Publicado

2023-11-10

Application of remote sensing methods for statistical estimation of organic matter in soils

Aplicación de métodos de teledetección terrestre para la estimación estadística del contenido de humus en suelos

DOI:

https://doi.org/10.15446/esrj.v27n3.100324

Palabras clave:

spectral brightness of soils, Landsat 8, prediction of humus content, multiple linear regression, bare soils identification, vegetation index (en)
brillo espectral de suelos, Landsat 8, predicción de contenido de humus, regresión lineal múltiple, identificación de suelos desnudos, índice de vegetación (es)

Descargas

Archivos adicionales

Autores/as

The availability of reliable information on the physicochemical properties of soils is a necessary tool for maintaining and improving fertility and effective optimization of agricultural land management in many countries. However, ground-based research methods require significant financial and time resources. It has been established that methods based on remote sensing data are an efficient, accurate, and less costly solution for studying different types of soil cover parameters. This work aims to determine the predicted indicator of humus content in soils in selected regions of the Kyiv region (Ukraine) with the corresponding soil types. For this, the spectral properties of chernozem soils were investigated based on Landsat 8 OLI satellite images. A mosaic of the mean spectral reflectance values for the study period (2013-2015) was created using the Google Earth Engine. The vegetation indices NDSI, NDWI, NDBI, MSAVI, and NDVI were used to identify bare soils. Using multiple linear regression, an optimal F-Comparing Nested Model was created for predicting humus content in soils, including seven parameters. The model's accuracy was estimated with such indicators R=0.95, R2= 0.90, σy = 0.16 %. The approach based on the proposed model can be used to support the adoption of the necessary management decisions to improve soil fertility and maintain balanced land use.

La disponibilidad de información confiable sobre las propiedades fisicoquímicas de los suelos es una herramienta necesaria para mantener y mejorar la fertilidad y la optimización efectiva de la gestión de las tierras agrícolas en muchos países. Los métodos de investigación basados en tierra requieren importantes recursos económicos y de tiempo. Se ha establecido que los métodos basados en datos de teledetección son una solución eficiente, precisa y menos costosa para estudiar diferentes tipos de parámetros de cobertura del suelo. El propósito de este trabajo es determinar el indicador predicho del contenido de humus en suelos en regiones seleccionadas de la región de Kiev con los tipos de suelo correspondientes.

Para ello, se investigaron las propiedades espectrales de los suelos chernozem a partir de imágenes satelitales Landsat 8 OLI y se creó un mosaico de los valores de reflectancia espectral media para el período de estudio (2013-2015) utilizando Google Earth Engine. Los índices de vegetación NDSI, NDWI, NDBI, MSAVI y NDVI se utilizaron para identificar suelos abiertos (suelos desnudos). Utilizando regresión lineal múltiple, se creó un modelo anidado de comparación F óptimo para predecir el contenido de humus en los suelos, incluidos siete parámetros. La precisión del modelo se estimó con dichos indicadores R=0.95, R2= 0.90, σy = 0.16 %. El enfoque basado en el modelo propuesto se puede utilizar para apoyar la adopción de las decisiones de gestión necesarias para mejorar la fertilidad del suelo y mantener un uso equilibrado de la tierra.

Referencias

Abramov, D. (2012). Dynamics of the humus state of dark chestnut soils using remote sensing data. Scientific works. Ecology, 206(194), 69-73. (In Ukrainian).

Achasov, A., & Bidolah, B. (2008). Use of space and terrestrial digital photography to determine the humus content of soils. Soil science, 3, 280-286. (In Russian). DOI: https://doi.org/10.1134/S1064229308030022

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 DOI: https://doi.org/10.5721/EuJRS20144731

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In: B. Petrov and F. Csake. (Eds). 2nd International Symposium on Informational Theory. Budapest, Akademiai Kiado.

Alkharabsheh, H. M., Seleiman, M. F., Battaglia, M. L., Shami, A., Jalal, R. S., Alhammad, B. A., Almutairi, K. F. & Al-Saif, A. M. (2021). Biochar and Its Broad Impacts in Soil Quality and Fertility, Nutrient Leaching and Crop Productivity: A Review. Agronomy, 11(5), 993. https://doi.org/10.3390/agronomy11050993 DOI: https://doi.org/10.3390/agronomy11050993

Allohverdi, T., Mohanty, A. K., Roy, P., & Misra, M. (2021). A Review on Current Status of Biochar Uses in Agriculture. Molecules, 26(18), 5584. https://doi.org/10.3390/molecules26185584 DOI: https://doi.org/10.3390/molecules26185584

Atlas of natural conditions and natural resources of the Ukrainian SSR (1978). Main Directorate of Geodesy and Cartography. Moscow. (In Russian).

Bach, E. M., Ramirez, K. S., Fraser, T. D. & Wall, D. H. (2020). Soil Biodiversity Integrates Solutions for a Sustainable Future. Sustainability, 12, 2662. https://doi.org/10.3390/su12072662 DOI: https://doi.org/10.3390/su12072662

Baveye, P. C., Schnee, L. S., Boivin P., Laba M., & Radulovich, R. (2020). Soil Organic Matter Research and Climate Change: Merely Re-storing Carbon Versus Restoring Soil Functions. Frontiers in Environmental Science, 8, 2296-665. https://doi.org/10.3389/fenvs.2020.579904 DOI: https://doi.org/10.3389/fenvs.2020.579904

Belenok, V., Hebryn-Baidy, L., Bielousova, N., Gladilin, V., Kryachok, S., Tereshchenko, A., Alpert, S., & Bodnar, S. (2023). Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine). Journal of Applied Remote Sensing, 17(1), 014506. https://doi.org/10.1117/1.JRS.17.014506 DOI: https://doi.org/10.1117/1.JRS.17.014506

Belenok, V., Hebryn-Baidy, L., Bіelousova, N., Zavarika, H., Sakal, O., & Kovalenko, A. (2022). Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine). Acta Scientiarum Polonorum. Formatio Circumiectus, 21(1), 69–84. https://doi.org/10.15576/ASP.FC/2022.21.1.69 DOI: https://doi.org/10.15576/ASP.FC/2022.21.1.69

Belenok, V., Noszczyk, T., Hebryn-Baidy, L., & Kryachok, S. (2021). Investigating anthropogenically transformed landscapes with remote sensing. Remote Sensing Applications: Society and Environment, 24, 100635. https://doi.org/10.1016/j.rsase.2021.100635 DOI: https://doi.org/10.1016/j.rsase.2021.100635

Bouasria, A., Ibno, N. K., Rahimi, A., & Mostafa, E. (2020). Soil organic matter estimation by using Landsat-8 pansharpened image and machine learning. 4rth International Conference On Intelligent Computing in Data Sciences (ICDS). https://doi.org/10.1109/ICDS50568.2020.9268725 DOI: https://doi.org/10.1109/ICDS50568.2020.9268725

Bouzekri, S., Lasbet, A. A., & Lachehab, A. (2015). A new spectral index for extraction of built-up area using Landsat-8 data. Journal of the Indian Society of Remote Sensing, 43(4), 867–873. https://doi.org/10.1007/s12524-015-0460-6 DOI: https://doi.org/10.1007/s12524-015-0460-6

Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47, 1287–1294. DOI: https://doi.org/10.2307/1911963

Byndych, T. Y. (2021). Diagnostic and parameterization of lateral soil heterogeneity based on multispectral space scanning data. [Thesis for a Doctor of Science degree], Kharkiv. (In Ukrainian).

Chatohin, A., & Lindin, M. (2001). Conjugate study of Donbass chernozems by land and remote sensing methods. Soil science, 9, 1037-1044. (In Ukrainian).

Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133-146. https://doi.org/10.1016/j.rse.2005.11.016 DOI: https://doi.org/10.1016/j.rse.2005.11.016

Chornyy, S., & Abramov, D. (2016). The monitoring of southern chernozem soil humus content with using multispectral satellite images Landsat: Spatial and temporal aspects. Fundamental and Applied Soil Science, 17(1-2), 22-30. http://dx.doi.org/10.15421/041602 DOI: https://doi.org/10.15421/041602

Das, S., Deb, P., Bora, P. K., & Katre, P. (2021). Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India. Sustainability (Switzerland), 13(1), 232, 1-22. https://doi.org/10.3390/su13010232 DOI: https://doi.org/10.3390/su13010232

David, J. M. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009 DOI: https://doi.org/10.1016/j.biosystemseng.2012.08.009

Demattê, J. A. M., Galdos, M. V., Guimarães, R. V., Genú, A. M., Nanni, M. R., & Zullo, J. (2007). Quantification of tropical soil attributes from ETM /LANDSAT-7 data. International Journal of Remote Sensing, 28(17), 3813-3829. https://doi.org/10.1080/01431160601121469 DOI: https://doi.org/10.1080/01431160601121469

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 DOI: https://doi.org/10.1016/j.jag.2015.02.010

Ding, Y., Zheng, X., Zhao, K., Xin, X., & Liu, H. (2016). Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China. Remote Sensing, 8(1), 29. https://doi.org/10.3390/rs8010029 DOI: https://doi.org/10.3390/rs8010029

EarthExplorer (2021). USGS. https://earthexplorer.usgs.gov (last accessed 16.12.2021).

Fiorio, P. R., & Demattȩ, J. A. M. (2009). Orbital and laboratory spectral data to optimize soil analysis. Scientia Agricola, 66(2), 250-257. https://doi.org/10.1590/s0103-90162009000200015 DOI: https://doi.org/10.1590/S0103-90162009000200015

Forkuor, G., Hounkpatin, O., Welp, G., & Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12(1), e0170478. https://doi.org/10.1371/journal.pone.0170478 DOI: https://doi.org/10.1371/journal.pone.0170478

Gao, B. C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3 DOI: https://doi.org/10.1016/S0034-4257(96)00067-3

Gasmi, A., Gomez, C., Lagacherie, P., Zouari, H., Laamrani, A., & Chehbouni, A. (2021). Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil clay content and mapping coverage. Geoderma, 388, 114864. https://doi.org/10.1016/j.geoderma.2020.114864 DOI: https://doi.org/10.1016/j.geoderma.2020.114864

Gaudutis, A., Jotautienė, E., Mieldažys, R., Bivainis, V., & Jasinskas, A. (2023). Sustainable Use of Biochar, Poultry and Cattle Manure for the Production of Organic Granular Fertilizers. Agronomy, 13(5), 1426. https://doi.org/10.3390/agronomy13051426 DOI: https://doi.org/10.3390/agronomy13051426

Google Earth Engine, 2021. https://earthengine.google.com/ (last accessed 30.11.2021).

Gopp, N. V., Nechaeva, T. V., Savenkov, O. A., Smirnova, N. V., & Smirnov, V. V. (2019). Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data. Izvestiya, Atmospheric, Oceanic Physics, 55, 1329-1337. https://doi.org/10.1134/S0001433819090202 DOI: https://doi.org/10.1134/S0001433819090202

Gopp, N. V., Nechaeva, T. V., Savenkov, O. A., Smirnova, N. V. & Smirnov, V. V. (2017). Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia. Eurasian Soil Science, 50, 1332-1343. https://doi.org/10.1134/S1064229317110060 DOI: https://doi.org/10.1134/S1064229317110060

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031 DOI: https://doi.org/10.1016/j.rse.2017.06.031

Hebryn-Baidy, L. (2017). Application of remote sensing methods to evaluation of soil fertility indicators of Zakarpattia lands. Interdepartmental scientific and technical collection. Geodesy, cartography and aerial photography, 85(85), 42-52. https://doi.org/10.23939/istcgcap2017.01.042 DOI: https://doi.org/10.23939/istcgcap2017.01.042

Higginbottom, T. P., & Symeonakis, E. (2014). Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions. Remote Sensing, 6, 9552-9575. https://doi.org/10.3390/rs6109552 DOI: https://doi.org/10.3390/rs6109552

Huang, Y., Chen, Z. X., Yu, T., Huang, X. Z., & Gu, X. F. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17, 1915-1931. https://doi.org/10.1016/S2095-3119(17)61859-8 DOI: https://doi.org/10.1016/S2095-3119(17)61859-8

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. https://doi.org/10.1016/0034-4257(88)90106-X DOI: https://doi.org/10.1016/0034-4257(88)90106-X

Jasinski, M. F. (1990). Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale. Remote Sensing of Environment, 32, 169-187. https://doi.org/10.1016/0034-4257(90)90016-F DOI: https://doi.org/10.1016/0034-4257(90)90016-F

Jiang, Z., Huete, A., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845. https://doi.org/10.1016/j.rse.2008.06.006 DOI: https://doi.org/10.1016/j.rse.2008.06.006

Jing, Z., Dongli, J., Dong, D., Jinjie, M., Hongwei, L., & Yaonan, B. (2020). Temporal paradox in soil potassium estimations using spaceborne multispectral imagery. Catena, 194, 104771. https://doi.org/10.1016/j.catena.2020.104771 DOI: https://doi.org/10.1016/j.catena.2020.104771

Joshi, P. P., Wynne, R. H. & Thomas, V. A. (2019). Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8. International Journal of Applied Earth Observation and Geoinformation, 82, 101898. https://doi.org/10.1016/j.jag.2019.101898 DOI: https://doi.org/10.1016/j.jag.2019.101898

Khangura, R., Ferris, D., Wagg, C., & Bowyer, J. (2023). Regenerative Agriculture - A Literature Review on the Practices and Mechanisms Used to Improve Soil Health. Sustainability, 15(3), 2338. https://doi.org/10.3390/su15032338 DOI: https://doi.org/10.3390/su15032338

Khellouk, R., Barakat, A., Jazouli, A. E., Lionboui, H., & Benabdelouahab, T. (2021). Assessment of water cloud model based on SAR and optical satellite data for surface soil moisture retrievals over agricultural area. Eurasian Journal of Soil Science, 10(3), 243-250. https://doi.org/10.18393/ejss.926813 DOI: https://doi.org/10.18393/ejss.926813

Koroleva, P. V., Rukhovich, D. I., Rukhovich, A. D., Rukhovich, D. D., Kulyanitsa, A. L., Trubnikov, A. V., Kalinina, N. V., & Simakova, M. S. (2017). Location of Bare Soil Surface and Soil Line on the RED-NIR Spectral Plane. Eurasian Soil Science, 50, 1375-1385. https://doi.org/10.1134/S1064229317100040 DOI: https://doi.org/10.1134/S1064229317100040

Kravtsova, V. (2005). Space methods of soil research. Aspect Press, Moscow. 190 p. (In Russian).

Kulyanitsa, A. L., Rukhovich, A. D., Rukhovich, D. D., Koroleva, P. V., Rukhovich, D. I., & Simakova, M. S. (2017). The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials. Eurasian Soil Science, 50, 387-395. https://doi.org/10.1134/S1064229317040044 DOI: https://doi.org/10.1134/S1064229317040044

Landsat 8 Collection 2 (C2) Level 2 Science Product (L2SP) Guide (2020). U.S. Geological Survey. https://www.usgs.gov/media/files/landsat-8-collection-2-level-2-science-product-guide (last accessed 19.08.2021).

Landsat Missions. (2021). USGS. https://www.usgs.gov/landsat-missions (last accessed 16.12.2021).

Li, S., & Chen, X. (2014). A new bare-soil index for rapid mapping developing areas using Landsat 8 data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(4), 139-144. https://doi.org/10.5194/isprsarchives-XL-4-139-2014 DOI: https://doi.org/10.5194/isprsarchives-XL-4-139-2014

Liashenko, D., Belenok, V., Spitsa, R., Pavlyuk, D., & Boiko, O. (2020). Landslide GIS modelling with QGIS software. In: XIV International Scientific Conference on Monitoring of Geological Processes and Ecological Condition of the Environment. European Association of Geoscientists & Engineers, Kyiv, Ukraine. https://doi.org/10.3997/2214-4609.202056069 DOI: https://doi.org/10.3997/2214-4609.202056069

Lu, M., Liu, Y., & Liu, G. (2022). Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods. Computers and Electronics in Agriculture, 200, 107246. https://doi.org/10.1016/j.compag.2022.107246 DOI: https://doi.org/10.1016/j.compag.2022.107246

Luo, J., Zhang, W., Zhang, X., & Liu, H. (2022). Mapping soil organic matter content using Sentinel-2 synthetic images at different time intervals in Northeast China. International Journal of Digital Earth, 16(1), 1094-1107. https://doi.org/10.1080/17538947.2023.2192005 DOI: https://doi.org/10.1080/17538947.2023.2192005

Main Department of the State Land Agency in Kyiv region. (2012). Program of land use and protection in Kyiv region for the period 2012-2020. (In Ukrainian) http://www.Kyivoblzem.org/ (last accessed 05.11.2021).

Mateo-García, G., Gómez-Chova, L., Amorós-López, J., Muñoz-Marí, J., & Camps-Valls, G. (2018). Multitemporal cloud masking in the Google Earth Engine. Remote Sensing, 10(7), 1079. https://doi.org/10.3390/rs10071079 DOI: https://doi.org/10.3390/rs10071079

Mekuriaw, A., Cherinet, M., & Tsegaye, L. (2021). Assessing the impact of land cover changes on selected ecosystem services in upper Suha watershed, Gojjam, Ethiopia. International Journal of River Basin Management, 19(4), 459-467. https://doi.org/10.1080/15715124.2019.1704767 DOI: https://doi.org/10.1080/15715124.2019.1704767

Mendenhall, W., & Sincich, T. (2013). A Second Course in Statistics: Regression Analysis. 7th Edition. Pearson New International Edition. 749 pp.

Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H., & Asadzadeh, F. (2016). Spatial variability of soil organic matter using remote sensing data. Catena, 145, 118-127. https://doi.org/10.1016/j.catena.2016.05.023 DOI: https://doi.org/10.1016/j.catena.2016.05.023

Montandon, L., & Small, E. (2008). The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment, 112, 1835-1845. https://doi.org/10.1016/j.rse.2007.09.007 DOI: https://doi.org/10.1016/j.rse.2007.09.007

Montero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., & Wieneke, S. (2023). A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research. Scientific Data, 10(197), 6254. https://doi.org/10.1038/s41597-023-02096-0 DOI: https://doi.org/10.1038/s41597-023-02096-0

Navarro-Pedreño, J., Almendro-Candel, M. B., & Zorpas, A. A. (2021). The Increase of Soil Organic Matter Reduces Global Warming, Myth or Reality? Sci, 3(1), 18. https://doi.org/10.3390/sci3010018 DOI: https://doi.org/10.3390/sci3010018

Olthof, I., & Fraser, R. H. (2014). Detecting landscape changes in high latitude environments using Landsat trend analysis: 2. classification. Remote Sensing, 6(11), 11558-11578. https://doi.org/10.3390/rs61111558 DOI: https://doi.org/10.3390/rs61111558

Omia, E., Bae, H., Park, E., Kim, M. S., Baek, I., Kabenge, I., & Cho, B. K. (2023). Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances. Remote Sensing, 15(2), 354. https://doi.org/10.3390/rs15020354 DOI: https://doi.org/10.3390/rs15020354

Parikh, S. J., & James, B. R. (2012). Soil: The Foundation of Agriculture. Nature Education Knowledge, 3(10), 2. https://www.nature.com/scitable/knowledge/library/soil-the-foundation-of-agriculture-84224268/ (last accessed 05.11.2021).

Prudnikova, E., & Savin, I. (2021). Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data. Remote Sensing, 13, 2313. https://doi.org/10.3390/rs13122313 DOI: https://doi.org/10.3390/rs13122313

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. https://doi.org/10.1016/0034-4257(94)90134-1 DOI: https://doi.org/10.1016/0034-4257(94)90134-1

Reis, A. S., Rodrigues, M., Santos, G. L. A. A., Oliveira, K. M., Furlanetto, R. H., Crusiol, L. G. T., Cezar, E., & Nanni, M. R. (2021). Detection of soil organic matter using hyperspectral imaging sensor combined with multivariate regression modeling procedures. Remote Sensing Applications: Society and Environment, 22, 100492. https://doi.org/10.1016/j.rsase.2021.100492 DOI: https://doi.org/10.1016/j.rsase.2021.100492

Riggs, G. A., Hall, D. K., & Salomonson, V. V. (1994). A snow index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer. Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium, 8-12 Aug., 1994, Pasadena, CA, USA. DOI: https://doi.org/10.1109/IGARSS.1994.399618

Romanova, S., Hrichenko, O., Venglinskiy, M., & Yarmolenko, E. (2018). Humus state of soils of Kyiv region. Agroecological journal, 3, 34-40. (In Ukrainian). https://doi.org/10.33730/2077-4893.3.2018.148057 DOI: https://doi.org/10.33730/2077-4893.3.2018.148057

Rukhovich, D. I., Koroleva, P. V., Rukhovich, D. D., & Kalinina, N. V. (2021). The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution. Remote Sensing, 13, 155. https://doi.org/10.3390/rs13010155 DOI: https://doi.org/10.3390/rs13010155

Sakhatsky, О. (2008). Experience in using satellite data to assess soil status to solve natural resource problems. Reports of the National Academy of Sciences of Ukraine, 3, 109-115. (In Ukrainian).

Savin, I., Prudnikova, E., Chendev, Y., Bek, A., Kucher, D., & Dokukin, P. (2021). Detection of Changes in Arable Chernozemic Soil Health Based on Landsat TM Archive Data. Remote Sensing, 13, 2411. https://doi.org/10.3390/rs13122411 DOI: https://doi.org/10.3390/rs13122411

Shapiro, S. S. & Francia, R. S. (1972). An approximate analysis of variance test for normality. Journal of the American Statistical Association, 67(337), 215-216. https://doi.org/10.1080/01621459.1972.10481232 DOI: https://doi.org/10.1080/01621459.1972.10481232

Shetty, A., Umesh, P., & Shetty, A. (2021). An exploratory analysis of urbanization effects on climatic variables: a study using Google Earth Engine. Modeling Earth Systems and Environment, 8(1), 1363–1378. https://doi.org/10.1007/s40808-021-01157-w DOI: https://doi.org/10.1007/s40808-021-01157-w

Shikhov, A., Gerasimov, A., Ponomarchuk, A., & Perminova, E. (2020). Thematic deciphering and interpretation of cosmic signs of middle and high spaciousness. State National Research University, Perm. (In Russian).

Srinivasan, R., Singh, S. K., Nayak, D. C., Hegde, R., & Ramesh, M. (2019). Estimation of soil loss by USLE model using remote sensing and GIS Techniques - A case study of Coastal Odisha, India. Eurasian Journal of Soil Science, 8(4), 321-328. https://doi.org/10.18393/ejss.598120 DOI: https://doi.org/10.18393/ejss.598120

State Department of Environmental Protection in Kyiv region. (2020). Regional report on the state of the environment in Kyiv region in 2020. (In Ukrainian) http://eco-Kyiv.com.ua (last accessed 05. 11. 2021).

State Department of Environmental Protection in Kyiv region. (2021). (In Ukrainian) https://ecology-Kyivoblast.com.ua/page/stan-dovkillya-kyyivskoyi-oblasti (last accessed 05. 11. 2021).

State Institution “Soils protection Institute of Ukraine”. (2016). Report on the implementation of design and technological and research work in 2015 (final). (In Ukrainian).

State Institution “Soils protection Institute of Ukraine”. (2020). Periodic report of the condition of soils on the land of agriculture of UKRAINE according to the results of the 10th round (2011–2015) agrochemical survey of lands. (In Ukrainian) http://www.iogu.gov.ua/wecompress.com.pdf (last accessed 05.11.2021).

State Institution “Soils protection Institute of Ukraine”. (2020). Agrochemical survey of land. (In Ukrainian) https://www.iogu.gov.ua/pasportizaciya/ahrohimichne-obstezhennya-silskohospodarskyh-uhid/ (last accessed 05. 11. 2021).

State Service of Ukraine for Geodesy, Cartography and Cadastre. (2020). Distribution of land fund of Kyiv region. (In Ukrainian) http://land.gov.ua/ (last accessed 05.11.2021).

Sullivan, D. G., Shaw, J. N., Rickman, D., Mask, P. L., & Luvall, J. C. (2005). Using remote sensing data to evaluate surface soil properties in Alabama ultisols. Soil Science, 170(12), 954–968. https://doi.org/10.1097/01.ss.0000187350.39611.d7 DOI: https://doi.org/10.1097/01.ss.0000187350.39611.d7

Taplin, R. H. (1999). Robust F-tests for linear models. Canadian Journal of Statistics, 27(2), 361-371. https://doi.org/10.2307/3315645 DOI: https://doi.org/10.2307/3315645

The Humanitarian Data Exchange. (2021). https://data.humdata.org (last accessed 16.12.2021).

Tian, J., Su, S., Tian, Q., Zhan, W., Xi, Y., & Wang, N. (2021). A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite. International Journal of Applied Earth Observation and Geoinformation, 101, 102361. https://doi.org/10.1016/j.jag.2021.102361 DOI: https://doi.org/10.1016/j.jag.2021.102361

Truskavetsky, S. (2006). The use of multispectral space scanning and geoinformation systems in the study of the soil cover of Polissya of Ukraine. [PhD Thesis]. (In Ukrainian).

Truskavetsky, S., Byndych, T., Sherstyuk, A., & Viatkin, K. (2015). Studying the Condition of Soil Protection Agrolandscape in Ukraine Using Remote Sensing Methods. Journal of Agricultural Science and Technology A 5, 235-240. https://doi.org/10.17265/2161-6256/2015.04.001 DOI: https://doi.org/10.17265/2161-6256/2015.04.001

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0 DOI: https://doi.org/10.1016/0034-4257(79)90013-0

Vermote, E. F., Justice, C. O., Claverie, M., & Franch, B. (2016). Analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46-56. https://doi.org/10.1016/j.rse.2016.04.008 DOI: https://doi.org/10.1016/j.rse.2016.04.008

Wang, X., & Ge, L. (2012). Evaluation of filters for ENVISAT ASAR speckle suppression in pasture area. Proceedings of the ISPRS Annals of the XXII ISPRS Congress-Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne, pp. 341–346. DOI: https://doi.org/10.5194/isprsannals-I-7-341-2012

Wang, Y., Luo, C., Zhang, W., Meng, X., Liu, Q., Zhang, Y., & Liu, H. (2022). Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window. Sustainability, 15(1), 1-15. https://doi.org/10.3390/su15010469 DOI: https://doi.org/10.3390/su15010469

Website of the Kyiv regional State Administration. (2021). (In Ukrainian) http://www.Kyiv.obl.gov.ua (last accessed 05. 11. 2021).

Weiss, M., Jacob, F., & Duveillerc, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402 DOI: https://doi.org/10.1016/j.rse.2019.111402

Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385-396. https://doi.org/10.1016/S0034-4257(01)00318-2 DOI: https://doi.org/10.1016/S0034-4257(01)00318-2

Wu, C., Wu, J., Luo, Y., Zhang, L., & DeGloria, S. (2009). Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data. Soil Science Society of America Journal, 73(4), 1202-1208. https://doi.org/10.2136/sssaj2008.0045 DOI: https://doi.org/10.2136/sssaj2008.0045

Yacuk, I., & Balyk, S. (2013). Methods of agrochemical certification of agricultural lands, 99. (In Ukrainian).

Yang, J., Li, X., Wu, B., Wu, J., Sun, B., Yan, C., & Gao, Z. (2021). High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China. Frontiers in Environmental Science, 9, 668912. https://doi.org/10.3389/fenvs.2021.668912 DOI: https://doi.org/10.3389/fenvs.2021.668912

Yu, Q., Yao, T., Lu, H., Feng, W., Xue, Y., & Liu, B. (2021). Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau. Computers and Electronics in Agriculture, 185, 106144. https://doi.org/10.1016/j.compag.2021.106144 DOI: https://doi.org/10.1016/j.compag.2021.106144

Yu, T., Jiapaer, G., Bao, A., Zheng, G., Jiang, L., Yuan, Y., & Huang, X. (2020). Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area. Remote Sensing, 13, 2851. https://doi.org/10.3390/rs13152851 DOI: https://doi.org/10.3390/rs13152851

Zatserkovny, V. I., Babych V. Y., Belenok, V. Y., Frolov, H. O., & Hebryn-Baidy, L. V. (2019). Black sea level change monitoring using altimetry data and geoinformation technologies. 18th International Conference on Goinformatics – Theoretical and Applied Aspects, 13-16 May 2019, Kyiv, Ukraine. 15785. https://doi.org/10.3997/2214-4609.201902060 DOI: https://doi.org/10.3997/2214-4609.201902060

Zekoll, V., Main-Knorn, M., Alonso, K., Louis, J., Frantz, D., Richter, R., & Pflug, B. (2021). Comparison of masking algorithms for Sentinel-2 imagery. Remote Sensing, 13(1), 137: 1-21. https://doi.org/10.3390/rs13010137 DOI: https://doi.org/10.3390/rs13010137

Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. https://doi.org/10.1080/01431160304987 DOI: https://doi.org/10.1080/01431160304987

Zhang, M., Liu, H., Zhang, M., Yang, H., Jin, Y., Han, Y., Tang, H., Zhang, X., & Zhang, X. (2021). Mappin Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on theNorthern Songnen Plain. Remote Sensing, 13(24), 5162. https://doi.org/10.3390/rs13245162 DOI: https://doi.org/10.3390/rs13245162

Cómo citar

APA

Belenok, V., Hebryn-Baidy, L., Bіelousova N. ., Zavarika, H. ., Kryachok, S. ., Liashenko, D. . y Malik , T. (2023). Application of remote sensing methods for statistical estimation of organic matter in soils. Earth Sciences Research Journal, 27(3), 299–313. https://doi.org/10.15446/esrj.v27n3.100324

ACM

[1]
Belenok, V., Hebryn-Baidy, L., Bіelousova N. , Zavarika, H. , Kryachok, S. , Liashenko, D. y Malik , T. 2023. Application of remote sensing methods for statistical estimation of organic matter in soils. Earth Sciences Research Journal. 27, 3 (nov. 2023), 299–313. DOI:https://doi.org/10.15446/esrj.v27n3.100324.

ACS

(1)
Belenok, V.; Hebryn-Baidy, L.; Bіelousova N. .; Zavarika, H. .; Kryachok, S. .; Liashenko, D. .; Malik , T. Application of remote sensing methods for statistical estimation of organic matter in soils. Earth sci. res. j. 2023, 27, 299-313.

ABNT

BELENOK, V.; HEBRYN-BAIDY, L.; Bіelousova N. .; ZAVARIKA, H. .; KRYACHOK, S. .; LIASHENKO, D. .; MALIK , T. Application of remote sensing methods for statistical estimation of organic matter in soils. Earth Sciences Research Journal, [S. l.], v. 27, n. 3, p. 299–313, 2023. DOI: 10.15446/esrj.v27n3.100324. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/100324. Acesso em: 9 ago. 2024.

Chicago

Belenok, Vadym, Liliia Hebryn-Baidy, Bіelousova Natalyya, Halyna Zavarika, Sergíy Kryachok, Dmytro Liashenko, y Tetiana Malik. 2023. «Application of remote sensing methods for statistical estimation of organic matter in soils». Earth Sciences Research Journal 27 (3):299-313. https://doi.org/10.15446/esrj.v27n3.100324.

Harvard

Belenok, V., Hebryn-Baidy, L., Bіelousova N. ., Zavarika, H. ., Kryachok, S. ., Liashenko, D. . y Malik , T. (2023) «Application of remote sensing methods for statistical estimation of organic matter in soils», Earth Sciences Research Journal, 27(3), pp. 299–313. doi: 10.15446/esrj.v27n3.100324.

IEEE

[1]
V. Belenok, «Application of remote sensing methods for statistical estimation of organic matter in soils», Earth sci. res. j., vol. 27, n.º 3, pp. 299–313, nov. 2023.

MLA

Belenok, V., L. Hebryn-Baidy, Bіelousova N. ., H. . Zavarika, S. . Kryachok, D. . Liashenko, y T. Malik. «Application of remote sensing methods for statistical estimation of organic matter in soils». Earth Sciences Research Journal, vol. 27, n.º 3, noviembre de 2023, pp. 299-13, doi:10.15446/esrj.v27n3.100324.

Turabian

Belenok, Vadym, Liliia Hebryn-Baidy, Bіelousova Natalyya, Halyna Zavarika, Sergíy Kryachok, Dmytro Liashenko, y Tetiana Malik. «Application of remote sensing methods for statistical estimation of organic matter in soils». Earth Sciences Research Journal 27, no. 3 (noviembre 10, 2023): 299–313. Accedido agosto 9, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/100324.

Vancouver

1.
Belenok V, Hebryn-Baidy L, Bіelousova N, Zavarika H, Kryachok S, Liashenko D, Malik T. Application of remote sensing methods for statistical estimation of organic matter in soils. Earth sci. res. j. [Internet]. 10 de noviembre de 2023 [citado 9 de agosto de 2024];27(3):299-313. Disponible en: https://revistas.unal.edu.co/index.php/esrj/article/view/100324

Descargar cita

CrossRef Cited-by

CrossRef citations1

1. Jinzhao Zou, Yanan Wei, Yong Zhang, Zheng Liu, Yuefeng Gai, Hongyan Chen, Peng Liu, Qian Song. (2024). Remote sensing inversion of soil organic matter in cropland combining topographic factors with spectral parameters. Frontiers in Environmental Science, 12 https://doi.org/10.3389/fenvs.2024.1420557.

Dimensions

PlumX

Visitas a la página del resumen del artículo

195

Descargas

Los datos de descargas todavía no están disponibles.

Licencia

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Earth Sciences Research Journal posee una licencia Creative Commons Attribution.

Usted es libre de: Compartir: copie y redistribuya el material en cualquier medio o formato. Adaptar: remezclar, transformar y ampliar el material para cualquier propósito, incluso comercialmente. El licenciante no puede revocar estas libertades mientras siga los términos de la licencia.