Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method

Luthfan Nur Habibi, Komariah Komariah, Dwi Priyo Ariyanto, Jauhari Syamsiyah, Takashi S.T. Tanaka

Abstract

Soil organic matter (SOM) is one of the important parameters in agriculture management, thus estimating its distribution on the land will be essential. Remote sensing can be utilized to map the SOM distribution in the large-scale area. The objective of this research was to determine the estimation of SOM distribution on the paddy field in Sukoharjo Regency, Indonesia using Landsat 8 OLI imagery. The sampling points were determined by purposive sampling based on an overlay of land use classification map of paddy field, NDSI (Normalized Difference Soil Index) map, and soil type map. The analysis method was used simple linear regression (SLR) and multiple linear regression (MLR) between SOM content and a digital number of Landsat 8 OLI imagery. The SLR analysis resulted that all band except band 1 and 5 of Landsat 8 OLI Imagery have the capability to estimating SOM. The MLR model based on best subset analysis resulted in the combination of bands 3, 4, 6, and 7 was the best model for estimating SOM distribution (R2=0.399).  The MLR model was used to create SOM distribution map on paddy field in Sukoharjo Regency and resulted in the SOM range of the area is distributed from very low (<1%) to moderate (2.1–4.2%) with the largest area was on low level (1–2%) about 11,028 ha. The result indicates that Landsat 8 OLI Imagery could be used for mapping the SOM distribution.

Keywords

Landsat 8 OLI; NDSI; PCA; Precision agriculture; Regression

Full Text:

PDF

References

Agus, F. (2011). Environmental and sustainability issues of Indonesian agriculture. J Litbang Pert, 30(4), 140–147.

Ahmed, Z., & Iqbal, J. (2014). Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS. European Journal of Remote Sensing, 47(1), 557–573. https://doi.org/10.5721/eujrs20144731

Al-doski, J., Mansor, S. B., & Shafri, H. Z. M. (2013). Support vector machine classification to detect land cover changes in Halabja City, Iraq. IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), 353–358. https://doi.org/10.1109/BEIAC.2013.6560147

Balai Penelitian Tanah. (2005). Analisis Kimia Tanah, tanaman, air, dan pupuk: Petunjuk Teknis (B. H. Prasetyo, D. Santoso, & R. W. Ladiyani, Eds.). Bogor, Indonesia: BALAI PENELITIAN TANAH.

BPS Statistic of Sukoharjo Regency. (2015). Kabupaten Sukoharjo dalam angka 2015. Sukoharjo, Indonesia: Penerbit BPS.

Chen, F., Kissel, D. E., West, L. T., & Adkins, W. (2000). Field-scale Mapping of Surface Soil Organic Carbon using Remotely Sensed Imagery. Soil Sci. Soc. Am. J., 64(2), 746–753. https://doi.org/10.2136/sssaj2000.642746x

Daendoro, P. (2012). Pengantar penginderaan jauh digital. Yogyakarta, Indonesia: Penerbit Andi.

Deng, Y., Wu, C., Li, M., & Chen, R. (2015). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40–48. https://doi.org/10.1016/j.jag.2015.02.010

Department of the Interior USGS. (2015). Landsat 8 (L8) Data Users Handbook. Sioux Falls, South Dakota: U.S. Geological Survey.

Gatu, C., & Kontoghiorghes, E. J. (2006). Branch-and-Bound Algorithms for Computing the Best- Subset Regression Models. Journal of Computational and Graphical Statistics, 15(1), 139–156. https://doi.org/10.1198/106186006X100290

Hutchinson, J. J., Campbell, C. A., & Desjardins, R. L. (2007). Some perspectives on carbon sequestration in agriculture. Agri. Forest Meteorol., 142(2–4), 288–302. https://doi.org/https://doi.org/10.1016/j.agrformet.2006.03.030

Idris, M., Subiyanto, S., & Sabri, L. (2014). Analisis pemanfaatan citra Landsat 7 untuk pemetaan kandungan bahan organik tanah dengan metode PCA dan regresi linier berganda bertahap di Kabupaten Bangkalan. J. Geodesi Undip, 3(1), 51–64.

Kim, H. J., Sudduth, K. A., & Hummel, J. W. (2009). Soil macronutrient sensing for precision agriculture. Journal of Environmental Monitoring. Journal of Environmental Monitoring, 11(10), 1810–1824. https://doi.org/10.1039/b906634a

Komatsuzaki, M., & Ohta, H. (2007). Soil management practice for sustainable agroecosystem. Sustainability Science, 2(1), 103–120. https://doi.org/10.1007/s11625-006-0014-5

Komatsuzaki, M., & Syuaib, M. F. (2010). Comparison of the Farming System and Carbon Sequestration between Conventional and Organic Rice Production in West Java, Indonesia. Sustainability Science, 2, 833–843. https://doi.org/https://doi.org/10.3390/su2030833

Kongapai, P. (2007). Application of remote sensing and geographic information system for estimation of soil organic matter in Nakhonpratom Province. Mahidol University, Thailand.

Lee, S. B., Lee, C. H., Jung, K. Y., Park, K. Do, Lee, D., & Kim, P. J. (2009). Changes of soil organic carbon and its fractions in relation to soil physical properties in a long-term fertilized paddy. Soil & Tillage Research, 104, 227–232. https://doi.org/https://doi.org/10.1016/j.still.2009.02.007

Nocita, M., Stevens, A., Noon, C., & Wesemael, B. van. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37–42. https://doi.org/10.1016/j.geoderma.2012.07.020

Rahmati, M., Oskouei, M. M., Neyshabouri, M. R., & Fard, A. (2016). Soil Organic Carbon Prediction using Remotely Sensed Data at Lighvan Watershed, Northwest of Iran. Azarian Journal of Agriculture, 3, 45–49.

Ratner, B. J. (2009). The correlation coefficient: Its values range between +1/−1, or do they? Target Meas Anal Mark, 17(2), 139–142. https://doi.org/https://doi.org/10.1057/jt.2009.5

Shonk, J. L., Gaultney, L. D., Schulze, D. G., & Scoyoc, G. E. Van. (1991). Spectroscopic Sensing of Soil Organic Matter Content. Transactions of the ASAE, 34(5), 1978–1984. https://doi.org/10.13031/2013.31826

Singh, S. K., Srivastava, P. K., & Gupta, M. (2014). Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci, 71(5), 2245–2255. https://doi.org/https://doi.org/10.1007/s12665-013-2628-0

Stephens, S. C., Rasmussen, V. P., Ramsey, R. D., Whitesides, R. E., Searle, G. S., & Robert, L. (2004). Remote Sensing Organic Carbon in Soil. Utah, US: Utah State University.

Sukojo, B. ., & Wahono. (2002). Pemanfaatan Teknologi Penginderaan Jauh untuk Pemetaan Kandungan Bahan Organik Tanah. J. Makara Teknologi, 6(3), 102–112.

Summers, D., Lewis, M., Ostendorf, B., & Chittleborough, D. (2011). Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11(1), 123–131. https://doi.org/10.1016/j.ecolind.2009.05.001

Sunaryo, S., & Siagian, T. . (2011). Mengatasi masalah multikolinearitas dan outlier dengan pendekatan robpca (studi kasus analisis regresi angka kematian bayi di Jawa Timur). J. Matematika, Saint, Dan Teknologi, 12(1), 1–10.

Supriyadi, Sudaryanto, R., Purwanto, & Hartati, S. (2014). Kualitas tanah, menuju pertanian yang berkelanjutan. Surakarta, Indonesia: Yuma Pressindo.

Syuaib, M. F. (2016). Sustainable agriculture in Indonesia: Facts and challenges to keep growing in harmony with environment. Agricultural Engineering International: CIGR Journal, 18(2), 170–184.

Wu, C., Wu, J., Luo, Y., Zhang, L., & DeGloria, S. D. (2009). Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data. Soil Sci. Soc. Am. J., 73(4), 1202–1208. https://doi.org/10.2136/sssaj2008.0045

DOI: https://doi.org/10.20961/stjssa.v16i2.35395

Refbacks

  • There are currently no refbacks.