Welcome to Open Science
Contact Us
Home Books Journals Submission Open Science Join Us News Unsubscribe Page
Remote Sensing for Sugarcane Crop Yield Estimation in Eswatini: Case of Lower Usuthu Smallholder Irrigation Project Sugarcane Farms
Current Issue
Volume 8, 2020
Issue 1 (March)
Pages: 19-27   |   Vol. 8, No. 1, March 2020   |   Follow on         
Paper in PDF Downloads: 22   Since Jan. 13, 2020 Views: 216   Since Jan. 13, 2020
Authors
[1]
Bhekumusa Senzo Shiba, Department of Geography, Environmental Science & Planning, The University of Eswatini, Kwaluseni Campus, Matsapha, Eswatini.
[2]
Sizwe Doctor Mabaso, Sabelo Nick Dlamini, Department of Geography, Environmental Science & Planning, The University of Eswatini, Kwaluseni Campus, Matsapha, Eswatini.
[3]
Saico Sibusiso Singwane, Department of Geography, Environmental Science & Planning, The University of Eswatini, Kwaluseni Campus, Matsapha, Eswatini.
Abstract
Early estimation of sugarcane crop yield is a key requirement for maximising profits in sugarcane production because most operations are based on these estimations. Such operations include planning for the season management of labour, transportation, storage and marketing. This study explored the potential for the use of NDVI to estimate sugarcane crop yield in LUSIP sugarcane farms at Siphofaneni, Eswatini, using Landsat 8 OLI satellite imagery for the years 2013 – 2016. Computed NDVI values were correlated with the respective sugarcane crop yield data sourced from Eswatini Water and Agricultural Development Enterprise (ESWADE), to establish the relationship between them, using regression analysis. The study concluded that the relationship between NDVI and sugarcane crop yield for the whole of LUSIP project at Siphofaneni was strongest for the month of August when considered at overall farms level (R2 = 0.973), rather than at individual farmer company scale (R2 = 0.134). Furthermore, it concluded that Landsat imagery was appropriate for sugarcane crop yield estimation in the country, especially in the month of August, even though at large scale than over small, individual project areas.
Keywords
Crop Yield Estimation, Landsat, NDVI, Remote Sensing, Sugarcane
Reference
[1]
Lia, I. M., Semwah, D. P., Rai, A., and Chikara, R. S. (2018). Use of Satellite Spectral Data in Crop Yield Estimation Surveys. International Journal of Remote Sensing, 8: 2583-2592.
[2]
Davey (2018). The Role of Spectral Resolution and Classifier Complexity in the Analysis of Hyperspectral Images of Frost Areas. Remote Sensing of Environment, 2345-2355.
[3]
Somrad, T. O., Colvin, D. B., S, J. R., and S, K. U. (2018). Using satellite and field data with crop growth modelling to monitor and estimate corn yield in Mexico. Crop Science. 42 (6), 1943-1949.
[4]
Duveiller, G., Lopez-Lozano, R., and Baruth, B. (2013). Enhanced Processing Of 1-KM Spatial Resolution fAPAR Time Series For Sugarcane Yield Forecasting And Monitoring. Remoter Sensing, 5: 1091-1116.
[5]
Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., van Ittersum, M., (2013). A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union. In: Field Crops Res., Crop Yield Gap Analysis-Rationale, Methods and Applications. 143. 130–142.
[6]
Inman-Bamber, N. (2005) “Sugarcane Physiology: Integrating from Cell to Crop to Advance Sugarcane Production.” Field Crops Research, 92 (2), 115-117.
[7]
Mulyono, K and Nadirah, L. P. (2016). Identifying Sugarcane Plantation using LANDSAT-8 Images with Support Vector Machines. Earth and Environmental Science. 47 (2016) 012008.
[8]
Du, M., and Noguchi, N. (2017). Monitoring of Wheat Growth Status and Mapping of Wheat Yields Within-Field Spatial Variations Using Colour Images Acquired from UAV-Camera System. Remote Sensing, 21 (3), 289-310.
[9]
Panda, S., and Arnes, D. (2010). Crop Yield Forecasting from Remotely Sensed Aerial Images with Self-Organizing Maps. American Society of Agricultural and Biological Engineers, 53 (2), 123-130.
[10]
Sinha, J. P., Kushwala, L. H., Kushwala, K. D., and Purushottam, M. (2016). Prospects of Unmanned Aerial Vehicle (UAV) Technology forAgricultural Production Management. Indian Agricultural Research Institute, 3 (4): 40-57.
[11]
Lofton, J., Wang, J., Huang, C. Z., Zhang, B., and Tong, Q. (2012). Estimating Sugarcane Yield Potential Using and In-Season Determination Of Normalised Difference Vegetation Index. Sensors, 12: 2184-2199.
[12]
Elhag, A and Abdelhadi, A. (2018). Monitoring and Yield Estimation of Sugarcane Using Remote Sensing and GIS. American Journal of Enginnering Research (AJER), 7 (1): 170-179.
[13]
Xue, J., & Su, B. C. (2017). Significant Remote Sensing Indices: A Review of Development and Applications. Yaugling: A & F University.
[14]
Begue, A., Lebourgeois, V., Bappel, E., Todoroff, P., Pellegrino, A., Baillarin, F., and Siegmund, B. (2018). Spatio-Temporal Variability Of Sugarcane Fileds And Recommendations For Yield Forecast Using NDVI. International Journal Of Remote Sensing, 6: 82-94.
[15]
Gielen, H., and de Wit, A. (2001). Crop Yield Forecasting Simulation Study (MCYF). Harare: University of Zimbabwe.
[16]
Rahman, M. M., and Robson, J. A. (2016). A Novel Approach For Sugarcane Yield Prediction Using Landsat Time Series Imagery. A Case Study On Bundaberg Region. Advances In Remote Sensing, 33: 1087-1102.
[17]
Abbas, H. M., and Hag, A. M. (2013). Crop Assessment And Monitoring For Sugarcane Crop, Sudan (New Halfa Case Study) Using Remote Sensing And GIS Techniques. International Journal Of Scientific And Research Publications, 27: 2250-3153.
[18]
Mutanga, S., van Schoor, C., Oloruriju, P., Gonah, T., and Ramoelo, A. (2013). Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery. Advances in Remote Sensing, 2013, 2, 269-275.
[19]
Jia, K., Liang, S., Zhang, N., Wei, X., Gu, X., Zhao, X., Yao, Y. and Xie, X. (2014). Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote. Sensing, 93: 49-55.
[20]
Sun, Y., Frankenberg, C., Wood, J. D., Schimel, M and Jung, L. (2017). Photosynthesis Observation From Space Via Solar-Induced Chlorophyll Flourescene. New York.
[21]
Panda, S. S., Hoogenboom, G., and Paz, O. J. (2010). Remote Sensing and Geospatial Technological Applications For Site-Specific Management Of Fruit And Nut Crops. Washington: Washington State University.
[22]
Marchiori, P. E. R.; Ribeiro, R. V.; da Silva, L.; Machado, R. S.; Machado, E. C.; Scarpari, M. S. (2010). Plant growth, canopy photosynthesis, and light availability in three sugarcane varieties. Sugar Tech. 12, 160–166.
[23]
Andrew, J. E., John, F. M., Maning, S. J., and David, B. L. (2000). Quantifying Vegetation Change In Semi-Arid Environment: Recesion And Accuracy Of Spectral Mixture Analysis And The Normalised Difference Vegetation Index. Remote Sensing Of Environment, 18: 87-102.
[24]
Rokhmana, C. A. (2015). The Potential of UAV-Based Remote Sensing for Supporting Precision Agriculture in Indonesia. Procedia Environmental Sciences, 24: 245-253.
[25]
Vibhute, D. A., and Gawali, B. W. (2013). Analysis And Modeling Of Agricultural Land Use Using Remote Sensing And Geographic Information System. India: Marathwada University.
[26]
Sriroth, K., Wirat, V., and Jackapon, S. (2016). The current status of sugar industry and byproducts in Thailand. Sugar Tech, 18 (6): 576– 582.
[27]
Stanton, C., Michael, J., Norman, E., Michael, B. and Tianxing C. (2017). Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment. Journal of Applied Remote Sensing, 11 (2): 026035.
[28]
Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens. 2013, 5, 949–981.
[29]
Morel, J., Todoroff, P., Begue, A., Bury, A., Martine, J. (2014). Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sens. 2014, 6, 6620-6635.
[30]
Baghdadi, N.; Cresson, R.; Todoroff, P.; Moinet, S. (2010). Multitemporal observations of sugarcane by TerraSAR-X images. Sensors. 10, 8899–8919.
[31]
Bramley, R. (2015). Precision Agriculture for the Sugarcane Industry. New York: Palgrave.
[32]
Lilienthal, H., & Schnug, E. (2007). Ground based remote sensing-a new tool for agricultural monitoring. New York: John Wiley and Sons.
[33]
Dlamini, K. (2012). The Use of Normalized Difference Vegetation Index (NDVI) To Predict Sugarcane Yield In Swaziland. Luyengo: University of Swaziland.
[34]
ESWADE. (2018). Smallholder Market-led Project. Siphofaneni: ESWADE.
[35]
USGS (2013). Department of Interior U.S Geographical Survey. Landsat-8 (L8) Data Users Handbook. Science for a Changing World, 2: 1-90.
[36]
Mabaso, S. D. (2015). Remote Sensing Data for Mapping and Monitoring African Savanna Woodlands. PhD Thesis, Aberystwyth University of Wales: United Kingdom.
[37]
Ehlers, P. (2000). Optical remotely sensed time series data for land cover classification. CRC Press London.
[38]
Tucker, C. J. (1979). Red And Photographic Infrared Linear Combinations For Monitoring Vegetation. Remote Sensing Of The Environment, 46 (55): 127-150.
[39]
Caudill, M. (2000). Geographic Information Systems, Remote Sensing, and Biodiversity. Lincoln: University of Nebraska.
[40]
Bastidas-Obando, O and Carbonell-Gonzalez, K. (2007). Evaluating the applicability of MODIS data for forecasting sugarcane yields in Colombia. In proceedings of the International Society of Sugar Cane Technologists (ISSCT), 14: 321–340.
[41]
Mulianga, B., Begue, A., Simoes, M., and Todoroff, P. (2013). Forecasting Regional Sugarcane Yield Based On Time Integral And Spatial Aggregation of MODIS NDVI. Remote Sensing, 7: 2184-2199.
[42]
Gunnula, W., Righetti, T., Kosittrakun and Prabpan, M. (2011). Relationship between MODIS NDVI and rainfall patterns for sugarcane farmers’ fields in northeastern Thailand. Aust. Journal of Crop Science, 5: 1845-1851.
[43]
Jurecka, P. H and Zdenek, Z. (2016). Crop Yield Estimation in the Field Level Using Vegetation Indices. Mendel University: Czech Republic.
[44]
Hadsarang and Sukmang (2000). “Comparison of broad-band and narrow-band red and near-infrared vegetation indices.” Remote Sensing of Environment, 54 (1) 38–48.
[45]
Rudorff, B. F., and Batista, G. T. (1990). Interpretation Of Remotely Multi-Spectral Imagery Of Agricultural Crops. U.S.A: Remote Sensing, 21: 1–8.
Open Science Scholarly Journals
Open Science is a peer-reviewed platform, the journals of which cover a wide range of academic disciplines and serve the world's research and scholarly communities. Upon acceptance, Open Science Journals will be immediately and permanently free for everyone to read and download.
CONTACT US
Office Address:
228 Park Ave., S#45956, New York, NY 10003
Phone: +(001)(347)535 0661
E-mail:
LET'S GET IN TOUCH
Name
E-mail
Subject
Message
SEND MASSAGE
Copyright © 2013-, Open Science Publishers - All Rights Reserved