Title

以無人機獲取之多光譜影像建立土壤中鎘與水稻吸收量關聯性

Translated Titles

Correlating the Soil Cadmium and its uptake of in Rice with Unmanned Aerial Vehicle-derived Multispectral Images

DOI

10.6342/NTU201903888

Authors

蔡顓均

Key Words

水稻 ; 反射光譜 ; 遙感探測 ; 重金屬 ; 鎘污染 ; 植生指數 ; 無人機 ; 多光譜影像 ; 高光譜 ; Oryza sativa L. ; reflectance ; remote sensing ; heavy metals ; cadmium pollution ; vegetative indices ; UAV ; multispectral images ; hyperspectral

PublicationName

臺灣大學農藝學研究所學位論文

Volume or Term/Year and Month of Publication

2019年

Academic Degree Category

碩士

Advisor

黃文達

Content Language

繁體中文

Chinese Abstract

農田土壤重金屬污染問題日趨嚴重,對糧食安全以及人體健康構成威脅,而傳統重金屬檢測方式僅以點代面、破壞性檢測重金屬污染。隨著遙測技術結合無人機載具平臺發展日漸成熟,以快速、高通量、低人力成本對作物及環境污染程度做即時監控。本試驗旨於建立一套標準程序可用於無人機多光譜影像輔助預測植體重金屬鎘濃度與糙米累積重金屬鎘含量管理評估系統。試驗場域位於桃園八德鎘隱患區水稻(Oryza sativa L.)試驗田,試驗期作為2018年一期作,種植台稉9號、台稉14號以及台東30號。試驗期間由無人機攜載多光譜(Red、Green、Red edge、NIR) Parrot Sequoia相機進行時序蒐集空拍影像。分析水稻營養生長期、生殖生長期以及成熟期植體鎘含量與53種植生指數之相關性。結果顯示,水稻不同品種與生育階段之植體鎘含量,各有相關性較佳之植生指數。其中,與台稉9號之收穫時期糙米鎘累積量最相關之植生指數為GNDVI (R squared為0.870;移植後第89天);與台稉14號之收穫時期糙米鎘累積量最相關之植生指數為ARI (R squared為0.985;移植後第75天);與台東30號之營養生長期植體鎘累積量最相關之植生指數為MCARI/MTVI2 (R squared為0.959)。以常用單波段、多光譜以及以高光譜數值計算多光譜波段,三種方式計算53種植生指數與糙米鎘濃度進行回歸分析,分別可得到最佳估算模式:台稉9號分別為Y= – 0.0002 × MCARI/MTVI– 0.0153、– 3.701 × NGRDI+ 1.497、0.0002 × TVI– 1.472;台稉14號分別為– 4.513 × GOSAVI + 3.846、– 4.386 × NGRDI + 1.866、– 2.088 × GSAVI + 2.590;台東30號分別為– 0.011 × TCI – 0.955、– 0.139 × Cl Red Edge + 0.908、– 0.003 × TCARIMSAVI + 0.299,以植生指數估算當下穀粒濃度,若超過食米限量標準0.4 mg · kg -1,進行農藝管理措施,實現預警之目的,因此以水稻葉片或植冠反射光譜計算植生指數,可以進行非破壞性地估算水稻植體累積重金屬鎘濃度之變化,可即時輔助監測水稻重金屬含量與作物生長狀況,有助於精準農耕之發展。

English Abstract

The problem of heavy metal pollution in farmland soil is more critical with every passing, which poses a threat to food security and human health. However, traditional methods of detecting heavy metal pollution was only selective abstraction and destructive. With the development of remote sensing technique combined with the platform for Unmanned Aerial Vehicle (UAVs), the crops and environmental pollution levels are monitored in real time with fast, high throughput and low labor costs. The aim of this study is to develop a standard protocol for field UAV times series multispectral images to assist in the prediction of above ground and brown rice cumulative cadmium (Cd) concentration management evaluation system. Rice (Oryza sativa L.) potential danger of Cd pollution field located in Taoyuan Bade. The experiment period during first crop season in 2018, planting Taikeng 9 (TK 9), Taikeng 14 (TK 14) and Taitung 30 (TT 30). UAVs carried Parrot Sequoia Multispectral camera (Red, Green, Red Edge, NIR) for collecting aerial images in time series. Fifty-three vegetative indices (VIs) derived from four original wavebands reflectance and integration of VIs were tested in this study for analysis correlation between Cd concentration in vegetative phase, reproductive phase and maturation phase. Result shows that the best VIs for different phenotypes regression varies over time and varieties. Cd content in brown rice of rice variety TY9 were highly correlated with GNDVI at 89 days after transplanting (DAT) (R squared = 0.870) , TY14 were highly correlated with ARI at 75 DAT (R squared = 0.985) and TT30 were highly correlated with MCARI/MTVI2 at 40 DAT (R squared = 0.985). VIs derived from three calculated methods about normal single band, UAV multiple spectral and simulating multiple spectral from hyperspectral were tested in this study for regression analysis. Result shows that the best prediction models to estimate Cd content in brown rice: TK 9 respectively Y= – 0.0002 × MCARI/MTVI– 0.0153、– 3.701 × NGRDI+ 1.497、0.0002 × TVI– 1.472; TK 14 respectively Y= – 4.513 × GOSAVI + 3.846、– 4.386 × NGRDI + 1.866、– 2.088 × GSAVI + 2.590; TT 30 respectively Y=– 0.011 × TCI – 0.955、– 0.139 × Cl Red Edge + 0.908、– 0.003 × TCARIMSAVI + 0.299. In summary, this system can provide estimate the current grain concentration by VIs. If it exceeds the rice limit of 0.4 mg · kg -1, agronomic management measures are taken to achieve the purpose of early warning. Therefore, the VIs can be calculated by reflectance from leaf and canopy, and non-destructive estimation of rice plants can be carried out. The accumulation of heavy metal Cd concentration in rice can immediately assist in monitoring content and crop growth of rice, and contribute to the development of precision agriculture.

Topic Category 生物資源暨農學院 > 農藝學研究所
生物農學 > 農業
Reference
  1. 王芳、鄭瑞倫、何刃、李花粉。2006。氯離子和乙二胺四乙酸對鎘的植物有效性的影響。應用生態學報,17(10),頁1953-1957。
  2. 王薏雯。2007。整合福衛二號高時間解析度和高空間解析度衛星影像與田間光譜資料監測水稻生長和預測產量。成功大學地球科學系學位論文,頁1-147。
  3. 王天成。2018。以無人機獲取之多光譜影像建立田間作物性狀調查暨栽培管理決策支援系統。臺灣大學農藝學研究所學位論文,頁1-119。
  4. 史磊、郭朝暉、梁芳、彭馳、肖細元、封文利。2017。水分管理和施用石灰對水稻鎘吸收與運移的影響。農業工程學報,33(24),頁111-117。
  5. 仲曉春、戴其根、何理、陳京都、孫成明、高輝、張洪程、鄭超。2012。不同濃度鎘脅迫下水稻冠層光譜特徵及其預測評價。農業環境科學學報,31(3),頁448-454。
  6. 任紅豔、莊大方、潘劍君、史學正、施潤和、王洪傑。2010。重金屬污染水稻的冠層反射光譜特徵研究。光譜學與光譜分析,30(02),頁430-434。
  7. 吳啟南、蕭國鑫、徐偉城、廖子毅、陳大科、劉治中。2002。衛星及地面遙測資料應用於水稻生長及產量監測初步研究。應用於水稻精準農業體系之知識與技術。頁19-38。
  8. 吳佩真、林毓雯、李長沛、許健輝、卓緯玄、顏信沐、郭洪玉、賴明信、吳東鴻。2017。以功能性分子標誌輔助回交策略選育具低鎘累積能力之秈稻新品系。臺灣農業研究,66 (3),頁248-260。
  9. 呂傑、 郝甯燕、崔曉臨。2015。利用可見光近紅外的尾礦區農田土壤 Cu 含量反演。農業工程學報,31(9),頁265-270。
  10. 李花粉。2000。根際重金屬污染。中國農業科技導報,2(4),頁54-59。
  11. 李粉玲、常慶瑞、申健、王力。2016。模擬多光譜衛星寬波段反射率的冬小麥葉片氮含量估算。農業機械學報,47(2),頁302-308。
  12. 李宗翰、範致豪、謝勝信、洪銘德、廖珮妤、黃文達。2018。氮肥及鉻對水稻幼苗生長與反射光譜之影響。中華民國雜草學會會刊,39(2),頁119-142。
  13. 李繼寧。2014。土壤空心菜系統中重金屬生物有效性及其人體健康風險評估研究。北京:中國環境科學院碩士論文。
  14. 沈文娟、蔣超群、侍昊、王春紅、李明詩。2014。土壤重金屬污染遙感監測研究進展。遙感資訊,29(6),頁112-117、124。
  15. 初建。 2012。農產品中重金屬與安全管理。興大農業,82,頁19-23。
  16. 周巧盈、巫思揚、陳琦玲。2018。應用無人飛機航拍影像協助農業勘災—以香蕉災損影像判釋為例。航測及遙測學刊,23(2),頁83-101。
  17. 林朝欽、邱祈榮、周巧盈。2005。火燒嚴重度之界定與評估:以武陵森林火災為例。臺灣林業科學,20(3),頁203-213。
  18. 林盈茹。2017。氮肥及重金屬對水稻幼苗生長及反射光譜之影響.。臺灣大學農藝學研究所學位論文。頁1-286。
  19. 金銘、劉湘南、李鐵瑛。2011。基於冠層多維光譜的水稻鎘污染脅迫診斷模型研究。中國環境科學,31(1),頁137-143。
  20. 邱相禎。2018。不同改良劑對污染土壤中重金屬植物有效性之影響。中央大學環境工程研究所在職專班碩士論文。
  21. 武威、陳雯、陳瑛瑛、劉濤、孫成明。2018。不同水稻品種主要生育期冠層光譜特徵分析。中國稻米,25(1),49-51。
  22. 夏增祿、穆從如、李森照。1983。北京東郊作物對重金屬的吸收及其重金屬在土壤中含量和存在形態的關係。生態學報,3(3),277-285。
  23. 夏天、周勇、周清波、吳文斌。2013。基於高光譜遙感和 HJ-1衛星的冬小麥 SPAD 反演研究。長江流域資源與環境,22(3),頁309-313。
  24. 張浩、胡昊、陳義、唐旭、吳春豔、劉玉學、楊生茂、鄭可鋒。2012。水稻葉片氮素及籽粒蛋白質含量的高光譜估測模型。核農學報,1,頁135-140。
  25. 張靜靜、周衛紅、鄒萌萌、劉影、杜小龍、李建龍。2018。高光譜遙感監測大面積土壤重金屬污染的研究現狀原理及展望。江蘇農業科學,46(12),9-13。
  26. 曹仁林。1993。鉻鎘對作物品質的影響。土壤,25(6), 324-326。
  27. 章國威、王淑姿、申雍、羅正宗、黃鼎名、蔡和霖。2006。應用抽穗期多光譜航照影像預估水稻產量之研究。航測及遙測學刊,11(1),頁27-38。
  28. 許明晃、陳宏銘、楊志維、張新軒、黃文達、楊棋明。2007。灰系統理論在生物學之應用:(6) 氣象因素影響 SPOT 衛星遙測草坪生長之灰關聯分析。中華民國雜草學會會刊,(1),頁59-70。
  29. 許惇惠、楊純明。2010。輻射光譜遙測在作物生產上之多元應用−以水稻為例介紹。技術服務,84,頁34-37。
  30. 陳文德。2000。我國精準農業的發展方向與策略。台中: 農業試驗所,頁7-14。
  31. 陳榮坤、楊純明。 2002。簡介農作物光譜−植被光譜特徵與植體水分含量之關係。農業試驗所技術服務,50(7),頁07-13。
  32. 陳仁炫、鄒裕民。2008。土壤與肥料分析手冊(一)土壤化學性質分析。台北市:中華土壤肥料學會,頁 76-85。
  33. 萬紅友、周生路、趙其國、張學雷。2010。蘇南經濟快速發展區土壤 Cu, Ni, Pb, Zn 形態及其有效性定量分析−以昆山市為例。土壤學報,47(4),頁652-658。
  34. 葉琮裕。2011。以植生復育法處理重金屬污染底泥之探討。臺灣礦業,63(4),頁28-43。
  35. 解憲麗、孫波、郝紅濤。2007。土壤可見光-近紅外反射光譜與重金屬含量之間的相關性。土壤學報,44(6),頁982-993。
  36. 謝慶芳。1990。土壤中鎘含量與水稻植物體不同部位鎘濃度變化之研究。臺中區農業改良場研究彙報,(29),頁11-27。
  37. 韓棟、楊曉梅、紀凱。2008。小衛星遙感影像自動提取水體方法研究。測繪科學,33(1),頁51-54。
  38. 關麗、劉湘南。2009。水稻鎘污染脅迫遙感診斷方法與試驗。農業工程學報,25(6),頁168-173。
  39. 關麗、劉湘南、程承旗。2009。土壤鎘污染環境下水稻葉片葉綠素含量監測的高光譜遙感資訊參數。光譜學與光譜分析,29(10),頁2713-2716。
  40. Ando, M., Sayato, Y., & Osawa, T. (1978). Hygienic chemical studies on poisonous metals VII Studies on the chemical composition and the activity of enzyme in bone of rats after continuous oral administration of cadmium. Eisei Kagaku, 24(1), 19-24.
  41. Anjum, S. A., Tanveer, M., Hussain, S., Bao, M., Wang, L., Khan, I., Ullah, E., Tuag, S. A., Samad, R. A., Shahzad, B. (2015). Cadmium toxicity in Maize (Zea mays L.): consequences on antioxidative systems, reactive oxygen species and cadmium accumulation. Environmental Science and Pollution Research, 22(21), 17022-17030.
  42. Bauer, M. E. (1975). The role of remote sensing in determining the distribution and yield of crops Advances in Agronomy. Elsevier, 27, 271-304.
  43. Boussama, N., Ouariti, O., & Ghorbal, M. H. (1999). Changes in growth and nitrogen assimilation in barley seedlings under cadmium stress. Journal of Plant Nutrition, 22(4-5), 731-752.
  44. Baryla, A., Carrier, P., Franck, F., Coulomb, C., Sahut, C., & Havaux, M. (2001). Leaf chlorosis in oilseed rape plants (Brassica napus) grown on cadmium-polluted soil: causes and consequences for photosynthesis and growth. Planta, 212(5-6), 696-709.
  45. 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.
  46. Bannari, A., Asalhi, H., & Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. Institute of Electrical and Electronics Engineers, 3053-3055.
  47. Balestrasse, K. B., Benavides, M. P., Gallego, S. M., & Tomaro, M. L. (2003). Effect of cadmium stress on nitrogen metabolism in nodules and roots of soybean plants. Functional Plant Biology, 30(1), 57-64.
  48. Clijsters, H., & Van Assche, F. (1985). Inhibition of photosynthesis by heavy metals. Photosynthesis Research, 7(1), 31-40.
  49. Chaney, R. (1989). Toxic element accumulation in soils and crops: protecting soil fertility and agricultural food-chains Inorganic contaminants in the vadose zone. Springer, 140-158.
  50. Crippen, R. E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34(1), 71-73.
  51. Carter, G. A. (1993). Responses of leaf spectral reflectance to plant stress. American Journal of Botany, 80(3), 239-243.
  52. 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.
  53. Chien, H. F., Lin, C. C., Wang, J. W., Chen, C. T., & Kao, C. H. (2002). Changes in ammonium ion content and glutamine synthetase activity in rice leaves caused by excess cadmium are a consequence of oxidative damage. Plant Growth Regulation, 36(1), 41-47.
  54. Du, A., Ledin, A., Karlsson, S., & Allard, B. (1995). Adsorption of zinc on colloidal (hydr) oxides of Si, Al and Fe in the presence of a fulvic acid. Applied Geochemistry, 10(2), 197-205.
  55. Das, P., Samantaray, S., & Rout, G. (1997). Studies on cadmium toxicity in plants: a review. Environmental Pollution, 98(1), 29-36.
  56. Daughtry, C., Walthall, C., Kim, M., De Colstoun, E. B., & McMurtrey Iii, J. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229-239.
  57. 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.
  58. Eitel, J., Long, D., Gessler, P., & Smith, A. (2007). Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 28(18), 4183-4190.
  59. Flanagan, P. R., Haist, J., & Valberg, L. S. (1980). Comparative effects of iron deficiency induced by bleeding and a low-iron diet on the intestinal absorptive interactions of iron, cobalt, manganese, zinc, lead and cadmium. The Journal of Nutrition, 110(9), 1754-1763.
  60. Fritioff, Å. (2005). Metal accumulation by plants: evaluation of the use of plants in stormwater treatment. Botaniska institutionen. 1-55.
  61. Fritioff, Å., Kautsky, L., & Greger, M. (2005). Influence of temperature and salinity on heavy metal uptake by submersed plants. Environmental Pollution, 133(2), 265-274.
  62. Farooq, M. A., Ali, S., Hameed, A., Ishaque, W., Mahmood, K., & Iqbal, Z. (2013). Alleviation of cadmium toxicity by silicon is related to elevated photosynthesis, antioxidant enzymes; suppressed cadmium uptake and oxidative stress in cotton. Ecotoxicology and Environmental Safety, 96, 242-249.
  63. 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.
  64. Gilabert, M. A., Gandía, S., & Melia, J. (1996). Analyses of spectral-biophysical relationships for a corn canopy. Remote Sensing of Environment, 55(1), 11-20.
  65. Green, E. P., Mumby, P. J., Edwards, A. J., Clark, C. D., & Ellis, A. C. (1997). Estimating leaf area index of mangroves from satellite data. Aquatic Botany, 58(1), 11-19.
  66. Geiken, B., Masojidek, J., Rizzuto, M., Pompili, M., & Giardi, M. (1998). Incorporation of [35S] methionine in higher plants reveals that stimulation of the D1 reaction centre II protein turnover accompanies tolerance to heavy metal stress. Plant, Cell and Environment, 21(12), 1265-1273.
  67. 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.
  68. Gong, P., Pu, R., Biging, G. S., & Larrieu, M. R. (2003). Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1355-1362.
  69. 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.
  70. Gamon, J. A., Huemmrich, K. F., Wong, C. Y., Ensminger, I., Garrity, S., Hollinger, D. Y., Noormets, A. & Peñuelas, J. (2016). A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proceedings of the National Academy of Sciences, 113(46), 13087-13092.
  71. Huete, A., Jackson, R., & Post, D. (1985). Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17(1), 37-53.
  72. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  73. Hernandez, L., Carpena‐Ruiz, R., & Garate, A. (1996). Alterations in the mineral nutrition of pea seedlings exposed to cadmium. Journal of Plant Nutrition, 19(12), 1581-1598.
  74. Hawkes, S. J. (1997). What is a" heavy metal"? Journal of Chemical Education, 74(11), 1374.
  75. 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(2-3), 416-426.
  76. Hsu, Y. T., & Kao, C. H. (2004). Cadmium toxicity is reduced by nitric oxide in rice leaves. Plant Growth Regulation, 42(3), 227-238.
  77. 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.
  78. He, J., Zhu, C., Ren, Y., Yan, Y., & Jiang, D. (2006). Genotypic variation in grain cadmium concentration of lowland rice. Journal of Plant Nutrition and Soil Science, 169(5), 711-716.
  79. Haboudane, D., Tremblay, N., Miller, J. R., & Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 423-437.
  80. Ishimaru, Y., Takahashi, R., Bashir, K., Shimo, H., Senoura, T., Sugimoto, K., Ono, K., Yano, M., Ishikawa, S., Arao, T., Nakanishi, Nishizawa, N. K. (2012). Characterizing the role of rice NRAMP5 in manganese, iron and cadmium transport. Scientific Reports, 2, 286.
  81. Irfan, M., Ahmad, A., & Hayat, S. (2014). Effect of cadmium on the growth and antioxidant enzymes in two varieties of Brassica juncea. Saudi Journal of Biological Sciences, 21(2), 125-131.
  82. Jarvis, S., Jones, L., & Hopper, M. (1976). Cadmium uptake from solution by plants and its transport from roots to shoots. Plant and Soil, 44(1), 179-191.
  83. Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: a remote sensing perspective. Pearson College Div. 242-245.
  84. Justice, C. O., Vermote, E., Townshend, J. R. G., Defries, R., Roy, D. P., Hall, D. K., Salomonson, V. V., Privette, J. L., Riggs, G., Strahler, A., Lucht, W., Myneni, R. B., Knyazikhin, Y., Running, S. W., Nemani, R. R., Wan, Z., Huete, A. R., Leeuwen, W. V., Wolfe, R. E., Giglio, L., Muller, J., Lewis, P., Barnsley, M. J.(1998). The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228-1249.
  85. Jensen, J. R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective Prentice Hall. Inc., Upper Saddle River, New Jersey. 1323.
  86. Ji, R., Xie, B. Y., Li, D. M., Li, Z., & Zhang, X. (2004). Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems and Environment, 104(3), 615-620.
  87. Jaishankar, M., Tseten, T., Anbalagan, N., Mathew, B. B., & Beeregowda, K. N. (2014). Toxicity, mechanism and health effects of some heavy metals. Interdisciplinary Toxicology, 7(2), 60-72.
  88. Knipling, E. B. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1(3), 155-159.
  89. Krishnan, P., Alexander, J. D., Butler, B., & Hummel, J. W. (1980). Reflectance technique for predicting soil organic matter. Soil Science Society of America Journal, 44(6), 1282-1285.
  90. Kloke, A., Sauerbeck, D., & Vetter, H. (1984). The contamination of plants and soils with heavy metals and the transport of metals in terrestrial food chains Changing metal cycles and human health. Springer, 113-141.
  91. Küpper, H., Küpper, F., & Spiller, M. (1996). Environmental relevance of heavy metal-substituted chlorophylls using the example of water plants. Journal of Experimental Botany, 47(2), 259-266.
  92. Küpper, H., Parameswaran, A., Leitenmaier, B., Trtílek, M., & Šetlík, I. (2007). Cadmium‐induced inhibition of photosynthesis and long‐term acclimation to cadmium stress in the hyperaccumulator Thlaspi caerulescens. New Phytologist, 175(4), 655-674.
  93. Krantev, A., Yordanova, R., Janda, T., Szalai, G., & Popova, L. (2008). Treatment with salicylic acid decreases the effect of cadmium on photosynthesis in maize plants. Journal of Plant Physiology, 165(9), 920-931.
  94. Khan, S., Farooq, R., Shahbaz, S., Khan, M. A., & Sadique, M. (2009). Health risk assessment of heavy metals for population via consumption of vegetables. World Applied Sciences Journal, 6(12), 1602-1606.
  95. 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.
  96. Lagriffoul, A., Mocquot, B., Mench, M., & Vangronsveld, J. (1998). Cadmium toxicity effects on growth, mineral and chlorophyll contents, and activities of stress related enzymes in young maize plants (Zea mays L.). Plant and Soil, 200(2), 241-250.
  97. Lee, Y. J., Yang, C. M., Chang, K. W., & Shen, Y. (2008). A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy. Agronomy Journal, 100(1), 205-212.
  98. Li, F. L., Yuan, J., & Sheng, G. D. (2012). Altered transfer of heavy metals from soil to Chinese cabbage with film mulching. Ecotoxicology and environmental safety, 77, 1-6.
  99. Lukačová, Z., Švubová, R., Kohanová, J., & Lux, A. (2013). Silicon mitigates the Cd toxicity in maize in relation to cadmium translocation, cell distribution, antioxidant enzymes stimulation and enhanced endodermal apoplasmic barrier development. Plant Growth Regulation, 70(1), 89-103.
  100. Li, F. L., Yang, C. M., Syu, C. H., Lee, D. Y., Tsuang, B. J., & Juang, K. W. (2016). Combined effect of rice genotypes and soil characteristics on iron plaque formation related to Pb uptake by rice in paddy soils. Journal of Soils and Sediments, 16(1), 150-158.
  101. Mench, M., Vangronsveld, J., Lepp, N., & Edwards, R. (1998), Physiochemical aspects and efficienty of trace element immobilization by soil amendments. In: Vangronsveld, J., Cunningham, S. (Eds.), In-situ Inactivation and Phytorestoration of Metal Contaminated Soils. Springer-Verlag, Berlin Heidel-berg and R. G. Landes Company, Geogetown TX, USA, 151-182.
  102. Nakanishi, H., Ogawa, I., Ishimaru, Y., Mori, S., & Nishizawa, N. K. (2006). Iron deficiency enhances cadmium uptake and translocation mediated by the Fe2+ transporters OsIRT1 and OsIRT2 in rice. Soil Science and Plant Nutrition, 52(4), 464-469.
  103. Nagajyoti, P. C., Lee, K. D., & Sreekanth, T. (2010). Heavy metals, occurrence and toxicity for plants: a review. Environmental Chemistry Letters, 8(3), 199-216.
  104. Miyadate, H., Adachi, S., Hiraizumi, A., Tezuka, K., Nakazawa, N., Kawamoto, T., Katou, K., Kodama, I., Sakurai, K., Takahashi, H., Nagasawa, N. S., Watanabe, A., Fujimura, T., Akagi, H. (2011). OsHMA3, a P1B‐type of ATPase affects root‐to‐shoot cadmium translocation in rice by mediating efflux into vacuoles. New Phytologist, 189(1), 190-199.
  105. Monzon, J., Calviño, P., Sadras, V., Zubiaurre, J., & Andrade, F. (2018). Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study. European Journal of Agronomy, 99, 62-71.
  106. Örlander, G., Egnell, G., & Forsén, S. (1989). Infrared thermography as a means of assessing seedling quality. Scandinavian Journal of Forest Research, 4(1-4), 215-222.
  107. Pearson, R. L., & Miller, L. D. (1972). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Pawnee National Grasslands, Colorado, in Proc. 8th Int. Symp. on Remote sensing of Environment, ERIM, Ann Arbor, MI, 1357-1381.
  108. Påhlsson, A.-M. B. (1989). Toxicity of heavy metals (Zn, Cu, Cd, Pb) to vascular plants. Water, Air, and Soil Pollution, 47(3-4), 287-319.
  109. Peñuelas, J., Savé, R., Marfà, O., & Serrano, L. (1992). Remotely measured canopy temperature of greenhouse strawberries as indicator of water status and yield under mild and very mild water stress conditions. Agricultural and Forest Meteorology, 58(1-2), 63-77.
  110. Price, J. C., & Bausch, W. C. (1995). Leaf area index estimation from visible and near-infrared reflectance data. Remote Sensing of Environment, 52(1), 55-65.
  111. Perfus‐Barbeoch, L., Leonhardt, N., Vavasseur, A., & Forestier, C. (2002). Heavy metal toxicity: cadmium permeates through calcium channels and disturbs the plant water status. The Plant Journal, 32(4), 539-548.
  112. Rouse J.W., Jr. (1972). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. Food and Agriculture Organization of the United Nations. 1-238.
  113. Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W.(1974). Monitoring vegetation systems in the great plains with ERTS. Remote Sensing Center, Texas A&M University, College Station, Taxas. 309-318.
  114. Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375-384.
  115. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
  116. Ren, H. Y., Zhuang, D. F., Pan, J. J., Shi, X. Z., & Wang, H. J. (2008). Hyper-spectral remote sensing to monitor vegetation stress. Journal of Soils and Sediments, 8(5), 323.
  117. Ren, H. Y., Zhuang, D. F., Singh, A. N., Pan, J. J., Qiu, D. S. & Shi, R. H. (2009). Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study. Pedosphere, 19(6), 719-726.
  118. Ren, H. Y., Zhuang, D. F., Singh, A. N., Pan, J. J., Qiu, D. S. & Shi, R. H. (2009). Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study. Pedosphere, 19(6), 719-726.
  119. Rascio, N., & Navari-Izzo, F. (2011). Heavy metal hyperaccumulating plants: how and why do they do it? And what makes them so interesting? Plant Science, 180(2), 169-181.
  120. Rajkumar, M., Sandhya, S., Prasad, M., & Freitas, H. (2012). Perspectives of plant-associated microbes in heavy metal phytoremediation. Biotechnology Advances, 30(6), 1562-1574.
  121. Sandell, E. B. (1950). Colorimetric determination of traces of metals. Interscience Publishers, 17
  122. Schafer, R., Young, S., Hendrick, J., & Johnson, C. (1984). Control concepts for tillage systems. Soil and Tillage Research, 4(4), 313-320.
  123. Semu, E., Singh, B. R., & Selmer-Olsen, A. R. (1987a). Adsorption of mercury compounds by tropical soils II Effect of soil: solution ratio, ionic strength, pH, and organic matter. Water, Air, and Soil Pollution, 32(1-2), 1-10.
  124. Semu, E., Singh, B. R., & Selmer-Olsen, A. R. (1987b). Adsorption of mercury compounds by tropical soils III. Adsorption isotherms. Water, Air, and Soil Pollution, 32(1-2), 11-16.
  125. Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76(3), 267-275.
  126. Shah, K., Kumar, R. G., Verma, S., & Dubey, R. (2001). Effect of cadmium on lipid peroxidation, superoxide anion generation and activities of antioxidant enzymes in growing rice seedlings. Plant Science, 161(6), 1135-1144.
  127. 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(2-3), 337-354.
  128. Sigfridsson, K. G., Bernát, G., Mamedov, F., & Styring, S. (2004). Molecular interference of Cd2+ with Photosystem II. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1659(1), 19-31.
  129. Sripada, R. P., Heiniger, R. W., White, J. G., & Meijer, A. D. (2006). Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy journal, 98(4), 968-977.
  130. Sripada, R. P., Schmidt, J. P., Dellinger, A. E., & Beegle, D. B. (2008). Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agronomy Journal, 100(6), 1553-1561.
  131. Seth, C. S., Misra, V., Chauhan, L., & Singh, R. (2008). Genotoxicity of cadmium on root meristem cells of Allium cepa: cytogenetic and comet assay approach. Ecotoxicology and Environmental Safety, 71(3), 711-716.
  132. Soriano-Disla, J., Gómez, I., Guerrero, C., Jordan, M., & Navarro-Pedreño, J. (2008). Soil factors related to heavy metal bioavailability after sewage sludge application. Fresenius Environmental Bulletin, 17(41), 1839-1845.
  133. Shimo, H., Ishimaru, Y., An, G., Yamakawa, T., Nakanishi, H., & Nishizawa, N. K. (2011). Low cadmium (LCD), a novel gene related to cadmium tolerance and accumulation in rice. Journal of Experimental Botany, 62(15), 5727-5734.
  134. Singh, R., Gautam, N., Mishra, A., & Gupta, R. (2011). Heavy metals and living systems: An overview. Indian journal of pharmacology, 43(3), 246.
  135. Sasaki, A., Yamaji, N., Yokosho, K., & Ma, J. F. (2012). Nramp5 is a major transporter responsible for manganese and cadmium uptake in rice. The Plant Cell, 24(5), 2155-2167.
  136. Sebastian, A., & Prasad, M. N. V. (2014). Cadmium minimization in rice. A review. Agronomy for Sustainable Development, 34(1), 155-173.
  137. Tanner, C. (1963). Plant Temperatures. Agronomy Journal, 55(2), 210-211.
  138. Tandon, S., & Gupta, J. (1970). Measurement of forbidden energy gap of semiconductors by diffuse reflectance technique. Physica Status Solidi (b), 38(1), 363-367.
  139. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  140. Tucker, C. J., Vanpraet, C. L., Sharman, M., & Van Ittersum, G. (1985). Satellite remote sensing of total herbaceous biomass production in the senegalese sahel: 1980-1984. Remote Sensing of Environment, 17(3), 233-249.
  141. Taiz, L., & Zeiger, E. (1991). Plant physiology. Redwood City. California, USA: Benjamin/Cumming Company, USA. Trejo, 32-34.
  142. Trejo, C. L. & Davies, W. J. (1991). Drought-induced closure of Phaseolus vulgaris stomata preceded leaf water deficit and any increase in xylem ABA concentration. Journal of Experimental Botany, 42, 1507-1515.
  143. Tian, Y., Zhu, Y., & Cao, W. (2005). Monitoring soluble sugar, total nitrogen & its ratio in wheat leaves with canopy spectral reflectance. Acta agronomica Sinica, 31(3), 355-360.
  144. Tanaka, K., Fujimaki, S., Fujiwara, T., Yoneyama, T., & Hayashi, H. (2007). Quantitative estimation of the contribution of the phloem in cadmium transport to grains in rice plants (Oryza sativa L.). Soil Science and Plant Nutrition, 53(1), 72-77.
  145. Tangahu, B. V., Abdullah, S., Rozaimah, S., Basri, H., Idris, M., Anuar, N., & Mukhlisin, M. (2011a). A review on heavy metals (As, Pb, and Hg) uptake by plants through phytoremediation. International Journal of Chemical Engineering, 1-30.
  146. Tangahu, B. V., Abdullah, S. R. S., Basri, H., Idris, M., Anuar, N., & Mukhlisin, M. (2011b). Isolation and screening of rhizobacteria from Scirpus grossus plant after lead (Pb) exposure. Journal of Civil Engineering and Architecture, 5(6), 484-493.
  147. Thakur, A. K., & Singh, K. J. (2012). Leaf temperature as thermal bio-indicator of heavy metal pollutants. Journal of Agricultural Science and Technology, A, 2(1A), 131.
  148. Tan, K., Ye, Y. Y. & Zhang, Q. Q. (2014). Estimation of heavy metal concentrations in reclaimed mining soils using reflectance spectroscopy. Spectroscopy and Spectral Analysis, 34(12), 3317-3322.
  149. Tiwari, M., Sharma, D., Dwivedi, S., Singh, M., Tripathi, R. D., & Trivedi, P. K. (2014). Expression in Arabidopsis and cellular localization reveal involvement of rice NRAMP, OsNRAMP1, in arsenic transport and tolerance. Plant, cell and Environment, 37(1), 140-152.
  150. Tattaris, M., Reynolds, M. P., & Chapman, S. C. (2016). A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Frontiers in Plant Science, 7, 1131.
  151. Ueno, D., Yamaji, N., Kono, I., Huang, C. F., Ando, T., Yano, M., & Ma, J. F. (2010). Gene limiting cadmium accumulation in rice. Proceedings of the National Academy of Sciences, 107(38), 16500-16505.
  152. Valberg, L., Sorbie, J., & Hamilton, D. (1976). Gastrointestinal metabolism of cadmium in experimental iron deficiency. American Journal of Physiology-Legacy Content, 231(2), 462-467.
  153. Vincini, M., Frazzi, E., & Alessio, P. D. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9(5), 303-319.
  154. Wagner, G. J. (1993). Accumulation of cadmium in crop plants and its consequences to human health. Advances in Agronomy., 51, 173-212.
  155. Wang, S., Wang, F., & Gao, S. (2015). Foliar application with nano-silicon alleviates Cd toxicity in rice seedlings. Environmental Science and Pollution Research, 22(4), 2837-2845.
  156. Yang, C. M., Chen, J. C., Peng, L. L., Yang, J. S., & Chou, C. H. (2002). Chi-Chi Earthquake-caused Landslide: grey prediction model for pioneer vegetation recovery monitored by satellite images. Botanical Bulletin of Academia Sinica, 43.
  157. Yannarelli, G. G., Fernández-Alvarez, A. J., Santa-Cruz, D. M., & Tomaro, M. L. (2007). Glutathione reductase activity and isoforms in leaves and roots of wheat plants subjected to cadmium stress. Phytochemistry, 68(4), 505-512.
  158. Yang, Z., Willis, P. & Mueller R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Proceedings of the 17th William Pecora Memorial Remote Sensing Symposium, Denver, Colo, USA, 1-11.
  159. Yadav, S. (2010). Heavy metals toxicity in plants: an overview on the role of glutathione and phytochelatins in heavy metal stress tolerance of plants. South African Journal of Botany, 76(2), 167-179.
  160. Zeng, F., Ali, S., Zhang, H., Ouyang, Y., Qiu, B., Wu, F., & Zhang, G. (2011). The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants. Environmental Pollution, 159(1), 84-91.
  161. Zabcic, N., Rivard, B., Ong, C., & Mueller, A. (2014). Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings. International Journal of Applied Earth Observation and Geoinformation, 32, 152-162.
  162. Zhang, B., Liu, X., Liu, M., & Wang, D. (2017). Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice. Journal of Applied Remote Sensing, 11(2), 026036.