A Decade Analysis (2013-2023) of Paddy's Yield Productivity by Using Landsat 8 Imagery in Sukoharjo District, Indonesia

Authors

DOI:

https://doi.org/10.24114/jg.v16i2.52397

Abstract

Sukoharjo District has the highest rice productivity in Central Java. Sukoharjo has a strategic location. It makes this area prone to land use changes. It makes a severe impact on Paddy's productivity. This condition needs to be monitored continually. Remote sensing can provide an efficient and accurate method to solve this condition. Using the NDVI from Landsat 8 imagery and ubinan data, the model can be built to calculate and analyze paddy™s productivity. The steps were 1) interpretation of paddy fields area; 2) calculation of NDVI™s mean values a month before harvesting; 3) interpretation accuracy test; 4) correlation value between NDVI and ubinan data in 2022; 5) calculation of paddy productivity; and 6) analysis. Within a decade (2013-2023), there was a reduction in paddy's yield area for 981.90 Ha. During that period, there was an increase in paddy's productivity, around 38.2 x 103 tons. Almost all sub-districts in Sukoharjo™s yield had been reduced except for Tawangsari and Weru. Kartasura and Grogol have experienced an intensive change in paddy yields to non-paddy yields. Intensive land use changes affected paddy™s productivity. Multi-temporal imagery combined with ubinan data can be used to monitor paddy™s productivity. Forty-one points were calculated for the y (productivity) and x (NDVI value) equation. The equation resulting from this method (y=6.2212x+6.7444) can be used as a reference for calculating productivity in Sukoharjo District in different periods. From different calculations, the accuracy obtained from this method was 86%.Keywords: Landsat 8; NDVI; Paddy™s Productivity; Sukoharjo; Ubinan data

References

Al Hibbi, A., Harsono, D., & Lena Satlita, D. (2023). Analisis Karakteristik Gentrifikasi Pada Kawasan Solo Baru Sukoharjo: Tinjauan Dari Penyebab Dan Dampak Ruang-Fisik Gentrifikasi.

Armayani, C., Fauzi, A., & Sembiring, H. (2021). Implementasi Data Mining Pengelompokan Jumlah Data Produktivitas Ubinan Tanaman Pangan Berdasarkan Jenis Ubinan Dengan Metode Clustering Dikab Langkat (Studi Kasus : Badan Pusat Statistik Langkat). Jurnal Informatika Kaputama (JIK), 5(1), 185“196. https://doi.org/10.59697/jik.v5i1.318

Ban, H. Y., Kim, K. S., Park, N. W., & Lee, B. W. (2017). Using MODIS data to predict regional corn yields. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010016

Berd, I., Ekaputra, E. G., Yanti, D., & Stiyanto, E. (2022). The Use of NDVI Algorithm in Predicting the Productivity of Rice Fields of Talang District of Solok Regency. IOP Conference Series: Earth and Environmental Science, 1059(1). https://doi.org/10.1088/1755-1315/1059/1/012004

BPS. (2022). Luas Panen dan Produksi Padi di Indonesia 2022. In Badan Pusat Statistik.

BPS. (2023). Kaputanten Sukoharjo Dalam Angka 2023. In Badan Pusat Statistik.

Brumbaugh, M. A., & Guilford, J. P. (1943). Fundamental Statistics in Psychology and Education. In Journal of the American Statistical Association (Vol. 38, Issue 222). https://doi.org/10.2307/2279562

DPP. (2023). Panen Raya Padi IP 400 bersama Menteri Pertanian dan Bupati Sukoharjo Gapoktan Krido Usodo di Desa Tegalsari, Kecamatan Weru, Kabupaten Sukoharjo. Dinas Pertanian Dan Perikanan Kabupaten Sukoharjo. https://dpp.sukoharjokab.go.id/berita/panen-raya-padi-ip-400-bersama-menteri-pertanian-dan-bupati-sukoharjo-gapoktan-krido-usodo-di-desa-tegalsari-kecamatan-weru-kabupaten-sukoharjo

Faisal, F. (2019). Pengaruh Perlakuan Varietas Berbeda Dan Konsentrasi Garam Terhadap Viabilitas Dan Vigor Benih Padi Sawah (Oryzae sativa ). Jurnal Agrium, 16(1), 13“20. https://doi.org/https://doi.org/10.29103/agrium.v16i1.1337

FAO. (2023). Crop Prospects and Food Situation #2, July 2023. In Crop Prospects and Food Situation #2, July 2023 (Issue 2). https://doi.org/10.4060/cc6806en

Franch, B., San Bautista, A., Fita, D., Rubio, C., Tarrazó-Serrano, D., Sánchez, A., Skakun, S., Vermote, E., Becker-Reshef, I., & Uris, A. (2021). Within-field rice yield estimation based on sentinel-2 satellite data. Remote Sensing, 13(20). https://doi.org/10.3390/rs13204095

Hisyam, A. K., Supriatna, S., & Shidiq, I. P. A. (2022). Remote sensing-based vegetation indices for monitoring rice crop phenology and productivity in cikakak sub-district, sukabumi regency. IOP Conference Series: Earth and Environmental Science, 1089(1), 6“13. https://doi.org/10.1088/1755-1315/1089/1/012025

Inayah, A. N., Mudasirah, M., Salfiana, S., & Khalik, A. (2023). Technique Tiles Taken in the Frame Work of Increasing Agricultural Production in Panca Rijang District. 11(3), 298“306.

Indrasari, S. D., & Kristamtini, K. (2018). Biofortofokasi mineral Fe dan Zn pada beras: perbaikan mutu gizi bahan pangan melalui pemuliaan tanaman. Jurnal Litbang Peranian, 37, 9“16.

Islam, M. D., Di, L., Qamer, F. M., Shrestha, S., Guo, L., Lin, L., Mayer, T. J., & Phalke, A. R. (2023). Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning. Remote Sensing, 15(9). https://doi.org/10.3390/rs15092374

Istiqomah, P., & Pramono, W. T. (2024). Population density analysis on the land use change in Kartasura District Sukoharjo between 2011 and 2021. IOP Conference Series: Earth and Environmental Science, 1314(1). https://doi.org/10.1088/1755-1315/1314/1/012118

Mardiansjah, F. H., Handayani, W., & Setyono, J. S. (2018). Pertumbuhan Penduduk Perkotaan dan Perkembangan Pola Distribusinya pada Kawasan Metropolitan Surakarta. Jurnal Wilayah Dan Lingkungan, 6(3), 215. https://doi.org/10.14710/jwl.6.3.215-233

Marques, A. C., Luís, I. C., Coelho, A. R. F., Pessoa, C. C., Daccak, D., Simões, M., Almeida, A. S., Campos, P. S., Ramalho, J. C., Semedo, J. M. N., Kullberg, J. C., Brito, M. G., Pessoa, M. F., Reboredo, F. H., Marques, P., Silva, M. M., Legoinha, P., Oliveira, K., Pais, I. P., & Lidon, F. C. (2022). Monitorization through NDVI of a Rice (Oryza sativa L.) Culture Production in Ribatejo Region. 3. https://doi.org/10.3390/iocag2022-12170

Nie, L., & Peng, S. (2017). Rice Production in China. In Rice Production Worldwide (pp. 33“52). https://doi.org/10.1007/978-3-319-47516-5_2

Panek, E., & Gozdowski, D. (2020). Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sensing Applications: Society and Environment, 17(August 2019), 100286. https://doi.org/10.1016/j.rsase.2019.100286

Perbup. (2022). Peraturan Bupati Sukoharjo No 56 Tahun 2022 Tentang Pola Tanam dan Rencana Tata Tanam Pada Daerah Irigasi Tahun 2022-2023. Sekretaris Daerah Kabupaten Sukoharjo.

Putri, M. R., . S., DM Manessa, M., & Ristya, Y. (2019). The Spatial Pattern of Rice Productivity Using Sentinel-2A Image in Cariu and Tanjungsari District, Bogor Regency. KnE Engineering, 2019, 355“362. https://doi.org/10.18502/keg.v4i3.5883

Rifai, M. H., Nugroho, B. K., & Wijayanti, A. (2021). Changes in Agricultural Land Use To Non-Agricultural Land in Grogol District of Sukoharjo Regency in 2001 - 2018. Journal of Geography Science and Education, 3(1), 1. https://doi.org/10.32585/jgse.v3i1.1386

Ruslan, K. (2019). Memperbaiki Data Pangan Indonesia Lewat Metode Kerangka Sampel Area. In Center for Indonesian Policy Studies (Issue September).

Supriatna, Rokhmatuloh, Wibowo, A., & Ash Shidiq, I. P. (2020). Rice productivity estimation by Sentinel-2A imagery in Karawang Regency, West Java, Indonesia. International Journal of GEOMATE, 19(72), 49“53. https://doi.org/10.21660/2020.72.5622

Tian, J., Tian, Y., Cao, Y., Wan, W., & Liu, K. (2023). Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data. Sensors, 23(13). https://doi.org/10.3390/s23135876

Wijdania, N., Rahayu, P., & Hardiana, A. (2023). Kawasan perdagangan-jasa Solo Baru sebagai Central Business District di Kabupaten Sukoharjo. Region : Jurnal Pembangunan Wilayah Dan Perencanaan Partisipatif, 18(1), 1. https://doi.org/10.20961/region.v18i1.47901

Yanti, D., Safitri, I., Rusnam, R., & Stiyanto, E. (2022). Rice Productivity Estimation Using Remote Sensing Method. Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), 11(3), 451. https://doi.org/10.23960/jtep-l.v11i3.451-465

Yogi, A. P., Samudro, B. R., Soesilo, A. M., & Pratama, Y. P. (2022). Land use and cover change (LUCC) and migration in Sukoharjo, Indonesia. International Journal of Ethics and Systems, 38(3), 465“483. https://doi.org/10.1108/IJOES-01-2021-0005

Yu, B., & Shang, S. (2018). Multi-year mapping of major crop yields in an irrigation district from high spatial and temporal resolution vegetation index. Sensors (Switzerland), 18(11), 1“15. https://doi.org/10.3390/s18113787

Zhai, Y., Wang, N., Zhang, L., Hao, L., & Hao, C. (2020). Automatic crop classification in northeastern china by improved nonlinear dimensionality reduction for satellite image time series. Remote Sensing, 12(17). https://doi.org/10.3390/RS12172726

Zhang, K., Chen, Y., Zhang, B., Hu, J., & Wang, W. (2022). A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning. Remote Sensing, 14(20). https://doi.org/10.3390/rs14205096

Zhang, K., Ge, X., Shen, P., Li, W., Liu, X., Cao, Q., Zhu, Y., Cao, W., & Tian, Y. (2019). Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages. Remote Sensing, 11(4). https://doi.org/10.3390/rs11040387

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Published

2024-07-24

How to Cite

Sekarsih, F. N., & Kusuma, G. F. (2024). A Decade Analysis (2013-2023) of Paddy’s Yield Productivity by Using Landsat 8 Imagery in Sukoharjo District, Indonesia. JURNAL GEOGRAFI, 16(2), 227–240. https://doi.org/10.24114/jg.v16i2.52397