___

• Barrachina M, Cristóbal J, Tulla AF (2015). Estimating aboveground biomass on mountain meadows and pastures through remote sensing. International Journal of Applied Earth Observation and Geoinformation 38: 184-192. doi: 10.1016/j. jag.2014.12.002
• Buschman C, Nagel E (1993). In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. International Journal of Remote Sensing 14: 711-722. doi: 10.1080/01431169308904370
• Chang J, Shoshany M (2016). Red-edge ratio normalized vegetation index for remote estimation of green biomass. In: IEEE International Geoscience and Remote Sensing Symposium; Beijing, China; 2016. pp. 1337-1339.
• Chappelle EW, Kim MS, McMurtrey JE 3rd (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. doi: 10.1016/0034- 4257(92)90089-3
• Cho MA, Skidmore AK, Corsi F, van Wieren SE, Sobhan I (2007). Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation 9 (4): 414-424. doi: 1016/j. jag.2007.02.001
• Darvishzadeh R (2008). Hyperspectral remote sensing of vegetation parameters using statistical and physical models. PhD, International Institute for Geo-information Science and Earth Observation (ITC), Enschede, the Netherlands.
• Daughtry CST, Walthall CL, Kim MS, de Colstoun EB, McMurtrey JE 3rd (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74 (2): 229-239. doi: 10.1016/S0034-4257(00)00113-9
• Dorigo W, Richter R, Baret F, Bamler R, Wagner W (2009). Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach. Remote Sensing 1 (4): 1139-1170. doi: 10.3390/rs1041139
• Dusseux P, Hubert-Moy L, Corpetti T, Vertès F (2015). Evaluation of SPOT imagery for the estimation of grassland biomass. International Journal of Applied Earth Observation and Geoinformation 38: 72-77. doi: 10.1016/j.jag.2014.12.003
• Elvidge CD, Chen Z (1995). Comparison of broadband and narrowband red and near-infrared vegetation indices. Remote Sensing of Environment 54: 38-48. doi: 10.1016/0034-4257(95)00132- K
• Gamon JA, Peñuelas J, Field CB (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41 (1): 35-44. doi: 10.1016/0034-4257(92)90059-S
• Gitelson AA, Merzlyak MN (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148 (3-4): 494-500. doi: 10.1016/S0176-1617(96)80284-7
• Guo X, Price KP, Stiles JM (2000). Modeling biophysical factors for grasslands in Eastern Kansas using Landsat TM data. Transactions of the Kansas Academy of Science 103 (3-4): 122- 138. doi: 10.2307/3628261
• Guo X, Zhang C, Wilmshurst JF, Sissons R (2005). Monitoring grassland health with remote sensing approaches. Prairie Perspective 8: 11-22.
• Hansen PM, Schjoerring JK (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86 (4): 542-553. doi: 10.1016/S0034-4257(03)00131-7
• He Y (2008). Modeling grassland productivity through remote sensing products. PhD, University of Saskatchewan, Saskatoon, Canada.
• He Y, Guo X, Wilmshurst JF (2009). Reflectance measures of grassland biophysical structure. International Journal of Remote Sensing 30 (10): 2509-2521. doi: 10.1080/01431160802552751
• He Y, Mui A (2010). Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: challenges and opportunities. Sensors 10: 11072-11087. doi: 10.3390/ s101211072
• Herbei M, Sala F (2015). Use Landsat image to evaluate vegetation stage in sunflower crops. AgroLife Scientific Journal 4 (1): 79- 86.
• Jiang Z, Huete AR, Li J, Chen Y (2006). An analysis of angle-based with ratio-based vegetation indices. IEEE Transactions on Geoscience and Remote Sensing 44 (9): 2506-2513. doi: 10.1109/TGRS.2006.873205
• Jin Y, Yang X, Qiu J, Li J, Gao T et al. (2014). Remote sensingbased biomass estimation and its spatio-temporal variations in temperate grassland, Northern China. Remote Sensing 6: 1496-1513. doi: 10.3390/rs6021496
• Jordan CF (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (4): 663-666. doi: 10.2307/1936256
• Karabulut M (2006). NOAA AVHRR verilerini kullanarak Türkiye’de bitki örtüsünün izlenmesi ve incelenmesi. Coğrafi Bilimler Dergisi 4 (1): 29-42 (in Turkish). doi: 10.1501/ Cogbil_0000000061
• Karabulut M (2018). An examination of spectral reflectance properties of some wetland plants in Göksu Delta, Turkey. Journal of International Environmental Application and Science 13 (4): 194-203.
• Karabulut M, Ceylan N (2005). The spectral reflectance responses of water with different levels of suspended sediment in the presence of algae. Turkish Journal of Engineering & Environmental Sciences (29): 351-360.
• Karabulut M, Cosun F (2009). Kahramanmaraş ilinde yağışların trend analizi. Coğrafi Bilimler Dergisi 7 (1): 65-83 (in Turkish). doi: 10.1501/Cogbil_0000000095
• Karabulut M, Küçükönder M, Karakoç A (2014). Kurak koşullarin bitki üzerindeki etkilerinin yakin mesafe uzaktan algilama yöntemleri ile incelenmesi: ön bulgular. In: TÜCAUM VIII. Coğrafya Sempozyumu; Ankara, Turkey; 2014. pp. 139-150 (in Turkish).
• Le Maire G, François C, Dufrêne E (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89 (1): 1-28. doi: 10.1016/j.rse.2003.09.004
• Liang S (2004). Quantitative Remote Sensing of Land Surfaces, Vol. 30. Hoboken, NJ, USA: John Wiley & Sons. Lillesand TM, Kiefer RW (1994). Remote Sensing and Image Interpretation. Hoboken, NJ, USA: John Wiley & Sons, Inc.
• May AMB, Pinder JE 3rd, Kroh GC (2010). A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, U.S.A. International Journal of Remote Sensing 18 (18): 3719-3728. doi: 10.1080/014311697216577
• Meroni M, Colombo R, Panigada C (2004). Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sensing of Environment 92 (2): 195-206. doi: 10.1016/j.rse.2004.06.005
• Mistele B, Schmidhalter U (2010). Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total nitrogen in winter wheat. Agronomy Journal 102 (2): 499-506. doi: 10.2134/agronj2009.0282
• Mulla DJ (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering 114 (4): 358-371. doi: 10.1016/j. biosystemseng.2012.08.009
• Mundava C, Helmholtz P, Schut AGT, Stovold R, Corner R et al. (2014). Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2 (7): 47-53. doi: 10.5194/isprsannalsII-7-47-2014
• Mutanga O, Skidmore AK (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing 25 (19): 3999-4014. doi: 10.1080/01431160310001654923
• Peddle DR, White HP, Soffer RJ, Miller JR, LeDrew EF (2001). Reflectance processing of remote sensing spectroradiometer data. Computer & Geoscience 27: 203-213. doi: 10.1016/ S0098-3004(00)00096-0
• Prasad B, Carver BF, Stone ML, Babar MA, Raun WR et al. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Science 47 (4): 1426-1440. doi: 10.2135/ cropsci2006.07.0492
• Psomas A (2008). Hyperspectral remote sensing for ecological analyses of grassland ecosystems: Spectral separability and derivation of NPP related biophysical and biochemical parameters. PhD, University of Zürich, Zürich, Switzerland.
• Read JJ, Tarpley L, McKinion JM, Reddy KR (2002). Narrowwaveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality 31 (5): 1442-1452. doi: 10.2134/jeq2002.1442
• Roberts AD, Roth LK, Perroy LR (2011). Hyperspectral vegetation indices. In: Thenkabail SP, Lyon GJ, Huete A (editors).
• Hyperspectral Remote Sensing of Vegetation. Boca Raton, FL, USA: CRC Press, pp. 309-328.
• Rouse JW Jr, Haas RH, Schell JA, Deering DW (1973). Monitoring vegetation systems in the Great Plains with ERTS. In: Third Earth Resource Technology Satellite - 1 Symposium, NASA SP-351(I), pp. 309-317.
• Sehgal VK, Chakraborty D, Sahoo RN (2016). Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements. Information Processing in Agriculture 3 (2): 107-118. doi: 10.1016/j. inpa.2016.04.001
• Shen M, Tang Y, Klein J, Zhang P, Gu S et al. (2008). Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau. Journal of Plant Ecology 1 (4): 247-257. doi: 10.1093/jpe/rtn025
• Sims DA, 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 (2-3): 337-354. doi: 10.1016/S0034- 4257(02)00010-X
• Tan C, Samanta A, Jin X, Tong L, Ma C et al. (2013). Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies. International Journal of Remote Sensing 34: 8789-8802. doi: 10.1080/01431161.2013.853143
• Tian YC, Yao X, Yang J, Cao WX, Hannaway DB et al. (2011). Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with groundand space-based hyperspectral reflectance. Field Crops Research 120 (2): 299-310. doi: 10.1016/j.fcr.2010.11.002
• Todd SW, Hoffer RM, Milchunas DG (1998). Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing 19: 427-438. doi: 10.1080/014311698216071
• Varol Ö (2003). Flora of Başkonuş Mountain (Kahramanmaraş). Turkish Journal of Botany 27 (2): 117-139.
• Vogelmann JE, Rock BN, Moss DM (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563-1575. doi: 10.1080/01431169308953986
• Xu D, Guo X, Li Z, Yang X, Yin H (2014). Measuring the dead component of mixed grassland with Landsat imagery. Remote Sensing of Environment 142: 33-43. doi: /10.1016/j. rse.2013.11.017
• Xue L, Cao W, Luo W, Dai T, Zhu Y (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal 96 (1): 135-142. doi: 10.2134/agronj2004.1350
• Yavaşlı DD (2012). Recent approaches in above ground biomass estimation methods. Aegean Geographical Journal 21 (1): 39- 51.
• Yu K, Lenz-Wiedemann V, Leufen G, Hunsche M, Noga G et al. (2012). Assessing hyperspectral vegetation indices for estimating leaf chlorophyll concentration of summer barley. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-7: 89-94. doi: 10.5194/isprsannals-I-7-89-2012
• Zarco-Tejada PJ, Miller JR, Noland TL, Mohammed GH, Sampson PH (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Geoscience and Remote Sensing Letters 39 (7): 1491-1507. doi: 10.1109/36.934080
• Zhang C, Ren H, Qin Q, Ersoy OK (2017). A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI). Remote Sensing Letters 8 (6): 576-585. doi: 10.1080/2150704X.2017.1306135