Modeling Soil Nitrogen Using Remote Sensing, Regression and Random Forest Models

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Mahboubeh Sadeghi ¹ , Mozhgan Ahmadi Nadoushan ²

1 - MSc., Environmental Sciences, Department of Environmental Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Environmental Sciences, Waste and Wastewater Research Center, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

Received: 2021-05-07 Accepted : 2021-08-28 Published : 2021-12-22

Keywords: Terrain data, Spectral Indexو Modeling, Landsat 8 satellite image,

Abstract :

Background and Aim: Soil is one of the important natural resources of any country, which plays an important role in preserving the environment and producing food. Increasing and decreasing the amount of total soil nitrogen due to various agricultural methods, the entry of industrial wastewater into water and other factors, leads to microbial contamination of soil, reduced vegetation and deficiency in agricultural products needed by humans. Mapping soil nutrient distribution helps mangers in decisions. Since laboratory analysis of these parameters is time consuming and costly across large scales, attempts have been made in recent years to study soil nitrogen based on remote sensing techniques. In this regard, the present study investigated the applicability of remote sensing predicting soil total nitrogen in the east of Lenjan city.Method: Nitrogen reference points were identified by analyzing 50 randomly-selected surface soil samples from 0-20 cm depth. Nitrogen of soil samples was measured by Kjeldahl method after drying soil at 25 ° C, passing through a 2 mm mesh sieve and transferring to the laboratory, to compare the final results obtained from field sampling and remote sensing technology. Landsat 8 OLI Satellite Image of 2019 (Path 164/Row 37) was obtained and geometric and radiometric correction were applied. Cloud cover for image provided was less than 10%. To reduce the effect of atmospheric diffusion on the quality of image, radiation and atmospheric correction were performed using the FLASH model. the Landsat-8 satellite image (rows 164 and 37) taken on 15 Sep. 2019 and along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the multiple linear regression and random forest models. Results: The digital elevation map of the area showed elevation values ​​between 1100 and 2050 meters. The slope of the study area was less than eight percent. Numerical values ​​of TWI index near water bodies were found to be 0.77. DVI and EVI index values ​​increased with increasing vegetation cover. NDVI index showed values ​​higher than 0.3 and NDWI index as a water index showed a maximum value of 0.77. The SAVI index showed high differences from areas without cover to sparse cover and areas with strong vegetation. SBI index and SI salinity indices showed very high variability in terms of soil parameters in barren lands. Nitrogen regression model was built using three indices RVI, DVI and TWI with p-values of 0.049 and 0.00, respectively. In the nitrogen random forest model, however, plant and soil indices played a more important role in model construction with an of r2 value of 0.44.Conclusion: Total soil nitrogen in soil parameters showed correlation with density and sand and clay from soil texture and in topographic parameters with elevation and spectral indices with EVI RVI, SAVI, NDWI, NDVI and DVI at the level of 0.01 and with SI3 of salinity indices at the 0.05 level. In soil parameters, silt is correlated with sand and clay at the level of 0.05 and sand with clay as well as density with clay are correlated at the level of 0.01. The results of this study showed that the topographic condition of the region along with red and near infrared-based indices had a significant role in predicting soil total nitrogen. Results also showed a slight difference was observed between the two models in predicting soil nitrogen.

References:

References:

Abbasi, Y., Mirzaei, F., and Sohrabi, T. (2018). Distribution of heavy metals Pb, Cu and Ni in irrigated fields by wastewater of Tehran city, Iran, using Sentinel2 image. Water and Irrigation Management, 8(1): 113-129. [in Persian]

Alexakis, D.I., Daliakopoulos, I., and Tsanis, P. (2018). Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece. Geocarto International, 33(4): 321-338.

Amini Khoei, Z., Abdollahpouri, A. (2017). Classification of network traffic using enhanced random forest algorithm. Computing Science Journal, 16: 2-17. [in Persian]       

Asfaw, E.K., Suryabhagavan, M., and Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 17(3): 250-258.

Aslani, A. (2012). Analysis and study of severe glaciations in Zayandehrood basin. Master thesis, Faculty of Geography, Yazd University, Faculty of Geography, 8: 2-24. [in Persian]

Belgiu, M., Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31.

Bremmer, J.M. and Mulvancey, C.S. (1982). Total nitrogen. In: Page AL, Miller RH and Keeney DR, (eds.). Method of Soil Analysis. Part II. Aragon Monogr, 9, Soil Science Society of America and American Society of Agronomy, Madison, WI, USA. 599-622.

Camera, C., Zomeni, Z., Noller, J.S., Zissimos, A., Christoforou, I.C., and Bruggeman, A. (2017). A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization. Geoderma, 285: 35-49.

Dayani, M., Naderi, M., Mohammadi, J. (2010). Mapping Concentrations of Pb, Zn and Cd in Soils Using Landsat ETM+ Data in Southern Isfahan. Journal of Water and Soil, 24(2): 286-296. [in Persian]

DengWei, W., YunZhao, W. and HongRui, M.A. (2009). Review on remote sensing monitoring on contaminated plant. Remote Sens. Technol. Appl., 2:238–245.

Desavathu, R.N., Nadipena, A.R., Peddada, J.R. (2018). Assessment of soil fertility status in Paderu Mandal, Visakhapatnam district of Andhra Pradesh through Geospatial techniques. The Egyptian Journal of Remote Sensing and Space Science, 21(1): 73-81.

Elhag, M. and Bahrawi, J. (2017). Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques. Geoscientific Instrumentation, Methods and Data Systems, 6(1): 149-158.

Elharti, A., Lhissou, R., Chokmani, K., Ouzemou, J., Hassouna, M., Bachaoui, E.M. and El Ghmari, A. (2016). Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation, 50: 64-73.

Fathizad, M., Ardakani, H., Sodaiezadeh R., Kerry, R., and Taghizadeh-Mehrjardi, R. (2020). Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma, 365: https://doi.org/10.1016/j.geoderma.2020.114233.

Hanusz, Z., Tarasinska, J., and Zielinski, W. (2016). Shapiro-Wilk test with known mean. REVSTAT-Statistical Journal, 14: 89-100.

Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D. and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution, Random forests significantly improve current predictions. PloS one, 10(6): 1-26.

Islam, T., and Toor, E. (2019). Power Comparison of Autocorrelation Tests in Dynamic Models. International Econometric Review, Econometric Research Association, 11(2): 58-69.

Jiang, Y., Rao, L., Sun, K., Han, Y., and Guo, X. (2018). Spatio-temporal distribution of soil nitrogen in Poyang lake ecological economic zone (South-China). Science of the Total Environment, 626: 235-243.

Khan, S. and Abbas, A. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. Int. Congr. Model. Simul.-MODSIM, Model. Simul. Soc. Aust. New Zealand, Bright-January, 2632-2638.

Kisi, O., Karahan, M., and Sen, Z. (2006). River suspended sediment modeling using fuzzy logic approach. Hydrol Process, 20(2): 4351-4362.

Lucà, F., Conforti, M., and Robustelli, G. (2011). Comparison of GISbased gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology, 134 : 297–308.

Mirchooli, F., Kiani-Harchegani, M., Khaledi Darvishan, A., Falahatkar, S., and Sadeghi, H. (2020). Spatial distribution dependency of soil organic carbon content to important environmental variables. Ecological Indicators, 116: 1-10.

Mohammadi, A., Majnoon Hoseini, N., Moghadam, H., Oveisi, M. (2020). Alfalfa yield prediction by some vegetative indices and environmental variables in Southern Khorasan:Case study of Sarayan. Iranian Journal of field crop science, 51(1): 137-148. [in Persian]

Nitze, I., Schulthess, U., Asche, H. (2012). Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, 79: 35-40.

Norouzi, E., Bahramifar, N., Ghasempouri, M. (2010). Determination Concentration of Lead in Breast in Lactating Women in Region Industrial Zarinshahr and Effect on Infant. Journal of Isfahan Medical School, 28(112): 640-646. [in Persian]

Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B. and Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337: 1309-1319.

Periasamy, S. and Shanmugam, R.S. (2017). Multispectral and microwave remote sensing models to survey soil moisture and salinity. Land Degradation & Development, 28(4): 1412-1425.

Rahmati, M. and Hamzehpour, N. (2017). Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data. International Journal of Remote Sensing, 38(1): 123-140.

Robinson, W., Allred, M., Jones, A., Moreno, J.S., Kimball, D.E., Naugle, T.A., and Richardson, E. (2017). A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote Sensing, 9(8): 863.

Santos Silva, F., dos Santos Verçosa, J., and Falcão Tavares, A. (2020). Evaluating methods to classify sugarcane planting using convolutional neural network and random forest algorithms. International Journal of DevelopmentResearch, 10(12): 42807-42811.

Sidike, A., Zhao, S. and Wen, Y. (2014). Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra. International Journal of Applied Earth Observation and Geoinformation, 26: 156-175.

Sulistyo, B.T., Gunawan, P., Danoedoro, N. and Listyaningrum, N. (2017). Absolute Accuracy of the Erosion Model of DEM-NDVI and its Modification. International Journal of Geoinformatics, 13(2): 13-20.

Tafazoli, M., Jalilvand, H., Hojjati, S., Lamersdorf, N. (2017). The effect of simulated nitrogen deposition on soil chemical properties in maple plantation stand. Environmental Sciences, 15(2): 39-54. [in Persian]

Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., and Malone, B. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213: 15-28.

Wang, C., Wang, S. Fu, B., Li, Z., Wu, X., and Tang, Q. (2017). Precipitation gradient determines the tradeoff between soil moisture and soil organic carbon, total nitrogen, and species richness in the Loess Plateau, China. Science of the Total Environment, 575: 1538-1545.

Xu, Y., Smith, S.E., Grunwald, S. Abd-Elrahman, A., Wani, S.P. and Nair, V.D. (2018). Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. Catena, 163: 111-122.

Zhang, Y., Sui, B., Shen, H., and Ouyang, L. (2019). Mapping stocks of soil total nitrogen using remote sensing data, A comparison of random forest models with different predictors. Computers and Electronics in Agriculture, 160: 23-30.

Zhang, W.J., Li, X.K., Chen, F., and Liu, J.W. (2012). Accumulation and distribution characteristics for nitrogen, phosphorus and potassium in different cultivars of Petunia hybrida Vlim. Sci Hortic, 141: 83–90.

Zhou, T., Geng, Y., Chen, J., Liu, M., Haase, D., and Lausch, A. (2020). Mapping soil organic
carbon content using multi-source remote sensing variables in the Heihe River Basin
in China. Ecol. Indic., 114, 106288. https://doi.org/10.1016/j.ecolind.2020.106288.