Skip to main content
SpringerLink
Log in

Impact of Surface Temperature on Soil Chemical Properties Using Coupled Approach of Satellite Imagery, Gamma Test and Regression Based Models in Semi-arid Area

  • Chapter
  • First Online:
Surface and Groundwater Resources Development and Management in Semi-arid Region

Part of the book series: Springer Hydrogeology ((SPRINGERHYDRO))

  • 171 Accesses

Abstract

Temperature of land surface has crucial effect on soil natural environment by controlling soil pH, soil water retention, organic content, physical and micro-biological forms of soil. In the present study, semi-arid Guhla and Kaithal blocks of Kaithal district, Haryana state, India, have been selected for the assessment of effect of land surface temperature (LST) on the soil chemical properties. Soil sampling was done from twenty one random sites of this blocks in which nine belong to Guhla block and twelve belong to Kaithal block on 11 June 2015. Total eighteen soil chemical properties viz. soil saturation, cation exchange capacity, organic carbon, calcium carbonate, N, P, K, exchangeable sodium percentage, electrical conductivity, pH, water soluble anions viz. carbonate, bicarbonate, chloride, sulphate and water soluble cations viz. calcium, magnesium, sodium, and potassium have been determined for each sample. Spilt window technique has been used for LST determination by utilizing weather data of the location. Multiple linear regression (MLR) and multiple non-linear regression (MNLR) analysis have been used for modeling LST and soil chemical properties. Due to no correlation among the variables, gamma test has been used for selecting best input structure. Coefficient of multiple determination, multiple correlation coefficient and root mean square error (RMSE) between remote sensing (RS) based LST and soil chemical parameters, were found as 0.367, 0.606 and 2.276 °K, respectively, in MLR model. In MNLR model, dataset length was divided into 70% for training and 30% for testing. Coefficient of determination (R2) and RMSE, between RS based LST and MNLR based LST, were found as 0.861 and 3.333 °K, respectively during testing period. MNLR model has given better results in terms of coefficient of determination in comparison to MLR analysis, with overestimated values of LST.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CEC:

Cation exchange capacity

ECe:

Electrical conductivity of a saturation soil paste

EDTA:

Ethylene diamine tetra acetic

ESP:

Exchangeable sodium percentage

FVC:

Fractional vegetation cover

GT:

Gamma test

K:

Available potassium

LSE:

Land surface emissivity

LST:

Land surface temperature

MLR:

Multiple linear regression

MNLR:

Multiple non-linear regression

N:

Available nitrogen

OLI:

Operational land imager

P:

Available phosphorous

Q-Q:

Quantity versus quantity

RH:

Relative humidity

RSC:

Residual sodium carbonate

TIR:

Thermal infrared

TOA:

Top of atmosphere

WS:

Water soluble

References

  • Agalbjorn S, Koncar N, Jones AJ (1997) A note on the gamma test. Neural Comput Appl 5:131–133. https://doi.org/10.1007/BF01413858

    Article  Google Scholar 

  • Al-Temeemi AA, Harris DJ (2001) The generation of subsurface temperature profiles for Kuwait. Energy Build 33(8):837–841

    Article  Google Scholar 

  • Anh DT, Tanim AH, Kushwaha DP, Pham QB, Bui VH (2023) Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers. Int J Sedim Res 38(1):128–140. https://doi.org/10.1016/j.ijsrc.2022.08.001

    Article  Google Scholar 

  • Buchas GD (2001) Soil temperature regime. In: Smith KA, Mullins ED (eds) Soil and environmental analysis: physical methods. Marcel Dekker, New York, pp 539–594

    Google Scholar 

  • Cambardella C, Moorman TB, Novak JM, Parkin TB, Karlen DL (1994) Field-scale variability of soil properties in Central Iowa soils. Soil Sci Soc Am J 58:1501–1511

    Article  Google Scholar 

  • Changa F, Heinemann PH (2018) Optimizing prediction of human assessments of dairy odors using input variable selection. Comput Electron Agric 150:402–410

    Article  Google Scholar 

  • Chatterjee RS, Singh N, Thapa S, Sharma D, Kumar D (2017) Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. Int J Appl Earth Observat Geo Inf. https://doi.org/10.1016/j.jag.2017.02.017

  • Csiszar I, Gutman G (1999) Mapping global land surface albedo from NOAA/AVHRR data. J Geophys Res 104:6215–6228

    Article  ADS  Google Scholar 

  • Davidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440(7081):165–173

    Article  CAS  PubMed  ADS  Google Scholar 

  • DeBano LF, Conrad CE (1978) The effect of fire on nutrients in a chaparral ecosystem. Ecology 59:489–497

    Article  CAS  Google Scholar 

  • DeVries DA (1963) Thermal properties of soils, physics of plant environment. In: Van Wijk WR (ed) North Holland Publishing Company, Amsterdam, pp 210–235

    Google Scholar 

  • Dousset B, Gourmelon F (2003) Satellite multi-sensor data analysis of urban surface temperatures and land cover. Photog Rem Sens 58:43–54

    Article  Google Scholar 

  • Farouki OT (1986) Thermal properties of soil. Trans Tech Publications, Clausthal Zellerfeld, Germany

    Google Scholar 

  • Fonseca R, Zorzano-Mier M, Azua-Bustos A, Gonzalez-Silva C, Martin-Torres J (2019) A surface temperature and moisture intercomparison study of the Weather Research and Forecasting model, in-situ measurements and satellite observations over the Atacama Desert. Clim Resilience Sustain 145(722):2202–2220

    Google Scholar 

  • Gabriele C, Amanda LS, Ming P, Eric FW (2015) 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. Rem Sen Environ. https://doi.org/10.1016/j.rse.2015.09.010

  • Gadekar K, Pande CB, Rajesh J, Gorantiwar SD, Atre AA (2023) Estimation of land surface temperature and urban heat island by using google earth engine and remote sensing data. In: Pande CB, Moharir KN, Singh SK, Pham QB, Elbeltagi A (eds) Climate change impacts on natural resources, ecosystems and agricultural systems. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-031-19059-9_14

  • Geiger R (1961) The climate near the ground. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Gimeno-Garcia E, Andreu V, Rubio JL (2004) Spatial patterns of soil temperatures during experimental fires. Geoderma 118(1–2):17–38

    Article  ADS  Google Scholar 

  • Giovannini G, Lucchesi S, Giachetti M (1990) Effect of heating on some chemical parameters related to soil fertility and plant growth. Soil Sci 149:344–350

    Article  CAS  ADS  Google Scholar 

  • Ghuman BS, Lal R (1982) Temperature regime of a tropical soil in relation to surface condition and air temperature and its Fourier analysis. Soil Sci 134:133–140

    Article  ADS  Google Scholar 

  • Guenther AB, Zimmerman PR, Harley PC, Monson RK, Fall R (1993) Isoprene and monoteme emission rate variability: model evaluation and sensitivity analyses. J Geophys Res 98(7):12609–12617

    Article  ADS  Google Scholar 

  • Gupta SC, Radke JK, Larson WE (1981) Predicting temperatures of bare and residue covered soils with and without a corn crop. Soil Sci Soc Am J 45:405–412

    Article  Google Scholar 

  • Hanks RJ, Austin DD, Ondreehen WT (1971) Soil temperature estimation by a numerical method. Soil Sci Am Proc 35:665–667

    Article  Google Scholar 

  • Hasfurther VR, Burman RD (1974) Soil temperature modeling using air temperature as a driving mechanism. Trans ASAE 17:78–81

    Article  Google Scholar 

  • Jebamalar AS, Raja S, Thambi A, Sunitha BSJ (2012) Prediction of annual and seasonal soil temperature variation using artificial neural network. Indian J Radio Space Phys 41(1):48–57

    Google Scholar 

  • Jiang H, Eastman JR (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogra Info Sys 14(2):173–184

    Article  Google Scholar 

  • Jones AJ, Margetts S, Durrant P (2002) The winGammaTM user guide. Cardiff U.K., University of Wales. http://users.cs.cf.ac.uk/O.F.Rana/Antonia.J.Jones/GammaArchive/Gamma%20Software/winGamma/winGammaManual2001.pdf

  • Kang S, Kim S, Oh S, Lee D (2000) Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature. For Ecol Manage 36:173–184

    Article  Google Scholar 

  • Kaup C (2020) The optimum of heat recovery—determination of the optimal heat recovery based on a multiple non-linear regression model. J Build Eng. https://doi.org/10.1016/j.jobe.2020.101548

    Article  Google Scholar 

  • Khatry AK, Sodha MS, Malik MAS (1978) Periodic variation of ground temperature with depth. Sol Energy 20:425–427

    Article  ADS  Google Scholar 

  • Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58

    Article  Google Scholar 

  • Krarti M, Lopez-Alonzo C, Claridge DE, Kreider JF (1995) Analytical model to predict annual soil surface temperature variation. J Sol Energy Eng 117(2):91–99

    Article  Google Scholar 

  • Kumar M, Tripathi DK, Maitri V, Biswas V (2017) Impact of urbanisation on land surface temperature in Nagpur, Maharashtra. In: Sharma P, Rajput S (eds)

    Google Scholar 

  • Kumar M, Kumari A, Kushwaha DP, Kumar P, Malik A, Ali R, Kuriqi A (2020) Estimation of daily stage–discharge relationship by using data-driven techniques of a perennial river, India. Sustainability 12:7877. https://doi.org/10.3390/su12197877

  • Kushwaha DP, Kumar D (2017a) Multilayer perceptron and suspended sediment modeling: a case study. Lambert Academic Publishing, Chisinau, Republic of Moldova

    Google Scholar 

  • Kushwaha DP, Kumar D (2017b) Modeling suspended sediment concentration using multilayer feedforward artificial neural network at the outlet of the watershed. Int J Agric Eng 10(2):1–9. https://doi.org/10.15740/HAS/IJAE/10.2/1-9

  • Kushwaha DP, Kumar D (2017c) Suspended sediment modeling with continuously lagging input variables using artificial intelligence and physics based models. Int J Curr Microbiol Appl Sci 6(10):1386–1399. https://doi.org/10.20546/ijcmas.2017.610.164

  • Kushwaha DP, Kumar A (2021) Modeling of sediment yield and nutrient loss after application of pre-determined dose of top soil amendments. Pharma Innov J 10(4):1199–1206

    Google Scholar 

  • Kushwaha DP, Singh VK, Tarate SB (2017a) Land surface temperature estimation using split window approach over US Nagar district of Uttarakhand state, India. Int J Agric Eng 10(2):354–359. https://doi.org/10.15740/HAS/IJAE/10.2/354-359

  • Kushwaha DP, Singh VK, Saran B (2017b) Daily pan evaporation estimation based on heuristic, regression and climate based techniques. In: Singh VK, Paras, Ramdayal (eds) Second all India seminar on advances in engineering and technology for sustainable development, The Institution of Engineers, Pantnagar, Uttarakhand, 25–26 November 2017a, pp 31–42

    Google Scholar 

  • Kushwaha DP, Malik A, Singh SK (2019) Geographic information system for generating spatial pattern of natural streams: a case study in Nainital, India. Bull Env Pharmacol Life Sci 8:56–63

    Google Scholar 

  • Kushwaha DP, Kumar A, Chaturvedi S (2021) Determining the effectiveness of carbon-based stabilizers blends in arresting soil erosion and elevating properties of Mollisols soils of North Western Himalayas. Environ Technol Innov 23:101768. https://doi.org/10.1016/j.eti.2021.101768

    Article  CAS  Google Scholar 

  • Lafdani EK, Moghaddam NA, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydro 478:50–62. https://doi.org/10.1016/j.jhydrol.2012.11.048

    Article  Google Scholar 

  • Lahti M, Aphalo PJ, Finer L, Lehto T, Leinonen I, Mannerkoski H, Ryyppo L (2002) Soil temperature, gas exchange and nitrogen status of 5-year old Norway spruce seedlings. Tree Physio 22:1311–1316

    Article  CAS  Google Scholar 

  • Langholz H (1989) A simple model for predicting daily mean soil temperatures. J Agron Crop Sci 163:312–318

    Article  Google Scholar 

  • Lehnert M (2013) The soil temperature regime in the urban and sub-urban landscapes of olomoric Czech Republic. Morarian Geograph Rep 21(3):27–36

    Google Scholar 

  • Lettau H (1979) Determination of the thermal diffusivity in the upper layers of a natural ground cover. Soil Sci 112:173–177

    Article  ADS  Google Scholar 

  • Lloyd J, Taylor JA (1994) On the temperature dependence of soil respiration. Funct Ecol 8:315–323

    Article  Google Scholar 

  • Mairizal AQ, Awad S, Priadi CR, Hartono DM, Moersidik SS, Tazerout M, Andres Y (2020) Experimental study on the effects of feedstock on the properties of biodiesel using multiple linear regressions. Renew Energy 145:375–381

    Article  CAS  Google Scholar 

  • Malczewski J (2006) GIS based multi-criteria decision analysis: a survey of literature. Int J Geogra Inf Sys 20(7):703–726

    Article  Google Scholar 

  • Malik A, Kumar A (2018) Comparison of soft-computing and statistical techniques in simulating daily river flow: a case study in India. J Soil Water Conserv 17(2):192–199

    Article  Google Scholar 

  • Malik A, Kumar A, Kushwaha DP, Kisi O, Salih SQ, Al-Ansari N, Yaseen ZM (2019) The Implementation of a hybrid model for hilly sub-watershed prioritization using morphometric variables: case study in India. Water 11(6):1138. https://doi.org/10.3390/w11061138

    Article  Google Scholar 

  • Mallick J, Kant Y, Bharath BD (2008) Estimation of land surface temperature over Delhi using Landsat-7 ETM Plus. J Ind Geophys Union 12(3):131–140

    Google Scholar 

  • Manrique LA (1990) Estimating soil surface temperatures under different crop covers in Hawaii. Commun Soil Sci Plant Anal 21:2105–2117

    Article  Google Scholar 

  • Marill KA (2004) Advanced statistics: multiple linear regression. Acad Emerg Med 11(1):94–102. https://doi.org/10.1197/S1069-6563(03)00601-8

    Article  PubMed  Google Scholar 

  • Meikle RW, Tredway TR (1979) A mathematical model for estimating soil temperatures. Soil Sci 128:226–242

    Article  ADS  Google Scholar 

  • Mihalakakou G (2002) On estimating soil surface temperature profiles. Energy Build 34(3):251–259

    Article  Google Scholar 

  • Moghaddamnia A, Ghafari M, Piri J, Han D (2009) Evaporation estimation using support vector machines technique. Int J Eng Appl Sci 5(7):415–423

    Google Scholar 

  • Navale MM, Kashyap PS, Singh SK, Kushwaha DP, Kumar D, Kumar P (2018) Estimation of deterministic component of monthly rainfall time series: A case study for Pantnagar. Mausam 69(3):449–458. https://doi.org/10.54302/mausam.v69i3.338

  • Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Ghafari Gousheh M (2011) Assessment of input variables determination on the SVM model performance using PCA, Gamma test and forward selection techniques for monthly stream flow prediction. J Hydrol 401:177–189

    Article  Google Scholar 

  • Pahlavan-Rad MR, Dahmardeh K, Hadizadeh M, Keykha G, Mohammadnia N, Gangali M, Keikha M, Davatgar N, Brungard C (2020) Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran. CATENA 194:104715

    Article  Google Scholar 

  • Pajari B (1995) Soil respiration in poor upland site of Scots pine stand subjected to elevated temperatures and atmospheric carbon concentration. Plant Soil 168:563. https://doi.org/10.1007/BF00029369

    Article  Google Scholar 

  • Parton WJ (1984) Predicting soil temperatures in a shortgrass steppe. Soil Sci 138:93–101

    Article  ADS  Google Scholar 

  • Paul KI, Polglase PJ, Smethurst PJ, Anthony MO, Carlyle CJ, Khannaa PK (2004) Soil temperature under forests: a simple model for predicting soil temperature under a range of forest types. Agric for Meteorol 121:167–182

    Article  ADS  Google Scholar 

  • Peng F, Weng Q (2016) Consistent land surface temperature data generation from irregularly spaced Landsat imagery. Rem Sen Environ 184:175–187

    Article  ADS  Google Scholar 

  • Philip JR, De Vries DR (1957) Moisture movement in porous media under temperature gradients. Trans Am Geophys Union 38:222–232

    Article  ADS  Google Scholar 

  • Probert RJ (2000) The role of temperature in the regulation of seed dormancy and germination. In: Fenner M (ed) Seeds: the ecology of regeneration in plant communities. CABI Publishing, Wallingford, pp 261–292

    Chapter  Google Scholar 

  • Rastgou M, Bayat H, Mansoorizadeh M, Gregory AS (2020) Estimating the soil water retention curve: comparison of multiple nonlinear regression approach and random forest data mining technique. Comput Electron Agric 174:105502

    Article  Google Scholar 

  • Rahman KU, Pham QB, Jadoon KZ, Shahid M, Kushwaha DP, Duan Z, Mohammadi B, Khedher KM, Anh DT (2022) Comparison of machine learning and process-based SWAT model in simulating streamflow in the Upper Indus Basin. Appl Water Sci 12:178. https://doi.org/10.1007/s13201-022-01692-6

    Article  ADS  Google Scholar 

  • Rajesh J, Pande CB (2023) Estimation of land surface temperature for rahuri taluka, Ahmednagar District (MS, India), Using remote sensing data and algorithm. In: Pande CB, Moharir KN, Singh SK, Pham QB, Elbeltagi A (eds) Climate change impacts on natural resources, ecosystems and agricultural systems. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-031-19059-9_24

  • Reimer A, Shaykewich CF (1980) Estimation of Manitoba soil temperatures from atmospheric meteorological measurements. Can J Soil Sci 60:299–309

    Article  Google Scholar 

  • Remesan R, Shamim MA, Han D (2008) Model input data selection using gamma test for daily solar radiation estimation. Hydrol Process 22:4301–4309

    Article  ADS  Google Scholar 

  • Repo TI, Leinonen AR, Finer L (2004) The effect of soil temperature on bid phenology, chlorophyll fluorescence, carbohydrate content and cold bardiness of Norway spruce seedlings. Physio Plant 121:93–100

    Article  CAS  Google Scholar 

  • Richards LA (1954) Diagnosis and improvement of saline and alkali soils. U. S. D. A. Hand Book No. 60. Oxford & IBH Publishing Co., New Delhi

    Google Scholar 

  • Schaab G, Lenz R, Seufert G (1999) A temporal-spatial solar radiation model to improve scaling of biogenic emissions from a sparse Mediterranean pine/oak forest. Phys Chem Earth Part B 24(6):673–680

    Article  ADS  Google Scholar 

  • Singh JS, Gupta SR (1977) Plant decomposition and soil respiration and soil respiration in terrestrial ecosystems. Bot Rev 43:449–528

    Article  CAS  Google Scholar 

  • Singh VK, Prakash R, Paul R, Kumar S, Singh K (2017) Satyavan, evaluation of groundwater quality for irrigation in Gulha block of Kaithal district in Haryana. J Soil Salinity Water Q 9(2):241–248

    Google Scholar 

  • Singh VK, Prakash R, Bhat MA, Deep G, Kumar S (2018a) Evaluation of groundwater quality for irrigation in Kaithal block (Kaithal District) Haryana. Int J Chem Stud 6(2):667–672

    Google Scholar 

  • Singh VK, Singh BP, Kisi O, Kushwaha DP (2018b) Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area. Comput Electron Agric 150:205–219. https://doi.org/10.1016/j.compag.2018.04.019

    Article  Google Scholar 

  • Singh SK, Kashyap PS, Kushwaha DP, Tamta S (2020) Runoff and sediment reduction using hay mulch treatment at varying land slope and rainfall intensity under simulated rainfall condition. Int Arch Appl Sci Technol 11(3):144–155. https://doi.org/10.15515/iaast.0976-4828.11.3.144155

  • Sobrino JA, Coll C, Vicente C (1991) Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Rem Sens Environ 38(1):19–34

    Article  ADS  Google Scholar 

  • Sobrino JA, Jimenez-Munoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Rem Sens Environ 90:434–440

    Article  ADS  Google Scholar 

  • Sobrino JA, Jimenez-Munoz JC, Zarco-Tejada PJ, Sepulcre-Canto G, de Miguel E (2006) Land surface temperature derived from airborne hyperspectral scanner thermal infrared data. Remote Sens Environ 102:99–115

    Article  ADS  Google Scholar 

  • Sobrino JA, Jimenez-Munoz JC, Soria G, Ruescas AB, Danne O, Brockmann C, Ghent D, Remedios J, North P, Merchant C, Berger M, Mathieu PP, Gottsche FM (2016) Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data. Rem Sen Environ 179:149–161

    Article  ADS  Google Scholar 

  • Stangierski J, Weiss D, Kaczmarek A (2019) Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese. Eur Food Res Technol 245:2539–2547. https://doi.org/10.1007/s00217-019-03369-y

    Article  CAS  Google Scholar 

  • Stefansson A, Koncar N, Jones AJ (1997) A note on the gamma test. Neural Comput Appl 5:131–133

    Article  Google Scholar 

  • Subbaiah BV, Asija GL (1956) A rapid procedure for the estimation of available nitrogen in soil. Curr Sci 25:259

    Google Scholar 

  • Tabari H, Marofi S, Sabziparvar A (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrigat Sci 28:399–406

    Article  Google Scholar 

  • Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110:135–142

    Article  ADS  Google Scholar 

  • Tamta S, Kumar A, Kushwaha DP (2023) Potential of roots and shoots of Napier grass for arresting soil erosion and runoff of mollisols soils of Himalayas. Int Soil and Water Conserv Res. https://doi.org/10.1016/j.iswcr.2023.02.001

  • Tarate SB, Singh VK, Kushwaha DP (2017) Assessment of meteorological drought for Parbhani district of Maharashtra, India. Int J Agric Eng 10(2):260–267. https://doi.org/10.15740/HAS/IJAE/10.2/1-9

  • Tingey DT, Manning M, Gmthaus LC, Bums WF (1980) Influence of light and temperature on monoterpene emission rates from slash pine. Plant Physiol 65:797–801

    Article  CAS  Google Scholar 

  • Trangmar BB, Yost RS, Uehara G (1985) Application of geostatistics to spatial studies of soil properties. Adv Agron 38:45–94

    Article  Google Scholar 

  • Tsui APM, Jones AJ, Guedes de Oliveria A (2002) The construction of smooth models using irregular embeddings determined by Gamma test analysis. Neural Comput Applic 10(4):318–329. https://doi.org/10.1007/s005210200004

    Article  MATH  Google Scholar 

  • Toselli M, Flore JA, Marogoni B, Masia A (1999) Effects of root-zone temperature on nitrogen accumulation by non-breeding apple trees. J Hort Sci Biotech 74:118–124

    Article  Google Scholar 

  • Varley J (1971) A textbook of soil chemical analysis. In: Hesse PR, Murray J (eds) London, p 520

    Google Scholar 

  • Walkley A, Black IA (1934) An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci 37:29–37

    Article  CAS  ADS  Google Scholar 

  • Wang Q, Zhang D, Zhou W, He X, Wang W (2020) Urbanization led to a decline in glomalin-soil-carbon sequestration and responsible factors examination in Changchun, Northeastern China. Urban for Urban Green 48:126506

    Article  Google Scholar 

  • Weih M, Karlson S (1999) The nitrogen economy of mountain birch seedlings. Implicat Winter Survival J Ecol 87:211–219

    Google Scholar 

  • Weng Q, Lu D, Jacquelyn S (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Rem Sens Environ 89:467–483

    Article  ADS  Google Scholar 

  • Wierenga JJ, Nielsen DR, Hagan RM (1969) Thermal properties of a soil based upon field and laboratory measurements. Soil Sci Soc Am Proc 33:354–360

    Article  Google Scholar 

  • Zhao S, Qin Q, Yang Y, Xiong YJ (2009) Comparison of two split-window methods for retrieving land surface temperature from MODIS data. J Earth Syst Sci 118(4):345–353. https://doi.org/10.1007/s12040-009-0027-4

    Article  ADS  Google Scholar 

  • Zheng X, Jiang Z, Ying Z, Song J, Chen W, Wang B (2020) Role of feedstock properties and hydrothermal carbonization conditions on fuel properties of sewage sludge-derived hydrochar using multiple linear regression technique. Fuel 271:117609

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Authors are very thankful to departmental staffs of soil science of Chaudary Charan Singh Haryana Agricultural University. They have provided necessary assistance in onerous work of soil sampling in one day and its analysis. We are also grateful for Govind Ballabh Pant University of Agriculture and Technology, Pantnagar for providing us computer laboratory for entire research work.

Author information

Authors and Affiliations

  1. Department of Soil Science, Punjab Agricultural University, Ludhiana, 141004, Punjab, India

    Vijay Kant Singh

  2. Department of Soil Science, Chaudary Charan Singh Haryana Agricultural University, Hisar, 125004, Haryana, India

    Ram Prakash

  3. College of Agriculture Engineering, Ara (Bhojpur), Under Bihar Agricultural University, Sabour, Bhagalpur, 813210, Bihar, India

    Daniel Prakash Kushwaha

Authors
  1. Vijay Kant Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ram Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Prakash Kushwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Prakash Kushwaha .

Editor information

Editors and Affiliations

  1. Indian Institute of Tropical Meteorology, Pune, India

    Chaitanya B. Pande

  2. Dr. Rajendra Prasad Central Agricultural University, Bihar, India

    Manish Kumar

  3. Indian Agricultural Research Institute, New Delhi, India

    N. L. Kushwaha

Ethics declarations

None.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, V.K., Prakash, R., Kushwaha, D.P. (2023). Impact of Surface Temperature on Soil Chemical Properties Using Coupled Approach of Satellite Imagery, Gamma Test and Regression Based Models in Semi-arid Area. In: Pande, C.B., Kumar, M., Kushwaha, N.L. (eds) Surface and Groundwater Resources Development and Management in Semi-arid Region. Springer Hydrogeology. Springer, Cham. https://doi.org/10.1007/978-3-031-29394-8_18

Download citation

Publish with us

Policies and ethics

Search

Navigation