Investigation of the Use of Some Satellite Indices in Monitoring Leaf Area Index in Cotton Plants

Öz

Leaf Area Index (LAI) is accepted as one of the basic indicators of plant development. Direct LAI estimation methods require intensive labor and time. This work; In order to realize the LAI estimation without damage, in a shorter time and with less labor-intensive effort, it was made on a total of 27 parcels where cotton cultivation is carried out in 8 villages in Artuklu and Kızıltepe districts of Mardin province. In the study, the relationships between terrestrial LAI observation and ARVI, GARI, EVI2, NDVI, WDRVI, MSI, NBR, NDMI, MTVI2, SIPI, OSAVI, SAVI indices derived from Sentinel-2 satellite data were examined. All indices were found significant at the 0.01 level. ARVI and GARI having atmospheric correction effect (R2 =0.77-0.76, respectively), basic indices EVI2, NDVI and WDRVI (R2 =0.74-0.74-0.75, respectively), MSI, NBR and NDMI with plant moisture content sensitivity (R2=0.77-0.79-0.77, respectively) showed high relationships. In addition, pigment sensitivity MTVI2 and SIPI (R2 =0.73-0.74), OSAVI and SAVI designed against background soil effect (R2 =0.74-0.74) showed high relation. It is recommended that these indices be used as a good LAI estimator in cotton plant.

Anahtar Kelimeler

• Leaf Area Index
• Vegetation Indices
• Cotton
• Sentinel-2
• Google Earth Engine

Destekleyen Kurum

Dicle University Scientific Research Projects Coordination Unit

Proje Numarası

FBE.21.009

Teşekkür

This study was produced from the PhD thesis titled "Investigation of the Possibilities of Using Satellite Images in Detecting Plant-Water Relationship in Cotton (G. hirsutum L.)" conducted by Serkan KILIÇASLAN in the Field Crops department of Dicle University, Institute of Science and Technology. This study was supported by Dicle University Scientific Research Projects Coordination Unit with project number FBE.21.009. We would like to thank the Scientific Research Coordination Unit for their support.

Pamuk Bitkisinde Yaprak Alan İndeksinin İzlenmesinde Bazı Uydu İndekslerinin Kullanımının Araştırılması

Öz

Yaprak Alan İndeksi (LAI) bitki gelişiminin temel göstergelerinden kabul edilmektedir. Doğrudan LAI tahmin yöntemleri yoğun emek ve zaman gerektirmektedir. Bu çalışma; LAI tahminini, hasarsız, daha kısa zamanda ve daha az yoğun emek harcayarak gerçekleştirebilmek amacıyla; Mardin ili Artuklu ve Kızıltepe ilçelerine bağlı 8 köyde, pamuk tarımı yapılan toplam 27 adet parselde yapılmıştır. Çalışmada, yersel LAI gözlemi ile Sentinel-2 uydu verilerinden türetilen ARVI, GARI, EVI2, NDVI, WDRVI, MSI, NBR, NDMI, MTVI2, SIPI, OSAVI, SAVI indisleri arasındaki ilişkiler incelenmiştir. Tüm indisler 0.01 düzeyinde önemli bulunmuştur. Atmosferik düzeltme etkisine sahip ARVI ve GARI (sırasıyla R2 =0.77-0.76), temel indislerden EVI2, NDVI ve WDRVI (sırasıyla R2 =0.74-0.74-0.75), bitki nem içeriği hassasiyetli MSI, NBR ve NDMI (sırasıyla R2=0.77-0.79-0.77) yüksek ilişki göstermişlerdir. Ayrıca pigment hassasiyetli MTVI2 ve SIPI (R2 =0.73-0.74), arka plan toprak etkisine karşı tasarlanan OSAVI ve SAVI (R2 =0.74-0.74) yüksek ilişki göstermişlerdir. İncelenen bu indislerin pamuk bitkisinde iyi bir LAI tahmin edicisi olarak kullanılması tavsiye edilmektedir. .

Anahtar Kelimeler

• Yaprak Alan İndeksi
• Vejetasyon İndeksleri
• Pamuk
• Sentinel-2

Proje Numarası

FBE.21.009

Kaynakça

1. Ahamed, T., Tian, L., Zhang, Y., & Ting, K. C. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), 2455–2469.
2. Ali, A. M., Darvishzadeh, R., Skidmore, A. K., & van Duren, I. (2017). Specific leaf area estimation from leaf and canopy reflectance through optimization and validation of vegetation indices. Agricultural and Forest Meteorology, 236, 162–174. https://doi.org/10.1016/j.agrformet.2017.01.015
3. Anonymous 2012, https://icac.org/DataPortal/DataPortal?Units=BeginningStock&Year=2011/12 Access Date: 10.01.2023
4. Anonymous 2022, https://icac.org/DataPortal/DataPortal?Year=2020/21%20est Access Date: 10.01.2023
5. Bajwa, S. G., Gowda, P. H., Howell, T. A., & Leh, M. (2008). COMPARING ARTIFICIAL NEURLA NETWORK WITH LEAST SQUARE REGRESSION TECHNIQUES FOR LAI RETRIEVAL FROM REMTOE SENSING DATA. In Proceedings of the The 17th William T. Pecora memorial remote sensing symposium–The future of Landsat imaging, American Society of Photogrammetry and Remote Sensing.
6. Biudes, M. S., Machado, N. G., Danelichen, V. H. de M., Souza, M. C., Vourlitis, G. L., & Nogueira, J. de S. (2014). Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil. International Journal of Biometeorology, 58(6), 1181–1193. https://doi.org/10.1007/s00484-013-0713-4
7. Blinn, C. E., House, M. N., Wynne, R. H., Thomas, V. A., Fox, T. R., & Sumnall, M. (2019). Landsat 8 based leaf area index estimation in loblolly pine plantations. Forests, 10(3), 222.
8. Boer, M. M., Macfarlane, C., Norris, J., Sadler, R. J., Wallace, J., & Grierson, P. F. (2008). Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sensing of Environment, 112(12), 4358–4369.

9. Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252.
10. Cibula, W. G., Zetka, E. F., & Rickman, D. L. (1992). Response of thematic mapper bands to plant water stress. International Journal of Remote Sensing, 13(10), 1869–1880.
11. Dabrowska-Zielinska, K., Bartold, M., Gurdak, R., Gatkowska, M., Kiryla, W., Bochenek, Z., & Malinska, A. (2018). Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 5382–5385. https://doi.org/10.1109/IGARSS.2018.8519120
12. Darvishzadeh, R., Atzberger, C., Skidmore, A. K., & Abkar, A. A. (2009). Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position. International Journal of Remote Sensing, 30(23), 6199–6218. https://doi.org/10.1080/01431160902842342
13. Das, B., Sahoo, R. N., Pargal, S., Krishna, G., Verma, R., Chinnusamy, V., Sehgal, V. K., & Gupta, V. K. (2020). Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands. Geocarto International, 35(13), 1415–1432. https://doi.org/10.1080/10106049.2019.1581271
14. Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L., & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 46, 42–52. https://doi.org/10.1016/j.eja.2012.12.001
15. Fan, L. Y., Gao, Y. Z., Brück, H. E. B. C., & Bernhofer, C. (2009). Investigating the relationship between NDVI and LAI in semi-arid grassland in Inner Mongolia using in-situ measurements. Theoretical and Applied Climatology, 95, 151–156.
16. Fan, X., Lv, X., Gao, P., Zhang, L., Zhang, Z., Zhang, Q., Ma, Y., Yi, X., Yin, C., & Ma, L. (2023). Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum. Land, 12(1), Article 1. https://doi.org/10.3390/land12010078
17. Feng, W., Wu, Y., He, L., Ren, X., Wang, Y., Hou, G., Wang, Y., Liu, W., & Guo, T. (2019). An optimized non-linear vegetation index for estimating leaf area index in winter wheat. Precision Agriculture, 20, 1157–1176.
18. Filipponi, F. (2018). BAIS2: Burned area index for Sentinel-2. Proceedings, 2(7), 364.
19. Fu, Y., Yang, G., Wang, J., & Feng, H. (2013). A comparative analysis of spectral vegetation indices to estimate crop leaf area index. Intelligent Automation & Soft Computing, 19(3), 315–326.
20. Gao, L., Darvishzadeh, R., Somers, B., Johnson, B. A., Wang, Y., Verrelst, J., Wang, X., & Atzberger, C. (2022). Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment. Agricultural and Forest Meteorology, 326, 109178. https://doi.org/10.1016/j.agrformet.2022.109178
21. 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.
22. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.
23. Gowda, P., Oommen, T., Misra, D., Schwartz, R., Howell, T., & Wagle, P. (2015). Retrieving leaf area index from remotely sensed data using advanced statistical approaches. J. Remote Sens. GIS, 5, 156.
24. Gupta, R. K., Prasad, T. S., & Vijayan, D. (2000). Relationship between LAI and NDVI for IRS LISS and Landsat TM bands. Advances in Space Research, 26(7), 1047–1050.
25. Haboudane, D. (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. https://doi.org/10.1016/j.rse.2003.12.013
26. Hancock, D. W., & Dougherty, C. T. (2007). Relationships between blue‐and red‐based vegetation indices and leaf area and yield of alfalfa. Crop Science, 47(6), 2547–2556.
27. Hassan, H. A., Abdeldaym, E. A., Aboelghar, M., Morsy, N., Kucher, D. E., Rebouh, N. Y., & Ali, A. M. (2023). Multi-Sensor Remote Sensing to Estimate Biophysical Variables of Green-Onion Crop (Allium cepa L.) under Different Sources of Magnesium in Ismailia, Egypt. Sustainability, 15(22), 16048. https://doi.org/10.3390/su152216048
28. Heiskanen, J. (2006). Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(6), 1135–1158.
29. Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141–2151.
30. Huang, X., Lin, D., Mao, X., & Zhao, Y. (2023). Multi-source data fusion for estimating maize leaf area index over the whole growing season under different mulching and irrigation conditions. Field Crops Research, 303, 109111. https://doi.org/10.1016/j.fcr.2023.109111
31. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI. Remote sensing of environment, 25(3), 295–309.
32. Hunt Jr, 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.
33. Johnson, J., Lanclos, K., MacDonald, S., Meyer, L., & Soley, G. (2020). Cotton Outlook for 2020.
34. Ji, Z., Pan, Y., Zhu, X., Wang, J., & Li, Q. (2021). Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index. Sensors, 21(4), Article 4. https://doi.org/10.3390/s21041406
35. Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845.
36. Jin, X., Diao, W., Xiao, C., Wang, F., Chen, B., Wang, K., & Li, S. (2013). Estimation of Wheat Agronomic Parameters using New Spectral Indices. PLOS ONE, 8(8), e72736. https://doi.org/10.1371/journal.pone.0072736
37. Kang, Y., Özdoğan, M., Zipper, S., Román, M., Walker, J., Hong, S., Marshall, M., Magliulo, V., Moreno, J., Alonso, L., Miyata, A., Kimball, B., & Loheide, S. (2016). How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment. Remote Sensing, 8(7), 597.
38. Kaplan, G., & Rozenstein, O. (2021). Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2. Land, 10(5), Article 5. https://doi.org/10.3390/land10050505
39. 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.
40. Key, C. H., Benson, N., Ohlen, D., Howard, S., McKinley, R., & Zhu, Z. (2002). The normalized burn ratio and relationships to burn severity: Ecology, remote sensing and implementation. Proceedings of the Ninth Forest Service Remote Sensing Applications Conference, 8–12.
41. Kinane, S. M., Montes, C. R., Albaugh, T. J., & Mishra, D. R. (2021). A model to estimate leaf area index in loblolly pine plantations using Landsat 5 and 7 images. Remote Sensing, 13(6).
42. Küßner, R., & Mosandl, R. (2000). Comparison of direct and indirect estimation of leaf area index in mature Norway spruce stands of eastern Germany. Canadian Journal of Forest Research, 30(3), 440–447. https://doi.org/10.1139/x99-227
43. Li, X., Xiang, F., Wu, S., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2022). Diagnosis methods for nitrogen status based on the time-series vegetation index in winter wheat. https://www.cabidigitallibrary.org/doi/full/10.5555/20220318262
44. Li, Y.-D., Cao, Z.-S., Shu, S.-F., Sun, B.-F., Ye, C., Huang, J.-B., Zhu, Y., & Tian, Y.-C. (2021). Model for monitoring leaf dry weight of double cropping rice based on crop growth monitoring and diagnosis apparatus. https://www.cabidigitallibrary.org/doi/full/10.5555/20210371370
45. Liang FengChao, L. F., Cheng HongXia, C. H., Hu LieQun, H. L., Li ShuAi, L. S., & Ma LiYun, M. L. (2014). Method for monitoring cotton growth during growing season base on FY-3/MERSI data.. 51(8), 1381–1387.
46. Lipovac, A., Bezdan, A., Moravčević, D., Djurović, N., Ćosić, M., Benka, P., & Stričević, R. (2022). Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods. Water, 14(22), 3786. https://doi.org/10.3390/w14223786
47. Lymburner, L., Beggs, P. J., & Jacobson, C. R. (2000). Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66(2), 183–192.
48. Ma, Y., Zhang, Q., Yi, X., Ma, L., Zhang, L., Huang, C., Zhang, Z., & Lv, X. (2022). Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index. Remote Sensing, 14(1), Article 1. https://doi.org/10.3390/rs14010136
49. Marino, S., & Alvino, A. (2019). Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices. Agronomy, 9(5), 226. https://doi.org/10.3390/agronomy9050226
50. Miura, T., Yoshioka, H., Fujiwara, K., & Yamamoto, H. (2008). Inter-comparison of ASTER and MODIS surface reflectance and vegetation index products for synergistic applications to natural resource monitoring. Sensors, 8(4), 2480–2499.
51. Mokhtari, A., Noory, H., & Vazifedoust, M. (2018). Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model. Agricultural and Forest Meteorology, 250–251, 159–170. https://doi.org/10.1016/j.agrformet.2017.12.250
52. Ochtyra, A., Marcinkowska-Ochtyra, A., & Raczko, E. (2020). Threshold-and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains. Remote Sensing of Environment, 249, 112026.
53. Peng, J., Yang, F., Dan, L., & Tang, X. (2022). Estimation of China’s Contribution to Global Greening over the Past Three Decades. Land, 11(3), Article 3. https://doi.org/10.3390/land11030393
54. Penuelas, J., Baret, F., & Filella, I. (1995a). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221–230.
55. Penuelas, J., Baret, F., & Filella, I. (1995b). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221–230.
56. Peppo, M., Taramelli, A., Boschetti, M., Mantino, A., Volpi, I., Filipponi, F., Tornato, A., Valentini, E., & Ragaglini, G. (2021). Non-Parametric statistical approaches for leaf area index estimation from Sentinel-2 Data: A multi-crop assessment. Remote Sensing, 13(14), 2841.
57. Pfeifer, M., Gonsamo, A., Disney, M., Pellikka, P., & Marchant, R. (2012). Leaf area index for biomes of the Eastern Arc Mountains: Landsat and SPOT observations along precipitation and altitude gradients. Remote Sensing of Environment, 118, 103–115.
58. Prada, M., Cabo, C., Hernández-Clemente, R., Hornero, A., Majada, J., & Martínez-Alonso, C. (2020). Assessing canopy responses to thinnings for sweet chestnut coppice with time-series vegetation indices derived from landsat-8 and sentinel-2 imagery. Remote Sensing, 12(18), 3068.
59. Prananda, A. R. A., Kamal, M., & Kusuma, D. W. (2020). The effect of using different vegetation indices for mangrove leaf area index modelling. IOP Conference Series: Earth and Environmental Science, 500(1), 012006.
60. Quang, A. V., Delbart, N., Jaffrain, G., Pinet, C., & Moiret, A. (2022). Detection of degraded forests in Guinea, West Africa, based on Sentinel-2 time series by inclusion of moisture-related spectral indices and neighbourhood effect. Remote Sensing of Environment, 281, 113230.
61. Ray, S. S., Das, G., Singh, J. P., & Panigrahy, S. (2006). Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing, 27(24), 5373–5387. https://doi.org/10.1080/01431160600763006
62. Rocha, A. V., & Shaver, G. R. (2009). Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology, 149(9), 1560–1563.
63. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7
64. Rosso, P., Nendel, C., Gilardi, N., Udroiu, C., & Chlebowski, F. (2022). Processing of remote sensing information to retrieve leaf area index in barley: A comparison of methods. Precision Agriculture, 23(4), 1449–1472.
65. Rouse Jr, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. https://ntrs.nasa.gov/api/citations/19730017588/downloads/19730017588.pdf
66. Routh, P. K., Sarkar, N. C., Das, P. K., Debnath, D., Bandyopadhyay, S., & Raj, U. (2019). RETRIVAL OF BIO-PHYSICAL PARAMETERS IN SUNFLOWER CROP ( HELIANTHUS ANNUUS ) USING FIELD BASED HYPERSPECTRAL REMOTE SENSING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W6, 623–629. https://doi.org/10.5194/isprs-archives-XLII-3-W6-623-2019
67. Sharma, L. K., Bali, S. K., Dwyer, J. D., Plant, A. B., & Bhowmik, A. (2017). A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine. Sensors, 17(5), Article 5. https://doi.org/10.3390/s17051095
68. Siegmann, B., & Jarmer, T. (2015). Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data. International Journal of Remote Sensing, 36, 4519–4534. https://doi.org/10.1080/01431161.2015.1084438
69. Singh, M., Singh, B., Sharma, A., Mukherjee, J., Thind, S. K., & Kaur, R. (2012). Spectral models for estimation of chlorophyll content, nitrogen, moisture stress and growth of wheat crop. 11th International Conference on Precision Agriculture (ICPA) to Be Held at the Hyatt Regency Indianapolis, 15–18.
70. Solgi, S., Ahmadi, S. H., & Seidel, S. J. (2023). Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields. Agricultural Water Management, 280, 108226.
71. Srinet, R., Nandy, S., & Patel, N. R. (2019). Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India. Ecological Informatics, 52, 94–102.
72. Srivastava, A., Roy, S., Kimothi, M. M., Kumar, P., Sehgal, S., Mamatha, S., & Ray, S. S. (2019). Detection Of Bacterial Wilt Disease (Pseudomonas Solancearum) In Brinjal Using Hyperspectral Remote Sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3-W6, 515–520. ISPRS-GEOGLAM-ISRS Joint International Workshop on Earth Observations for Agricultural Monitoring (Volume XLII-3/W6) - 18–20 February 2019, New Delhi, India. https://doi.org/10.5194/isprs-archives-XLII-3-W6-515-2019
73. Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics. Remote Sensing of Environment, 71(2), 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X
74. Vina, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115(12), 3468–3478.
75. Wang, F. M., Huang, J. F., Tang, Y. L., & Wang, X. Z. (2007). New vegetation index and its application in estimating leaf area index of rice. Rice Science, 14(3), 195–203.
76. Wei, C., Chen, J., Chen, J. M., Yu, J. C., Cheng, C. P., Lai, Y. J., Chiang, P. N., Hong, C. Y., Tsai, M. J., & Wang, Y. N. (2020). Evaluating relationships of standing stock, LAI and NDVI at a subtropical reforestation site in southern Taiwan using field and satellite data. Journal of Forest Research, 25(4), 250–259.
77. Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2007). Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26(3), 335–344.
78. Zhao, H., Pei, Z., Ma, S., Wang, L., & Ma, Z. (2012). Retrieving leaf area index of winter wheat using HJ-1-A/B CCD2 data. Transactions of the Chinese Society of Agricultural Engineering, 28(10), 172–176.
79. Zhen, Z., Chen, S., Yin, T., Chavanon, E., Lauret, N., Guilleux, J., Henke, M., Qin, W., Cao, L., Li, J., Lu, P., & Gastellu-Etchegorry, J.-P. (2021). Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas. Sensors, 21(6), 2115.
80. Zhen-wang, L., Xiao-ping, X., Huan, T., Fan, Y. A. N. G., Bao-rui, C., & Bao-hui, Z. (2017). Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China. Journal of Integrative Agriculture, 16(02), 286–297.
81. Zhu, X., Guo, R., Liu, T., & Xu, K. (2021). Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data. Remote Sensing, 13(10), Article 10. https://doi.org/10.3390/rs13102016.