A review of remote sensing vegetation indices in the land cover assessment

Document Type : Review Article

Author

Associate Professor/ Department of Natural Resources and Environment, Branch Bushehr, Islamic Azad University, Bushehr, Iran

Abstract

Introduction
Remote sensed information on growth, vigor, and dynamics from terrestrial vegetation can provide useful insights for applications in environmental monitoring, biodiversity conservation, agriculture, forestry, urban green infrastructures, and other related fields. Specifically, these types of information applied to agriculture provide not only an objective basis (depending on resolution) for the macro- and micro-management of agricultural production but also on many occasions the necessary information for yield estimation of crops. Vegetation indices (VIs) obtained from the vegetation canopy in remote sensing are simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indicators are used in remote sensing applications and satellite systems. To date, there is no unified mathematical expression that defines all VIs due to the complexity of different combinations of light spectra, instrumentation, platforms, and resolutions used. Therefore, special algorithms have been developed for different applications according to the specific mathematical expressions in the range of the visible light radiation spectrum, mainly the green spectrum region, from vegetation, and invisible spectra to quantitatively determine the level of vegetation cover. In this article, the spectral characteristics of vegetation and vegetation indices, the advantages and disadvantages of various developed indices, and their application are discussed according to the characteristics of vegetation, environment, and accuracy of implementation.
Materials and Methods
Remote sensing of vegetation is primarily performed by getting the electromagnetic wave reflectance data from canopies utilizing passive sensors. It is well known that the reflectance of light spectra from plants changes with plant sort, water substance inside tissues, and other natural components. The reflectance from vegetation to the electromagnetic range (spectral reflectance or emanation characteristics of vegetation) is decided by chemical and morphological characteristics of the surface of organs or clears out. Most applications for inaccessible detecting of vegetation are based on the taking after light spectra: (i) the bright locale (UV), which goes from 10 fr 380 nm; (ii) the apparent spectra, which are composed of the blue (450–495 nm), green (495−570 nm), and ruddy (620–750 nm) wavelength districts; and (iii) the close and mid-infrared band (850–1700 nm). The emissivity rate of the surface of takes off (equivalent to the absorptivity within the warm waveband) of a completely developed green arrange.
Results and Discussion
Many studies have constrained this translation by extricating vegetation data utilizing person light spectra groups or a bunch of single groups for information investigation. Hence, analysts regularly combine the information from near-infrared (0.7–1.1 m) and ruddy (0.6–0.7 m) groups in numerous ways concurring with their particular targets. These sorts of combinations display many disadvantages (e.g., need for affectability) by employing a single or restricted gathering of groups to detect, for case, vegetation biomass. These impediments are especially apparent when attempting to apply these sorts of VI on heterogeneous canopies, such as green tree ranches. A blended combination of soils, weeds, and cover crops within the interrow. The plants of intrigued make the segregation locales of intrigued and extraction of straightforward VI exceptionally troublesome, particularly, when the vegetation of intrigued has distinctive VIs due to spatial inconstancy, or VIs compared to other vegetation (weeds and cover edit), which can be compared to those of intrigued. The last mentioned will complicate imaging denoising and sifting forms. A few picture examination procedures and calculations have been created to go around these issues, which can be depicted afterward. Indeed in spite of the fact that there are numerous contemplations as portrayed sometime recently, the development of a straightforward VI calculation seems numerous times to render basic and compelling apparatuses to degree vegetation status on the surface of the soil. Vegetation data from remotely detected pictures is primarily translated by contrasts and changes within the green clears out from plants and canopy ghastly characteristics. The foremost common approval preparation is through coordinate or backhanded relationships between VIs gotten and the vegetation characteristics of intrigued measured in situ, such as vegetation cover, leaf area index (LAI), biomass, development, and vigor evaluation. More set-up strategies are utilized to evaluate VIs utilizing coordinate and georeferenced strategies by checking sentinel plants to be compared with VIs gotten from the same plants for calibration purposes.
Conclusion
Basic vegetation records combining obvious and near-infrared groups have essentially moved forward the affectability of green vegetation discovery. Diverse situations have their variable and complex characteristics that must be considered when utilizing distinctive plant lists. Hence, each vegetation list has its claim definition of green vegetation, its reasonableness for particular applications, and a few restricting variables. Subsequently, for commonsense applications, the choice of a particular vegetation file ought to be done carefully by considering and comprehensively analyzing the points of interest and confinements of existing vegetation records and after that combining them for application in a particular environment. In this way, the utilization of plant markers can be custom-fitted to particular applications, instruments, and stages. With the advancement of hyperspectral and multispectral further detecting innovation, it is conceivable to create unused plant markers that grow investigative areas.

Keywords

Main Subjects

References

Amiri, F. (2021). Carbon storage potential of avicennia marina as South influenced by soil factors in National Park Nayband, Coast of Iran. Acta Ecologica Sinica, 41(6), 566-574.
Amiri, F., & Nateghi S. (2023). Lands cover classification of Bushehr Province using Landsat-8 and MODIS images. Water and Soil Management and Modelling, 3(2), 143-156. [In Persian]
Amiri, F., Rahdari, V., Maleki Najafabadi, S., Pradhan, B., & Tabatabaei, T. (2014). Multi-temporal landsat images based on eco-environmental change analysis in and around Chah Nimeh reservoir, Balochestan (Iran). Environmental Earth Sciences, 72(3), 801-809.
Amiri, F., & Tabatabaie, T. (2020). The influence of green spaces on land surface temperature and humidity of the surrounding environment in Bushehr city. Environmental Sciences, 18(3), 184-205. [In Persian]
Amiri, F., & Tabatabaie, T. (2021). Classification and analysis of land use changes in urban environments using multi-temporal landsat images: a case study of Bushehr. Land Management, 9(1), 167-186. [In Persian]
Amiri, F., & Yeganeh, H. (2012). Evaluation of vegetation indices for preparing vegetation cover percentage in semi-arid lands of central iran (case study: Ghareh Aghaj Watershed). Journal of Range and Watershed Managment, 65(2), 175-189. [In Persian]
Anda, A. (2009). Irrigation timing in maize by using the crop water stress index (CWSI). Cereal Research Communications, 37(4), 603-610.
Arkebauer, T.J. (2005). Leaf radiative properties and the leaf energy budget. Micrometeorology in Agricultural Systems, 47, 93-103.
Ashburn, P. (1979). The vegetative index number and crop identification. NASA Johnson Space Center Proc of Tech Sessions, 1-2, 19800007243
Badhwar, G. (1981). The use of parameters to separate and identify spring small grains. In: Proceedings of the Quarterly Technical Interchange Meeting NASA Johnson Space Flight Center, Houston, Tex, USA.
Bannari, A. (1994). High spatial and spectral resolution remote sensing for the management of the urban environment. In: First Int. Airborne Remote Sensing Conference and Exhibition, Strasburg, France. Pp. 247-260.
Bannari, A., Asalhi, H., & Teillet, P.M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. In: IEEE International geoscience and remote sensing symposium. IEEE. doi:3053-3055.3010.1109/IGARSS.1989.576128
Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2), 161-173.
Baret, F., Jacquemoud, S., & Hanocq, J. (1993). The soil line concept in remote sensing. Remote Sensing Reviews, 7(1), 65-82.
Batten, G. (1998). Plant analysis using near infrared reflectance spectroscopy: the potential and the limitations. Australian Journal of Experimental Agriculture, 38(7), 697-706.
Berni, J.A.J., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Fereres, E., & Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113(11), 2380-2388.
Birth, G.S., & McVey, G.R. (1968). Measuring the color of growing turf with a reflectance spectrophotometer 1. Agronomy Journal, 60(6), 640-643.
Blackburn, G.A. (1998). Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273-285.
Broge, N.H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156-172.
Burns, D.A., & Ciurczak, E.W. (2007). Handbook of near-infrared analysis. 4th Edition: CRC press, 834 pages.
Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711-722.
Chappelle, E.W., Kim, M.S., & McMurtrey, J.E. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239-247.
Chen, J.M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229-242.
Crippen, R.E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34(1), 71-73.
Cruden, B.A., Prabhu, D., & Martinez, R. (2012). Absolute radiation measurement in venus and mars entry conditions. Journal of Spacecraft and Rockets, 49(6), 1069-1079.
Datt, B. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111-121.
Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741-2759.
Daughtry, C.S. (2001). Discriminating crop residues from soil by shortwave infrared reflectance. Agronomy Journal, 93(1), 125-131.
Daughtry, C.S.T., Walthall, C.L., Kim, M.S., De Colstoun, E.B., & McMurtrey, J.E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229-239.
Demetriades-Shah, T.H., Steven, M.D., & Clark, J.A. (1990). High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33(1), 55-64.
Elsheikh, R., Mohamed Shariff, A.R.B., Amiri, F., Ahmad, N.B., Balasundram, S.K., & Soom, M.A.M. (2013). Agriculture land suitability evaluator (alse): a decision and planning support tool for tropical and subtropical crops. computers and electronics in agriculture, 93, 98-110.
Elvidge, C.D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54(1), 38-48.
Fuentes, S., De Bei, R., Pech, J., & Tyerman, S. (2012). Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrigation Science, 30(6), 523-536.
Gago, J., Douthe, C., Coopman, R.E., Gallego, P.P., Ribas-Carbo, M., Flexas, J., Escalona, J., & Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9-19.
Gamon, J., & Surfus, J. (1999). Assessing leaf pigment content and activity with a reflectometer. The New Phytologist, 143(1), 105-117.
Gamon, J.A., Peñuelas, J., & Field, C.B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35-44.
Gitelson, A., & Merzlyak, M.N. (1994). Spectral reflectance changes associated with autumn senescence of aesculus hippocastanum l. and acer platanoides l. leaves. spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3), 286-292.
Gitelson, A.A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161(2), 165-173.
Gitelson, A.A., Kaufman, Y.J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76-87.
Gitelson, A.A., Keydan, G.P., & Merzlyak, M.N. (2006). Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33(11), L11402.
Gitelson, A.A., & Merzlyak, M.N. (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 148(3), 494-500.
Gitelson, A.A., Merzlyak, M.N., & Chivkunova, O.B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.
Goel, N.S., & Qin, W. (1994). Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews, 10(4), 309-347.
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., & Strachan, I.B. (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(3), 337-352.
Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2), 416-426.
Haxeltine, A., & Prentice, I. (1996). A general model for the light-use efficiency of primary production. Functional Ecology, 551-561.
Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P., & Friborg, T. (2015). Estimating evapotranspiration with thermal UAV data and two source energy balance models. Hydrology & Earth System Sciences Discussions, 12(8), 7469-7502.
Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., & Pesonen, L. (2013). Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing, 5(10), 5006-5039.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1), 195-213.
Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
Hunt, E.R., Rock, B.N. (1989). Detection of changes in leaf water content using Near and Middle Infrared reflectances. Remote Sensing of Environment, 30(1), 43-54.
Nourqolipour, R., Shariff, A.R.B.M., Balasundram, S.K., Ahmad, N.B., Sood, A.M., Buyong, T., & Amiri, F. (2015a). A GIS-based model to analyze the spatial and temporal development of oil palm land use in Kuala Langat district, Malaysia. Environmental Earth Sciences, 73(4), 1687-1700.
Nourqolipour, R., Shariff, A.R.B.M., Ahmad, N.B., Balasundram, S.K., Sood, A.M., Buyong, T., & Amiri, F. (2015b). Multi-objective-based modeling for land use change analysis in the South West of Selangor, Malaysia. Environmental Earth Sciences, 74(5), 4133-4143.
Idso, S.B., Jackson, R.D., Pinter, P.J., Reginato, R.J., & Hatfield, J.L. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24, 45-55.
Jackson, R.D. (1980). Hand-held radiometry: a set of notes developed for use at the workshop on hand-held radiometry, phoenix, ariz. Agricultural Research (Western Region), Science and Education Administration, U.S. Department of Agriculture, 66 pages.
Jackson, R.D. (1983). Spectral indices in N-Space. Remote Sensing of Environment, 13(5), 409-421.
Jordan, C.F. (1969). Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 50(4), 663-666.
Kanemasu, E.T., Hellman, J.L., Bagley, J.O., & Powers, W.L. (1977). Using landsat data to estimate evapotranspiration of winter wheat. Environmental Management, 1(6), 515-520.
Karnieli, A., Agam, N., Pinker, R.T., Anderson, M., Imhoff, M.L., Gutman, G.G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations. Journal of Climate, 23(3), 618-633.
Kaufman, Y.J., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2), 261-270.
Kauth, R.J., & Thomas, G. (1976). The tasselled capa graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In: LARS symposia. p 159.
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91-100.
Lebourgeois, V., Chopart, J.L., Bégué, A., Le & Mézo, L. (2010). Towards using a thermal infrared index combined with water balance modelling to monitor sugarcane irrigation in a tropical environment. Agricultural Water Management, 97(1), 75-82.
Li, B., Liu, R., Liu, S., Liu, Q., Liu, F., & Zhou, G. (2012). Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system. Transactions of the Chinese Society of Agricultural Engineering, 28(13), 160-165.
Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S.L., & Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111-123.
Lichtenthaler, H.K., Lang, M., Sowinska, M., Heisel, F., & Miehé, J.A. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5), 599-612.
Liu, H.Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457-465.
Louhaichi, M., Borman, M.M., & Johnson, D.E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65-70.
Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plümer, L., Steiner, U., & Oerke, E.C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21-30.
Mahlein, A.K., Oerke, E.C., Steiner, U., & Dehne, H.W. (2012). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1), 197-209.
Major, D., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11(5), 727-740.
Martynenko, A., Shotton, K., Astatkie, T., Petrash, G., Fowler, C., Neily, W., & Critchley, A.T. (2016). Thermal imaging of soybean response to drought stress: the effect of Ascophyllum nodosum seaweed extract. Springerplus, 5(1), 1-14.
McFeeters, S.K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432.
McMurtrey, J.E., Chappelle, E.W., Kim, M.S., Meisinger, J.J., & Corp, L.A. (1994). Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47(1), 36-44.
McNairn, H., & Protz, R. (1993). Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper. Canadian Journal of Remote Sensing, 19(2), 152-159.
Merzlyak, M.N, Gitelson, A.A., Chivkunova, O.B., & Rakitin, V.Y. (1999). Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135-141.
Misra, P., & Wheeler, S. (1977). Landsat data from agricultural sites: crop signature analysis. In: ERIM Proc. of the 11th Intern. Symp. on Remote Sensing of Environment, 2, 1473-1482.
Musick, H.B., & Pelletier, R.E. (1988). Response to soil moisture of spectral indexes derived from bidirectional reflectance in thematic mapper wavebands. Remote Sensing of Environment, 25(2), 167-184.
Nourqolipour, R., Mohamed Shariff, A.R.B., Balasundram, S.K., Ahmad, N.B., Sood, A.M., Buyong, T., & Amiri, F. (2015a). A GIS-based model to analyze the spatial and temporal development of oil palm land use in Kuala Langat district, Malaysia. Environmental Earth Sciences, 73(4), 1687-1700.
Nourqolipour, R., Shariff A.R.B.M., Ahmad, N.B., Balasundram, S.K., Sood, A.M., Buyong, T, & Amiri, F. (2015b). Multi-objective-based modeling for land use change analysis in the South West of Selangor, Malaysia. Environmental Earth Sciences, 74(5), 4133-4143.
Oerke, E.C., Mahlein, A.K., & Steiner, U. (2014). Proximal Sensing of Plant Diseases. In: Gullino, M.L., Bonants, P.J.M. (eds) Detection and Diagnostics of Plant Pathogens. Springer Netherlands, Dordrecht, pp 55-68.
O'Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., & Howell, T.A. (2011). Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 98(10), 1523-1535.
Pearson, R.L., & Miller, L.D. (1972). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Remote Sensing of Environment, VIII: 1355.
Pinty, B., & Verstraete, M.M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1), 15-20.
Plummer, S. (1994). The angular vegetation index: an atmospherically resistant index for the second along track scanning radiometer (ATSR-2). In: Proc. Sixth Int. Symp. Physical Measurements and Signatures in Remote Sensing, Val d'Isere, France.
Prashar, A., & Jones, H.G. (2016). Assessing drought responses using thermal infrared imaging. In: Environmental Responses in Plants. Springer, pp 209-219.
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.
Quan, Z., Xianfeng, Z., & Miao, J. (2011). Eco-environment variable estimation from remote sensed data and eco-environment assessment: models and system. Acta Botanica Sinica, 47, 1073-1080.
Rahim, H.R.B.A., Lokman, M.Q.B., Harun, S.W., Hornyak, G.L., Sterckx, K., Mohammed, W.S., & Dutta, J. (2016). Applied light-side coupling with optimized spiral-patterned zinc oxide nanorod coatings for multiple optical channel alcohol vapor sensing. Journal of Nanophotonics, 10(3), 036009.
Ren-hua, Z., Rao, N., & Liao, K. (1996). Approach for a vegetation index resistant to atmospheric effect. Journal of Integrative Plant Biology, 38(1), 53-62.
Richardson, A.J., & Wiegand, C. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
Roujean, J.L., & Breon, F.M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375-384.
Rouse, J.J., Haas, R.H., Deering, D., Schell, J., & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA Special Publication 351, 309.
Rouse, J.J.W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA Technical Reports Server, Tex, USA, 1974.
Ruimy, A., Kergoat, L., & Bondeau, A. (1999). Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light‐use efficiency. Global Change Biology, 5(1), 56-64.
Serrano, L., Peñuelas, J., & Ustin, S.L. (2002). Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment, 81(2), 355-364.
Sims, D.A., & Gamon, J.A. (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(2), 337-354. doi:10.1016/S0034-4257(02)00010-X
Sishodia, R.P., Ray, R.L., & Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136.
Smith, R., Adams, J., Stephens, D., & Hick, P. (1995). Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research, 46(1), 113-125.
Sripada, R.P., Heiniger, R.W., White, J.G., Weisz, R. (2005). Aerial color infrared photography for determining late‐season nitrogen requirements in corn. Agronomy Journal, 97(5), 1443-1451.
Tabatabaie, T., & Amiri, F. (2019). Multi-Temporal Assessment of mangrove forests change in the coastal areas of Bushehr region based on landsat satellite imagery. Iranian Journal of Applied Ecology, 8(3), 45-62. [In Persian]
Tanre, D.D. (1990). Description of a computer code to simulate the satellite signal in the solar spectrum: The 5s code. International Journal of Remote Sensing, 11(1), 659-668.
Tian, Q., & Min, X. (1998). Advances in study on vegetation indices. Advances in Earth Science, 13(4), 327-333.
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
Van Genderen, J.L. (2011). Advances in environmental remote sensing: sensors, algorithms, and applications. CRC Press, Taylor & Francis Group, Boca Raton, USA, 4(5), 556 pp.
Verrelst, J., Schaepman, M.E., Koetz, B., & Kneubühler, M. (2008). Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 112(5), 2341-2353.
Vogelmann, J., Rock, B., & Moss, D. (1993). Red edge spectral measurements from sugar maple leaves. Remote Sensing, 14(8), 1563-1575.
Wang, L., & Qu, J.J. (2007). NMDI: a normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters, 34(20), L20405.
Wang, X., Miaomiao, W., Shaoqiang, W., & Yundong, W. (2015). Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 31(5), 152-159.
Wenlong, X. (2009). Vegetation index controlling the influence of soil reflection.
Wolf, A.F. (2012). Using WorldView-2 Vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios. In: algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XVIII. The International Society for Optical Engineering, pp 188-195.
Yang, Z., Willis, P., & Mueller, R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. In: Proc. Pecora, Denver, Colo, USA, 1, pp 1-11.
Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M.R., De & Frutos, A. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99(3), 271-287.
Zarco-Tejada, P.J., González-Dugo, V., & Berni, J.A.J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117: 322-337.
Zarco-Tejada, P.J., González-Dugo, V., Williams, L.E., Suárez, L., Berni, J.A.J., Goldhamer, D., & Fereres, E. (2013). A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment, 138, 38-50.
Zhang, B., Wu, D., Zhang, L., Jiao, Q., & Li, Q. (2012). Application of hyperspectral remote sensing for environment monitoring in mining areas. Environmental Earth Sciences, 65(3), 649-658.
Zhang, C., & Kovacs, J.M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712.
Zhang, Y., Meng, Q.Y., Wu, J.L., & Zhao, F. (2011). Study of environmental vegetation index based on environment satellite CCD data and LAI inversion. Spectroscopy and Spectral Analysis, 31(10), 2789-2793.
 
Water and Soil Management and Modelling
Volume 3, Issue 2
June 2023
Pages 297-318

Files

History

  • Receive Date: 25 January 2023
  • Revise Date: 06 February 2023
  • Accept Date: 27 February 2023

Share

How to cite

Statistics