A comparison of UAV and satellites multispectral imagery in monitoring onion crop. An application in the ‘Cipolla Rossa di Tropea’ (Italy)

Remote Sensing

Volumn 12, Issue 20, 2020, Pages 1-27

  Messina, Gaetano ^a   Peña, Jose M ^b   Vizzari, Marco ^c   Modica, Giuseppe ^a  

^a UNIVERSITY MEDITERRANEA OF REGGIO CALABRIA   (Italy)

^b INSTITUTO DE CIENCIAS AGRARIAS   (Spain)

^c UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Bare soil; Correlation analysis; Geographical object based image classification (GEOBIA); Mixed pixels; Onion crops; Soil adjusted vegetation index (SAVI); Spatial resolution; Vegetation indices (VIs)

Indexed keywords

AGRICULTURAL ROBOTS; ANTENNAS; CROPS; PIXELS; SOILS; UNMANNED AERIAL VEHICLES (UAV);

AGRO ECOLOGICAL SERVICES; GEOGRAPHICAL OBJECTS; INFORMATION AND COMMUNICATION TECHNOLOGIES; MANAGEMENT STRATEGIES; MULTI-SPECTRAL IMAGERY; SATELLITE PLATFORMS; SPATIAL AND TEMPORAL VARIABILITY; SPECTRAL CONTRIBUTION;

SATELLITE IMAGERY;

EID: 85092926343     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs12203424     Document Type: Article

References (97)

1. Solano, F.; Di Fazio, S.; Modica, G. A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards. Int. J. Appl. Earth Obs. Geoinf. 2019, 83, 101912. [CrossRef]
2. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358–371. [CrossRef]
3. Moran, M.S.; Inoue, Y.; Barnes, E.M. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 1997, 61, 319–346. [CrossRef]
4. Sagan, V.; Maimaitijiang, M.; Sidike, P.; Eblimit, K.; Peterson, K.T.; Hartling, S.; Esposito, F.; Khanal, K.; Newcomb, M.; Pauli, D.; et al. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras. Remote Sens. 2019, 11, 330. [CrossRef]
5. Messina, G.; Modica, G. Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sens. 2020, 12, 1491. [CrossRef] 6. McCabe, M.F.; Houborg, R.; Lucieer, A. High-resolution sensing for precision agriculture: From Earth-observing satellites to unmanned aerial vehicles. Remote Sens. Agric. Ecosyst. Hydrol. XVIII 2016, 9998, 999811. [CrossRef]
6. McCabe, M.F.; Houborg, R.; Lucieer, A. High-resolution sensing for precision agriculture: From Earth-observing satellites to unmanned aerial vehicles. Remote Sens. Agric. Ecosyst. Hydrol. XVIII 2016, 9998, 999811. [CrossRef]
7. ESA. Resolution and Swath. Available online: earth.esa.int/web/sentinel/missions/sentinel-2/instrument-payload/resolution-and-swath (accessed on 2 April 2020).
8. Vizzari, M.; Santaga, F.; Benincasa, P. Sentinel 2-Based Nitrogen VRT Fertilization in Wheat: Comparison between Traditional and Simple Precision Practices. Agronomy 2019, 9, 278. [CrossRef]
9. Modica, G.; Pollino, M.; Solano, F. Sentinel-2 Imagery for Mapping Cork Oak (Quercus suber L.) Distribution in Calabria (Italy): Capabilities and Quantitative Estimation. In New Metropolitan Perspectives; ISHT Smart Innovation, Systems and Technologies; Calabrò, F., Della Spina, L., Bevilacqua, C., Eds.; Springer: Cham, Switzerland, 2019; Volume 100, pp. 60–67. ISBN 9783319920993.
10. Houborg, R.; McCabe, M.F. High-Resolution NDVI from planet’s constellation of earth observing nano-satellites: A new data source for precision agriculture. Remote Sens. 2016, 8, 768. [CrossRef]
11. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. [CrossRef]
12. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693–712. [CrossRef]
13. Maes, W.H.; Steppe, K. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends Plant Sci. 2019, 24, 152–164. [CrossRef]
14. Khanal, S.; Fulton, J.; Shearer, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 2017, 139, 22–32. [CrossRef]
15. He, Y.; Weng, Q. High Spatial Resolution Remote Sensing: Data, Analysis, and Applications; CRC Press: Boca Raton, FL, USA, 2018; ISBN 9780429470196.
16. Jeong, S.; Kim, D.; Yun, H.; Cho, W.; Kwon, Y.; Kim, H. Monitoring the growth status variability in Onion (Allium cepa) and Garlic (Allium sativum) with RGB and multi-spectral UAV remote sensing imagery. In Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture, Hamilton, New Zealand, 16–18 October 2017; pp. 1–6.
17. Tang, C.; Turner, N.C. The influence of alkalinity and water stress on the stomatal conductance, photosynthetic rate and growth of Lupinus angustifolius L. and Lupinus pilosus Murr. Aust. J. Exp. Agric. 1999, 39, 457–464. [CrossRef]
18. Benincasa, P.; Antognelli, S.; Brunetti, L.; Fabbri, C.A.; Natale, A.; Sartoretti, V.; Modeo, G.; Guiducci, M.; Tei, F.; Vizzari, M. Reliability of Ndvi Derived By High Resolution Satellite and Uav Compared To in-Field Methods for the Evaluation of Early Crop N Status and Grain Yield in Wheat. Exp. Agric. 2017, 1–19. [CrossRef]
19. Yao, H.; Qin, R. Unmanned Aerial Vehicle for Remote Sensing Applications—A Review. Remote Sens. 2019, 11, 1–22. [CrossRef]
20. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2–16. [CrossRef]
21. Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.; Queiroz Feitosa, R.; van der Meer, F.; van der Werff, H.; van Coillie, F.; et al. Geographic Object-Based Image Analysis—Towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87, 180–191. [CrossRef]
22. Chen, G.; Weng, Q.; Hay, G.J.; He, Y. Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities. GISci. Remote Sens. 2018, 55, 159–182. [CrossRef]
23. Belgiu, M.; Csillik, O. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens. Environ. 2018, 204, 509–523. [CrossRef]
24. Csillik, O.; Cherbini, J.; Johnson, R.; Lyons, A.; Kelly, M. Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones 2018, 2, 39. [CrossRef]
25. De Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 2018, 10, 285. [CrossRef]
26. De Castro, A.I.; Peña, J.M.; Torres-Sánchez, J.; Jiménez-Brenes, F.; López-Granados, F. Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control. Adv. Anim. Biosci. 2017, 8, 267–271. [CrossRef]
27. López-Granados, F.; Torres-Sánchez, J.; De Castro, A.I.; Serrano-Pérez, A.; Mesas-Carrascosa, F.J.; Peña, J.M. Object-based early monitoring of a grass weed in a grass c rop using high resolution UAV imagery. Agron. Sustain. Dev. 2016, 36, 1–12. [CrossRef]
28. Ok, A.O.; Ozdarici-Ok, A. 2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models. Int. J. Digit. Earth 2018, 11, 583–608. [CrossRef]
29. Ozdarici-Ok, A. Automatic detection and delineation of citrus trees from VHR satellite imagery. Int. J. Remote Sens. 2015, 36, 4275–4296. [CrossRef]
30. Torres-Sánchez, J.; López-Granados, F.; Peña, J.M. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Comput. Electron. Agric. 2015, 114, 43–52. [CrossRef]
31. De Castro, A.I.; Jiménez-Brenes, F.M.; Torres-Sánchez, J.; Peña, J.M.; Borra-Serrano, I.; López-Granados, F. 3-D characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture applications. Remote Sens. 2018, 10, 584. [CrossRef]
32. Peña, J.M.; Kelly, M.; De Castro, A.I.; López Granados, F. Object-based approach for crop row characterization in UAV images for site-specific weed management. In Proceedings of the 4th International Conference on Geographic Object-Based Image Analysis (GEOBIA 2012), Rio Janeiro, Brazil, 7–9 May 2012; Queiroz-Feitosa, A., Ed.; pp. 426–430.
33. Córcoles, J.I.; Ortega, J.F.; Hernández, D.; Moreno, M.A. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle. Biosyst. Eng. 2013, 115, 31–42. [CrossRef]
34. Ballesteros, R.; Ortega, J.F.; Hernandez, D.; Moreno, M.A. Onion biomass monitoring using UAV-based RGB imaging. Precis. Agric. 2018, 1–18. [CrossRef]
35. Aboukhadrah, S.H.; El—Alsayed, A.W.A.H.; Sobhy, L.; Abdelmasieh, W. Response of Onion Yield and Quality To Different Planting Date, Methods and Density. Egypt. J. Agron. 2017, 39, 203–219. [CrossRef]
36. Mallor, C.; Balcells, M.; Mallor, F.; Sales, E. Genetic variation for bulb size, soluble solids content and pungency in the Spanish sweet onion variety Fuentes de Ebro. Response to selection for low pungency. Plant Breed. 2011, 130, 55–59. [CrossRef]
37. Ranjitkar, H. A Handbook of Practical Botany; Ranjitkar, A.K., Ed.; Kathmandu Publishing: Kathmandu, Nepal, 2003.
38. Pareek, S.; Sagar, N.A.; Sharma, S.; Kumar, V. Onion (Allium cepa L.). In Fruit and Vegetable Phytochemicals: Chemistry and Human Health; Yahia, E.M., Ed.; Wiley & Sons: Hoboken, NJ, USA, 2017.
39. Zhao, L.; Shi, Y.; Liu, B.; Hovis, C.; Duan, Y.; Shi, Z. Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data. Remote Sens. 2019, 11, 12. [CrossRef]
40. Bernardi, B.; Zimbalatti, G.; Proto, A.R.; Benalia, S.; Fazari, A.; Callea, P. Mechanical grading in PGI Tropea red onion post harvest operations. J. Agric. Eng. 2013, 44, 317–322. [CrossRef]
41. Tiberini, A.; Mangano, R.; Micali, G.; Leo, G.; Manglli, A.; Tomassoli, L.; Albanese, G. Onion yellow dwarf virus ∆∆Ct-based relative quantification obtained by using real-time polymerase chain reaction in “Rossa di Tropea” onion. Eur. J. Plant Pathol. 2019, 153, 251–264. [CrossRef]
42. Consorzio di Tutela della Cipolla Rossa di Tropea Calabria IGP. Available online: www. consorziocipollatropeaigp.com (accessed on 30 April 2020).
43. Meier, U. Growth Stages of Mono-and Dicotyledonous Plants; Federal Biological Research Centre for Agriculture and Forestry, Ed.; Blackwell Wissenschafts-Verlag: Berlin, Germany, 2001; Volume 12, ISBN 9783826331527.
44. Bukowiecki, J.; Rose, T.; Ehlers, R.; Kage, H. High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor. Front. Plant Sci. 2020, 10, 1. [CrossRef] [PubMed]
45. Iqbal, F.; Lucieer, A.; Barry, K. Poppy crop capsule volume estimation using UAS remote sensing and random forest regression. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 362–373. [CrossRef]
46. Maimaitijiang, M.; Ghulam, A.; Sidike, P.; Hartling, S.; Maimaitiyiming, M.; Peterson, K.; Shavers, E.; Fishman, J.; Peterson, J.; Kadam, S.; et al. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS J. Photogramm. Remote Sens. 2017, 134, 43–58. [CrossRef]
47. Fawcett, D.; Panigada, C.; Tagliabue, G.; Boschetti, M.; Celesti, M.; Evdokimov, A.; Biriukova, K.; Colombo, R.; Miglietta, F.; Rascher, U.; et al. Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sens. 2020, 12, 514. [CrossRef]
48. Jorge, J.; Vallbé, M.; Soler, J.A. Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images vegetation index obtained from UAV images. Eur. J. Remote Sens. 2019, 52, 169–177. [CrossRef]
49. Pádua, L.; Marques, P.; Adão, T.; Guimarães, N.; Sousa, A.; Peres, E.; Sousa, J.J. Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts. Agronomy 2019, 9, 581. [CrossRef]
50. Deng, L.; Mao, Z.; Li, X.; Hu, Z.; Duan, F.; Yan, Y. UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS J. Photogramm. Remote Sens. 2018, 146, 124–136. [CrossRef]
51. Cubero-Castan, M.; Schneider-Zapp, K.; Bellomo, M.; Shi, D.; Rehak, M.; Strecha, C. Assessment Of The Radiometric Accuracy In A Target Less Work Flow Using Pix4D Software. In Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands, 23–26 September 2018; Volume 2018, pp. 1–4.
52. Messina, G.; Fiozzo, V.; Praticò, S.; Siciliani, B.; Curcio, A.; Di Fazio, S.; Modica, G. Monitoring Onion Crops Using Multispectral Imagery from Unmanned Aerial Vehicle (UAV). In Proceedings of the “NEW METROPOLITAN PERSPECTIVES. Knowledge Dynamics and Innovation-driven Policies Towards Urban and Regional Transition”, Reggio Calabria, Italy, 18–23 May 2020; Bevilacqua, C., Francesco, C., Della Spina, L., Eds.; Springer: Reggio Calabria, Italy, 2020; Volume 2, pp. 1640–1649.
53. Messina, G.; Praticò, S.; Siciliani, B.; Curcio, A.; Di Fazio, S.; Modica, G. Telerilevamento multispettrale da drone per il monitoraggio delle colture in agricoltura di precisione. Un’applicazione alla cipolla rossa di Tropea (Multispectral UAV remote sensing for crop monitoring in precision farming. An application to the Red onion of Tropea). LaborEst 2020, 21. in press.
54. Bartsch, A.; Widhalm, B.; Leibman, M.; Ermokhina, K.; Kumpula, T.; Skarin, A.; Wilcox, E.J.; Jones, B.M.; Frost, G.V.; Höfler, A.; et al. Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data. Remote Sens. Environ. 2020, 237, 111515. [CrossRef]
55. Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sens. 2017, 9, 596. [CrossRef]
56. Spoto, F.; Martimort, P.; Drusch, M. Sentinel—2: ESA’s Optical High-Resolution Mission for GMES Operational Services; Elsevier: Amsterdam, The Netherlands, 2012; Volume 707 SP. ISBN 9789290922711.
57. Rapinel, S.; Mony, C.; Lecoq, L.; Clément, B.; Thomas, A.; Hubert-Moy, L. Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sens. Environ. 2019, 223, 115–129. [CrossRef]
58. Copernicus. Available online: scihub.copernicus.eu (accessed on 15 April 2020).
59. Planet Team. Planet Application Program Interface: In Space for Life on Earth; Planet Team: San Francisco, CA, USA, 2017; Available online: https://api.planet.com (accessed on 30 April 2020).
60. Ghuffar, S. DEM generation from multi satellite Planetscope imagery. Remote Sens. 2018, 10, 1462. [CrossRef]
61. Kääb, A.; Altena, B.; Mascaro, J. Coseismic displacements of the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation. Nat. Hazards Earth Syst. Sci. 2017, 17, 627–639. [CrossRef]
62. Planet Labs Inc. Planet Imagery and Archive. Available online: https://www.planet.com/products/planet-imagery/(accessed on 30 April 2020).
63. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [CrossRef]
64. Taylor, P.; Silleos, N.G. Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years Vegetation Indices. Geocarto Int. 2006, 37–41. [CrossRef]
65. Huete, A.R.; Jackson, R.D.; Post, D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 1985, 17, 37–53. [CrossRef]
66. Khaliq, A.; Comba, L.; Biglia, A.; Ricauda Aimonino, D.; Chiaberge, M.; Gay, P. Comparison of satellite and UAV-based multispectral imagery for vineyard variability assessment. Remote Sens. 2019, 11, 436. [CrossRef]
67. Drǎguţ, L.; Csillik, O.; Eisank, C.; Tiede, D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 2014, 88, 119–127. [CrossRef]
68. Aguilar, M.A.; Aguilar, F.J.; García Lorca, A.; Guirado, E.; Betlej, M.; Cichon, P.; Nemmaoui, A.; Vallario, A.; Parente, C. Assessment of multiresolution segmentation for extracting greenhouses from WorldView-2 imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.—ISPRS Arch. 2016, 41, 145–152. [CrossRef]
69. Baatz, M.; Schäpe, A. 2000 Multi-resolution segmentation: An optimization approach for high quality multi-scale. Beiträge zum Agit XII Symposium Salzburg, Heidelberg 2000, 12–23. [CrossRef]
70. Trimble Inc. eCognition ®Developer; Trimble Germany GmbH: Munich, Germany, 2019; pp. 1–266.
71. Drǎguţ, L.; Tiede, D.; Levick, S.R. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 2010, 24, 859–871. [CrossRef]
72. El-naggar, A.M. Determination of optimum segmentation parameter values for extracting building from remote sensing images. Alexandria Eng. J. 2018, 57, 3089–3097. [CrossRef]
73. Ma, L.; Li, M.; Ma, X.; Cheng, L.; Du, P.; Liu, Y. A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote Sens. 2017, 130, 277–293. [CrossRef]
74. Modica, G.; Messina, G.; De Luca, G.; Fiozzo, V.; Praticò, S. Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery. Comput. Electron. Agric. 2020. [CrossRef]
75. Peña, J.M.; Torres-Sánchez, J.; De Castro, A.I.; Kelly, M.; López-Granados, F. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS ONE 2013, 8. [CrossRef] [PubMed]
76. Malacarne, D.; Pappalardo, S.E.; Codato, D. Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture. GI_Forum 2018, 105–116. [CrossRef]
77. Chuvieco, E. Fundamentals of Satellite Remote Sensing, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2016; ISBN 9781498728072.
78. Jones, H.G.; Vaughan, R.A. Remote Sensing of Vegetation Principles, Techniques, and Applications; Oxford University Press: Oxford, UK, 2010; ISBN 9780199207794.
79. Tarnavsky, E.; Garrigues, S.; Brown, M.E. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sens. Environ. 2008, 112, 535–549. [CrossRef]
80. Anderson, J.H.; Weber, K.T.; Gokhale, B.; Chen, F. Intercalibration and Evaluation of ResourceSat-1 and Landsat-5 NDVI. Can. J. Remote Sens. 2011, 37, 213–219. [CrossRef]
81. Goward, S.N.; Davis, P.E.; Fleming, D.; Miller, L.; Townshend, J.R. Empirical comparison of Landsat 7 and IKONOS multispectral measurements for selected Earth Observation System (EOS) validation sites. Remote Sens. Environ. 2003, 88, 80–99. [CrossRef]
82. Soudani, K.; François, C.; le Maire, G.; Le Dantec, V.; Dufrêne, E. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sens. Environ. 2006, 102, 161–175. [CrossRef]
83. Xu, H.; Zhang, T. Comparison of Landsat-7 ETM+ and ASTER NDVI measurements. In Proceedings of the Remote Sensing of the Environment: The 17th China Conference on Remote Sensing, Hangzhou, China, 27–31 August 2010; Volume 8203, p. 82030K.
84. Abuzar, M. Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. Am. J. Remote Sens. 2014, 2, 1. [CrossRef]
85. Psomiadis, E.; Dercas, N.; Dalezios, N.R.; Spyropoulos, N.V. The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices. Remote Sens. Agric. Ecosyst. Hydrol. XVIII 2016, 9998, 99981L. [CrossRef]
86. Yin, H.; Udelhoven, T.; Fensholt, R.; Pflugmacher, D.; Hostert, P. How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study. Remote Sens. 2012, 4, 3364–3389. [CrossRef]
87. Miura, T.; Yoshioka, H.; Fujiwara, K.; Yamamoto, H. Inter-comparison of ASTER and MODIS surface reflectance and vegetation index products for synergistic applications to natural resource monitoring. Sensors 2008, 8, 2480–2499. [CrossRef]
88. Li, Z.; Zhang, H.K.; Roy, D.P.; Yan, L.; Huang, H. Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution. Remote Sens. 2020, 12, 2406. [CrossRef]
89. Gallo, K.P.; Daughtry, C.S.T. Differences in vegetation indices for simulated Landsat-5 MSS and TM, NOAA-9 AVHRR, and SPOT-1 sensor systems. Remote Sens. Environ. 1987, 23, 439–452. [CrossRef]
90. Teillet, P.M.; Staenz, K.; Williams, D.J. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sens. Environ. 1997, 61, 139–149. [CrossRef]
91. Huete, A.R.; Liu, H.Q.; Batchily, K.; Van Leeuwen, W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 1997, 59, 440–451. [CrossRef]
92. Wilson, E.H.; Sader, S.A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ. 2002, 80, 385–396. [CrossRef]
93. Zhang, N.; Wang, M.; Wang, N. Precision agriculture—A worldwide overview. Comput. Electron. Agric. 2002, 36, 113–132. [CrossRef]
94. Robert, P.C. Precision agriculture: A challenge for crop nutrition management. Plant Soil 2002, 247, 143–149. [CrossRef]
95. Gebbers, R.; Adamchuk, V.I. Precision agriculture and food security. Science 2010, 327, 828–831. [CrossRef]
96. Matese, A.; Toscano, P.; Di Gennaro, S.F.; Genesio, L.; Vaccari, F.P.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 2015, 7, 2971–2990. [CrossRef]
97. Zhang, J.; Huang, Y.; Pu, R.; Gonzalez-Moreno, P.; Yuan, L.; Wu, K.; Huang, W. Monitoring plant diseases and pests through remote sensing technology: A review. Comput. Electron. Agric. 2019, 165. [CrossRef]