برآورد کمی فلزات روی و سرب در خاک با استفاده از تجزیه و تحلیل چند متغیره و فن سنجش از راه دور

Abdollahi S, Delavar MA and Shekari P, 2012. Spatial distribution mapping of Pb, Zn and Cd and soil pollution assessment in Anguran area of Zanjan Province. Journal of Water and Soil 26(6):1410-1420. (In Persian with English abstract)

Ali I, Greifeneder F, Stamenkovic J, Neumann M and No-tarnicola C, 2015. Review of machine learning approaches forbiomass and soil moisture retrievals from remote sensing data. Remote Sensing 7(12): 221–236.

Askari MS, McCarthy T, Magee A and Murphy DJ, 2019. Evaluation of grass quality under different soil management scenarios using remote sensing techniques. Remote Sensing 11(15), 1835.‏

Bolyn C, Michez A, Gaucher P, Lejeune Ph and Bonnet S, 2018. Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery. Biotechnology, Agronomy, Society and Environment  22(3): 172-187.

Choe E, Kim KW, Bang S, Yoon IH and Lee KY, 2009. Qualitative analysis and mapping of heavy metals in an abandoned Au-Ag mine area using NIR spectroscopy. Environmental Geology 58(3): 477–482.

Clevers JGPW and Gitelson A, 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoformation 23(1):344-351.

Dash J and Curran PJ, 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25(23): 5403-5413.

Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun EC and McMurtrey JE, 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74(2): 229-239.

De Sousa C, Hilker T, Waring R, De Moura Y and Lyapustin A, 2017. Progress in remote sensing of photosynthetic activity over the Amazon Basin. Remote Sensing 9(1): 48-60.

Fard RS and Matinfar HR, 2016. Capability of vis-nir spectroscopy and landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran). Arabian Journal of Geosciences 9(20):1-14.

Fu XL and Wang QJ, 2017. Inversion analysis of heavy metal pollution in soil in mining disturbed areas based on remote sensing data: A case study of lanping Zn. Journal of Residuals Science and Technology 14(3): 85-93.

Gilmour J and Kittrick J, 1979. Solubility and equilibria of Zinc in a flooded soil. Soil Science Society of America Journal 43(5): 890-892.

Gitelson AA, Gritz Y and Merzlyak MN, 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.  Journal of Plant Physiology 160(3): 271-282.

Goel NS and Qin W, 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation. Remote Sensing 10(4): 309-347.

Gholizadeh A, Boruvka L, Vasat R and Saberioon MM, 2015. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Research 10 (4): 218–227. (In Persian with English abstract)

Haboudane D, Miller JR, Patery E, Zarco-Tejada PJ and Strachan IB, 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90: 337 – 352.

Hamidi Nehrani S, Askari MS, Saadat S, Delavar MA, Taheri M and Holden NM, 2020. Quantification of soil quality under semi-arid agriculture in the northwest of Iran. Ecological Indicators. 108:105770-105780.

Hill MJ, 2013. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment 137: 94-111.

Hollberg JL and Schellberg J, 2017. Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices. Remote Sensing 9(1): 81-94.

Huete AR, Didan K, Miura, T, Rodriguez EP, Gao X and Ferreira LG, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195–213.

Kaya Z, 2006. Pollution. Pp. 1343-1346. In: Lal R, (Ed.). Encyclopedia of Soil Science, Second Edition 2. Volume Set. Taylor & Francis Group. New York, USA.

Khalid S, Shahid M, Niazi NK, Murtaza B, Bibi I  and Dumat C, 2017. A comparison of technologies for remediation of heavy metal contaminated soils. Journal of Geochemical Exploration 182: 247–268.

Lu P, Bai S and Casagli N, 2014. Investigating spatial patterns of persistent scatterer interferometry point targets and landslide occurrences in the Arno river basin. Remote Sensing 6(8):6817-6843.

Malley DF and Williams PC, 1997. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environmental Science and Technology 31(12): 3461–3467.

Marsett RC, Qi J, Heilman P, Biedenbender SH, Watson MC, Amer S, Weltz M, Goodrich D and Marsett R, 2006. Remote sensing for grassland management in the arid Southwest. Rangeland Ecology and Management 59: 530–540.

Navarro-Pedreño J, Gómez I, Almendro-Candel M and Meléndez-Pastor I, 2008. Heavy metals in Mediterranean soils. Pp. 161-176. In: Dominguez J, (Ed.). Soil Contamination Research Trends. New York, USA: Nova Science Publishers, Inc.

Nellis MD and Briggs JM, 1992. Transformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas. Transactions of the Kansas. Academy of Sciences 1903 (95): 93–99.

Pouget M, Madeira J, Le Floch E and Kamal S, 1990. Spectral characteristics of sandy surfaces in the northwestern coast region of Egypt: Application to SPOT satellite data. In: International Conference of Characterization and Monitoring of Terrestrial Environments in Arid and Tropical Regions. 4–6 December. ORSTOM, Colloquiums and Seminars Collection, Paris, France.

Pinheiro E, Ceddia M, Clingensmith C, Grunwald S and Vasques G, 2017. Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central amazon. Remote Sensing 9(4): 293-301.

Ramoelo A, Skidmore AK, Azongcho M, Schlerf M, Mathieu R and Heitkonig I, 2012. Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19(1):151-162.

Rondeaux G, Steven M and Baret F, 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55(2): 95-107.

Sims D and Gamon JA, 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 337-354.

Smith MO, Ustin SL, Adams JB and Gillespie AR, 1990. Vegetation in deserts: I. Regional measure of abundance from multispectral images. Remote Sensing of Environment 31: 1–26.

Sposito G, 2008. The Chemistry of Soils. 2^nd Ed. New York. Oxford University Press. 344 p.

Tucker CJ, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150.

Yang K, Pinker RT, Koike YMT, Wonsick MM, Cox SJ,  Zhang YC and Stackhouse P, 2008. Evaluation of satellite estimates of downward shortwave radiation over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres  113:207-219.

Yari Y, Momtaz HR and Taheri M, 2016. Spatial distribution of some heavy metals in soils of Zanjan industrial region. Water and Soil Science 26(4.1): 223-236. (In Persian with English abstract)

Wang F, Gao J and Zha Y, 2018. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges. Journal of Photogrammetry and Remote Sensing 136: 73–84.

Westerman REL, 1990. Soil Testing and Plant Analysis, SSSA, Madison, Wisconsin, USA.

Xiao X, Zhang Q, Braswell B, Urbanski S, Boles S, Wofsy SC, Moore B and Ojima D, 2004. Modeling gross primary production of a deciduous broadleaf forestusing satellite images and climate data. Remote Sensing of Environment 91: 256–270.