significant positive effect on soil moisture at the study site (y = 7.4996x – 3.4789; R2 = 0.7146**).  It is suggested that the SMI method can be used to estimate soil moisture in the coffee plantation.
References
Ahmed, M., Else, B., Esklund, L., Ardo, J. and Seaquist ,J. 2017. Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel Region. International Journal of Remote Sensing 38(19): 5409-5429.
Badan Pusat Statistik. 2019. Statistik Kopi Indonesia 2019. Badan Pusat Statistik Indonesia.
Carrão, H., Russo, S., Sepulcre-Canto, G. and Barbosa, P. 2016. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. International Journal of Applied Earth Observation Geoinformation 48: 74-84.
Departemen Kehutanan Direktorat Jendral Rehabilitas Lahan dan Perhutanan Sosial. 2005. Pedoman Inventarisasi dan Identifikasi Lahan Kritis Mangrove. Jakarta: Departemen Kehutanan Direktorat Jendral Rehabilitas Lahan dan Perhutanan Sosial.
Dias, P.C., Araujo, W.L., Moraes, G.A., Barros, R.S. and Da Matta, F. 2007. Morphological and physiological responses of two coffee progenies to soil water availability. Journal of Plant Physiology 164(12): 1639-1647.
Gandhi, G.M., Parthiban, S., Thummalu, N. and Christy, A. 2015. NDVI: vegetation change detection using remore sensing and GIS – a case study of Vellore District. Procedia Computer Science 57: 1199-1210
Hunt, E.D., Hubbard, K.G., Wilhite, D.A., Arkebauer, T.J. and Dutcher, A.L. 2009. The development and evaluation of a soil moisture index. International Journal Climatology 29: 747-759.
Karyati, R., Putri, O. dan Syafrudin, M. 2018. Suhu dan kelembaban tanah pada lahan revegetasi pasca tambang di PT Adimitra Baratama Nusantara Provinsi Kalimantan Timur. Jurnal Agrifor XVII: 103-114.
Laboratorium Fisika Tanah. 2006. Instruksi Kerja Pelaksanaan Analisis Berat Isi Tanah dan Pengelolaan Data. Malang: Jurusan Tanah, Fakultas Pertanian, Universitas Brawijaya.
Lwin, K.K. 2010. Estimation of Landsat TM Surface Temperature Using ERDAS Imagine Spatial Modeler; SIS Tutorial Series; Division of Spatial Information Science: 2010. Available online: http://giswin.geo.tsukuba.ac.jp/sis/tutorial/koko/SurfaceTemp/SurfaceTemperature.pdf
Michigan Tech Research Institute. 2009. Inputs to The Multi-Criteria Decision Model. National Consortium for Remote Sensing in Transportation.
Nesper, M.C., Kueffer, S., Krishnan, C., Kushalappa, G. and Ghazoul, J. 2017. Shade tree diversity enchances coffee production and quality in agroforestry system in Teh Western Ghats. Journal Agriculture Ecosystem and Environment 247(2017): 172-181.
Noorhadi dan Sudadi. 2003. Kajian pemberian air dan mulsa terhadap iklim mikro pada tanaman cabai di tanah Entisol. Jurnal Ilmu Tanah dan Lingkungan 4(1): 41-49
Ochbelagh, R.D., Hakimabad, H.M. and Najafabadi, N.R. 2009. The soil moisture and its effect on the detection of buried hydrogenous material by neutron back-scattering technique. Radiation Physics Chemical 78: 303-306.
Parida, B.R., Collado, W.B., Borah, R., Hazarika, M.K. and Samarakoon, L. 2008. Detecting drought-prone areas of rice agriculture using a MODIS-derived soil moisture index. Geosscience & Remote Sensing 45: 109-129.
Park, J.Y., Ahn, S.R., Hwang, S.J., Jang, C.H., Park, G.A. and & Kim, S.J. 2014. Evaluation of MODIS NDVI and LST for indicating soil moisture of forest areas based on SWAT modeling. Paddy and Water Environment 12(1): 77-88.
Pinheiro, H.A., Da Matta, F.M., Chaves, A.R., Loureiro, M.E. and Ducatti, C. 2005. Drought tolerance is associated with rooting depth and stomatal control of water use in clones of Coffea canephora. Annals of Botany 96(1): 101-108.
Potic, I., Bugarski, M. and Matic-Varenica, J. 2017. Soil Moisture Determination Using Remote Sensing Data for The Property Protection and Increase of Agriculture Production. Annual World Bank Conference on Land and Poverty.
Pramesti, G. 2014. Kupas Tuntas Data Penelitian dengan SPSS 22. Jakarta: Elex Media Komputindo.
Purwadhi, F.S.H., Kardono, P., Karsidi, A., Haryani, N.S. dan Rokhmatuloh. 2015. Aplikasi Penginderaan Jauh dan Sistem Informasi Geografis Untuk Pengembangan Wilayah. Jakarta: Polimedia Publishing.
Rahardjo, P. 2012. Kopi. Jakarta: Penebar Swadaya.
Rayes, M.L. 2007. Metode Inventarisasi Sumber Daya Lahan. Yogyakarta: Andi.
Rouse, J.W., Hass, R.H., Schell, J.A. and Deering, D.W. 1973. Monitoring Vegetation Systems in The Great Plains With ERTS. In: Proceedings of The Third ERTS Symposium. Washington: Goddard Space Flight Center.
Sandholt, I., Rasmussen, K. and Andersen, J. 2002. A simple interpretation of teh surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79: 213-224.
Schmugge, T., Gloersen, P., Wilheit, T. and Geiger, F. 1974. Remote sensing of soil moisture with microwave radiometers. Journal of Geophysical Research 79: 317–323.
Schnur, M.T., Xie, H. and Wang, X. 2010. Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA. Ecological Informatics 5(5): 400-409.
Shafian, S. and Maas, S. 2015. Index of soil moisture using raw landsat image digital count data in Texas High Plains. Remote Sensing 7: 2352-2372.
Silva, B.M., Oliveira, G.C., Serafim, M.E., Silva, É.A., Guimarães, P.T.G., Melo, L.B.B., Norton, L.D. and Curi, N. 2019. Soil moisture associated with least limiting water range, leaf water potential, initial growth and yield of coffee as affected by soil management system. Soil and Tillage Research 189: 36-43.
Sridhar,V., Hubbard, K.G., You, J. and Hunt, E.D. 2008. Development of the soil moisture index to quantify agricultural drought and its “user friendliness†in severity-area-duration assessment. Journal Hydrometeorology 9: 660-676.
Taktikou, E., Bourazanis, G., Papaioannou, G. and Kerkides, P. 2016. Prediction of soil moisture from remote sensing data. Procedia Engineering 162: 309-316.
Verhulst, N. and Govaerts, B. 2010. The Normalized Difference Vegetation Index (NDVI) GreenSeekerâ„¢ Handheld Sensor: Toward The Integrated Evaluation of Crop Management. Part A: Concepts and Case Studies. Mexico, D. F,; CIMMYT.
Vicente-Serrano, S.M., Pons-Fernández, X. and Cuadrat-Prats, J.M. 2004. Mapping soil moisture in The Central Ebro River Valley (Northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data. International Journal of Remote Sensing 25: 4325–4350.
Wang, L. and Qu, J.J. (2009). Satellite remote sensing application for surface soil moisture monitoring: a review. Front Earth Science China 3: 237-247.
Wang, X., Xie, H., Guan, H. and Zhou, X. 2007. Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology 340(1-2): 12-24.
Weng, Q., Lu, D. and Schubring, J. 2004. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment 89: 467-483.
Wolka, K., Mulder, J. and Biazin, B. 2018. Effects of soil and water conservation techniques on crop yield, runoff and soil loss in sub-saharan Africa. Agriculture Water Management 207: 67-79.
Yue, J., Tian, J., Tian, Q., Xu, K. and Xu, N. 2019. Development of soil moisture indices from differences in water absorption between shortwave-infrared bands. International Journal of Photogrammetry and Remote Sensing 154: 216-230.
Zeng, Y., Feng, Z. and Xiang, N. 2004. Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment. Geoscience and Remote Sensing Symposium 6: 4306-4309.
Zhan, Z., Qin, Q. and Wang, X. 2004. The Application of LST/NDVI Index for Monitoring Land Surface Moisture in Semiarid Area. In Proceedings of the IGARSS ’04, Anchorage, AK, USA, 20–24 September 2004; Volume 3, pp. 1551–1555.
Zhang, C., Mishra, D.R. and Pennings, S.C. 2019. Mapping Salt Marsh Soil Properties Using Imaging Spectroscopy. International Journal of Photogrammetry and Remote Sensing 148: 221-234.
Zhang, D. and Zhou, G. 2016. Estimation of soil moisture from optical and thermal remote sensing. Sensors 16: 1-29.
Zhang, Y., Gong, J., Sun, K., Yin, J. and Chen, X. 2018. Estimation of soil moisture index using multi-temporal Sentinel-1 images over Poyang Lake ungauged zone. Remote Sensing 10(1): 12-24.
Downloads
Published
How to Cite
Issue
Section
License
The copyright of the received article shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
