Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area

Abstract Soil temperature is a noteworthy factor, particularly in farming and land treatment of organic wastes, since development of organic system is firmly controlled by soil temperature. Furthermore, soil temperature has impacts on the physical, substance, and microbiological forms that occur in soil. In this study, for estimation of multi-depth soil temperature, the most reliable combination of input variables for model development was achieved using gamma test technique. The most suitable training and testing data length i.e. training data set 11,529 (01 January 1975 to 25 July 2006; 81%) and testing data set 2716 (26 July 2006 to 31 December 2013; 19%) of the most efficient input variables were determined by means of M-test technique. Multi-depth soil temperature model was achieved by means of multiple linear regression (MLR), multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) techniques. After comparing results of all models, it was found that the ANFIS technique with generalized bell membership function had the best performing results when compared to MLR and MLP for all cases i.e. 5 cm, 10 cm and 20 cm depths of soil. Potential of gamma test technique was evaluated by means of sensitivity analysis. The spatial variation of land surface temperature (LST) was assessed by remote sensing (RS) and GIS technique and also the qualitative and quantitative performances were evaluated for determining the reliable and accurate spatial variation of LST. In qualitative evaluation, it was observed that the split-window (SW) approach overestimated the LST with coefficient of determination 0.8654. According to the quantitative evaluation based on root mean square error, correlation coefficient and coefficient of efficiency values between observed and estimated LST, the spatial variation of LST was found to be accurate over the Udaipur City, Rajasthan.

[1]  S. Jones,et al.  On the effective measurement frequency of TDR in dispersive and non-conductive dielectric materials , 2005 .

[2]  W. E. Larson,et al.  Predicting Temperatures of Bare and Residue Covered Soils With and Without a Corn Crop , 1981 .

[3]  Fares Laouacheria,et al.  Comparison of WBNM and HEC-HMS for Runoff Hydrograph Prediction in a Small Urban Catchment , 2015, Water Resources Management.

[4]  Pablo J. Zarco-Tejada,et al.  Land surface temperature derived from airborne hyperspectral scanner thermal infrared data , 2006 .

[5]  A. Jebamalar,et al.  Prediction of annual and seasonal soil temperature variation using artificial neural network , 2012 .

[6]  Jose C. Principe,et al.  Neural and adaptive systems : fundamentals through simulations , 2000 .

[7]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[8]  Hossein Tabari,et al.  Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region , 2011 .

[9]  G. Buchan,et al.  Soil Temperature Regime , 2000 .

[10]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[11]  M. Fenner,et al.  The role of temperature in the regulation of seed dormancy and germination. , 2000 .

[12]  Sinkyu Kang,et al.  Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature , 2000 .

[13]  William J. Parton,et al.  PREDICTING SOIL TEMPERATURES IN A SHORTGRASS STEPPE , 1984 .

[14]  S. Garba,et al.  A comparison of ANFIS and ANN-based models in river discharge forecasting , 2014 .

[15]  M. Weih,et al.  The nitrogen economy of mountain birch seedlings: implications for winter survival , 1999 .

[16]  Nadn Napagoda,et al.  Artificial Neural Network Approach for Modeling of Soil Temperature: A Case Study for Bathalagoda Area , 2013 .

[17]  Ozgur Kisi,et al.  Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques , 2017, Theoretical and Applied Climatology.

[18]  Jafar Habibi,et al.  Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature , 2016, Comput. Electron. Agric..

[19]  Dawei Han,et al.  Evaporation Estimation Using Support Vector Machines Technique , 2008 .

[20]  Qihao Weng,et al.  Consistent land surface temperature data generation from irregularly spaced Landsat imagery , 2016 .

[21]  Nai-Jia Guo,et al.  Spatiotemporal modeling of monthly soil temperature using artificial neural networks , 2013, Theoretical and Applied Climatology.

[22]  Sharad K. Jain,et al.  Development of Integrated Sediment Rating Curves Using ANNs , 2001 .

[23]  José A. Sobrino,et al.  The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters , 2009 .

[24]  Eric F. Wood,et al.  Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS , 2015 .

[25]  O. Kisi,et al.  Prediction of long‐term monthly air temperature using geographical inputs , 2014 .

[26]  R. Lal,et al.  TEMPERATURE REGIME OF A TROPICAL SOIL IN RELATION TO SURFACE CONDITION AND AIR TEMPERATURE AND ITS FOURIER ANALYSIS , 1982 .

[27]  Hong Jiang,et al.  Application of fuzzy measures in multi-criteria evaluation in GIS , 2000, Int. J. Geogr. Inf. Sci..

[28]  Ozgur Kisi,et al.  Modeling soil temperatures at different depths by using three different neural computing techniques , 2015, Theoretical and Applied Climatology.

[29]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[30]  N. Khalili,et al.  Deciding the Embedding Nonlinear Model Dimensions and Data Size prior to Daily Reference Evapotranspiration Modeling , 2010 .

[31]  A. Ahmadi,et al.  Daily suspended sediment load prediction using artificial neural networks and support vector machines , 2013 .

[32]  B. Pajari.  Soil respiration in a poor upland site of Scots pine stand subjected to elevated temperatures and atmospheric carbon concentration , 2004, Plant and Soil.

[33]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[34]  Nenad Koncar,et al.  A note on the Gamma test , 1997, Neural Computing & Applications.

[35]  Dawei Han,et al.  Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction , 2011 .

[36]  L. Finér,et al.  Soil temperature, gas exchange and nitrogen status of 5-year-old Norway spruce seedlings. , 2002, Tree physiology.

[37]  A. Reimer,et al.  Estimation of Manitoba soil temperatures from atmospheric meteorological measurements. , 1980 .

[38]  J. Lloyd,et al.  On the temperature dependence of soil respiration , 1994 .

[39]  J. Singh,et al.  Plant decomposition and soil respiration in terrestrial ecosystems , 1977, The Botanical Review.

[40]  J. Mallick,et al.  Estimation of land surface temperature over Delhi using Landsat-7 ETM+ , 2008 .

[41]  X. Wen,et al.  A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region , 2014 .

[42]  H. Langholz.  A Simple Model for Predicting Daily Mean Soil Temperatures , 1989 .

[43]  P. Hosseinzadeh Talaee,et al.  Daily soil temperature modeling using neuro-fuzzy approach , 2014 .

[44]  Dawei Han,et al.  Model data selection using gamma test for daily solar radiation estimation , 2008 .

[45]  Manish Kumar,et al.  Impact of Urbanisation on Land Surface Temperature in Nagpur, Maharashtra , 2017 .

[46]  Mehmet Bilgili,et al.  Regional Assessment of Monthly Soil Temperatures in the Aegean Region of Turkey , 2012 .

[47]  M. Toselli,et al.  Effects of root-zone temperature on nitrogen accumulation by non-bearing apple trees , 1999 .

[48]  Carsten Brockmann,et al.  Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data , 2016 .

[49]  Dheeraj Kumar,et al.  Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[50]  José A. Sobrino,et al.  Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5 , 1991 .

[51]  E. Davidson,et al.  Temperature sensitivity of soil carbon decomposition and feedbacks to climate change , 2006, Nature.

[52]  Mehmet Bilgili,et al.  Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models , 2012, Environmental Monitoring and Assessment.

[53]  Keryn I. Paul,et al.  Soil temperature under forests: a simple model for predicting soil temperature under a range of forest types , 2004 .