Distinguishing Rain-fed and Irrigated Crops in Hamadan Province Using Spectral Indices of Satellite Images

Document Type : Research Article

Authors

• H. Mohamadi-Monavar ¹
• S. Zibazadeh ²

¹ Department of Bio-system Engineering, Faculty of Agriculture, Bu-ali Sina University, Hamadan, Iran

² MSc graduate, Department of Bio-system Engineering, Faculty of Agriculture, Bu-ali Sina University, Hamadan, Iran

https://doi.org/10.22067/jam.2021.69074.1022

Abstract

Introduction
Remote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall which has a significant effect on the spectral characteristics of plants. In this study, Landsat 8 and MODIS satellite images were used to identify and separate two irrigated and rain-fed wheat farms in Hamadan province. Two algorithms of support vector machine (SVM) and minimum distance (MD) were used simultaneously to classify irrigated and rain-fed farms. In the next step, the area under cultivation of rain-fed and irrigated wheat was predicted in the whole cultivated area of Hamadan province. Finally, the cultivation area of rain-fed and irrigated crops was calculated in the province using Sentinel 3 satellite images based on the random forest algorithm in 2016.
Materials and Methods
The study area is Hamedan province, which is located between 59^◦ 33′ and 49^◦ 35′ north latitude and also from 34^◦ 47′ to 34^◦ 49′ east longitude of the Greenwich meridian. A 50-hectare rain-fed wheat farm in Amzajerd was used as a sample to extract the properties of rain-fed wheat. Also, irrigated indices were extracted from a 100-hectare irrigated wheat farm located in Kaboudrahang. Satellite images were applied to separate irrigated and rain-fed wheat in Hamadan province. NDVI, EVI and NDWI indices were extracted from 16-day images of Landsat, MODIS, and Sentinel 3 sensors in the five-year period (2015-2019). Google Earth Engine (GEE) system was the environment for performing image processing calculations and extracting indices and maps.
Results and Discussion
The NDVI and EVI of irrigated and rain-fed wheat farms were calculated in 2015-2019. A small peak was observed in the rain-fed and irrigated NDVI trend in November due to the early germination of wheat leaves in winter, and the larger peak in May and June showed the maximum greenness of irrigated and rain-fed wheat, respectively. The ascending or descending trend of NDVI / EVI had no constant slope. This can be due to changes in meteorological parameters, which sometimes cause a sudden increase or decrease in the values of these indices. Despite the non-linearity of the NDVI / EVI trend over time, the maximum greenness was recorded just a month before the wheat harvest, which was seen in the third decade of May to the first decade of June. One of the cases is the sharp drop of NDVI / EVI after its final peak, which was definitely due to yellowing wheat and harvesting. Since the distinction between rain-fed and irrigated crops was difficult only based on NDVI, NDWI was also used to determine the water content of wheat so that irrigated wheat could be identified. However, the difference between rain-fed and irrigated wheat in terms of NDWI spectral density was insignificant; the maximum and minimum occurrence times of NDWI and NDVI of rain-fed and irrigated wheat were chosen for their separation. In order to map the cultivation area, in addition to the MODIS sensor, Sentinel 3 was used due to its ability to detect chlorophyll accurately. Due to the fact that the imaging of the Sentinel 3 satellite started since 2016, the map of rain-fed and irrigated cultivation as well as the cultivation area and their separation was done based on the random forest algorithm in 2016.
Conclusion
The results of this study showed that the appropriate method for distinguishing between rain-fed and irrigated wheat is the simultaneous use of several indices. Also, the greatest difference is in the maximum greenness, which happened almost one month before harvest. MD and SVM classification algorithms could distinguish irrigated and rain-fed wheat from other crops with 90% and 80% accuracy, respectively. Distinguished maps of irrigated and rain-fed crops based on the random forest algorithm were obtained using Sentinel 3 satellite imagery which can show the fertility of agricultural lands in the province.

Keywords

• Distinguishing of rainfed and irrigated
• Satellite images
• Vegetation indices
• Wheat

20.1001.1.22286829.1401.12.4.6.2

Main Subjects

• Precision Farming

Open Access

©2022 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Ajadi O., Barr, J., Liang, S. Z., Ferreira, R., Kumpatla, S. P., Patel, R., & Swatantran, A. (2021). Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery. International Journal of Applied Earth Observations and Geoinformation, 97, 1-16. https://doi.org/10.1016/j.jag.2020.102294
2. Akbari, M., Mamanpoush, A. R., Gieske, A., Miranzadeh, M., Torabi, M., & Salemi, H. R. (2006). Crop and land cover classification in Iran using Landsat 7 imagery. International Journal of Remote Sensing, 27(19), 4117-4135. https://doi.org/10.1080/01431160600784192
3. Alexandridis, T. K., Zalidis, G. C., & Silleos, N. G. (2008). Mapping irrigated area in Mediterranean basins using low cost satellite Earth Observation. Computers and Electronics in Agriculture, 64(2), 93-103. https://doi.org/10.1016/j.compag.2008.04.001
4. Alipour, F., Agh-Khani, M. H., Abbaspour-Fard, M. H., & Sepehr, A. (2014). Limiting and estimating the area under cultivation of agricultural products to help satellite images (Case study: Astan Quds Razavi sample farm). Journal of Agricultural Machinery, 4(2), 244-254. (in Persian). https://doi.org/10.22067/jam.v4i2.34827
5. Arekhi, S., & Adib-nejad, M. (2011). Evaluating the efficiency of support vector machine algorithms for land use classification using Landsat + ETM satellite data (Case study: Ilam area). Iranian Range and Desert Research, 3(44), 420-440. (in Persian).
6. Bazzi, H., Baghdadi, N., Ienco, D., El Hajj, M., Zribi, M., Belhouchette, H., Escorihuela, M. J., & Demarez, V. (2019). Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain. Remote Sensing, 11, 1836. https://doi.org/10.3390/rs11151836
7. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
8. Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9(1-2), 161-173. https://doi.org/10.1080/02757259409532220
9. Cheng, Y. B., Zarco-Tejada, P. J., Riaño, D., Rueda, C. A., & Ustin, S. L. (2006). Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS andMODIS indexes. Remote Sensing of Environment, 105(4), 30 2006, 354-366
10. Delloye, C., Weiss, M., & Defourny, P. (2018). Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment, 216, 245-261. https://doi.org/10.1016/j.rse.2018.06.037
11. Demarez, V., Helen, F., Marais-Sicre, C., & Baup, F. (2019). In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series. Remote Sensing, 11(2), 118. https://doi.org/10.3390/rs11020118
12. Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., & Hughesg, M. K. (2003). Hughes. Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sensing of Environment, 84, 393-410. https://doi.org/10.1016/S0034-4257(02)00130-X
13. Droogers, P. 2002. Global irrigated area mapping: overview and recommendations, Working Paper 36, International Water Management Institute. Colombo, Sri Lanka.
14. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Famiglietti, J. S., & Rodell, M. (2013). Water in the balance. Science 340(6138), 1300-1301. https://doi.org/10.1126/science.1236460
15. Farajzadeh, M. (2005). Drought from Concept to Solutions. National Geographical Organization Publication.
16. Fatemi, S. B., & Rezaee, F. (2005). Fundamental of Remote Sensing. 1st Pub, Azade Publication. Tehran.
17. Ferrant, S., Selles, A., Le Page, M., Herrault, P. A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., & Saqalli, M. (2017). Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in south India. Remote Sensing, 9, 11-19. https://doi.org/10.3390/rs9111119
18. Gao, B. C. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environment, 58, 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
19. Gao, F., Schaaf, C. B., Strahler, A. H., Roesch, A., Lucht, W., & Dickinson R. (2005). MODIS biodirectional reflectance distribution function and albedo climate modeling grid products and the variability of albedo for major global vegetation types. Journal of Geophysical Research Atmospheres, 110, 1-13. https://doi.org/10.1029/2004JD005190
20. Giannini, A., & Bagnoni, V. (2000). Schede di tecnica irrigua per l’agricoltura toscana. ARSIA– Servizio Telematico Irrigazione. Regione Toscana, EFFEMME Lito, Firenze, pp. 66-97 ISBN 88-8295-015-018.
21. Gupta, O., Das, A. J., Hellerstein, J., & Raskar, R. (2018). Machine Learning approaches for large scale classification of produce, Scientific Reports Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. 8:5226. DOI: https://doi.org/10.1038/s41598-018-23394-3
22. Guzinski, R., & Nieto, H. (2019). Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sensing of Environment, 221, 157-172. https://doi.org/10.1016/j.rse.2018.11.019
23. Hartmann, D. L., Tank, A. M. K., Rusticucci, M., Alexander, L. V., Brönnimann, S., Charabi, Y. A. R., Dentener, F. J., Dlugokencky, E. J., Easterling, D. R., & Kaplan, A. (2013). Observations: atmosphere and surface. Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.
24. Heenkenda, M. K., Joyce, K. E., Maier, S. W., & De Bruin, S. (2015). Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS Photogrammetry of Remote Sensing, 108, 234-244. https://doi.org/10.1016/j.isprsjprs.2015.08.003
25. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.‏
26. Immitzer, M., Vuolo, F., Atzberger, C., Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8, 166. https://doi.org/10.3390/rs8030166
27. Mahmoud, A. M. A., Hasmadi, M., Alias, M. S., & Alias, M. A. (2016). Rangeland degradation assessment in the south slope of the Al-Jabal Al-Akhdar, northeast Libya using remote sensing technology. Rangeland Science, 6(1), 73-81.
28. Martimort, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36.
29. Matsushita, B., Wei, Y., Jin, C., Yuyichi, O., & Guoyn, Q. (2007). Sensivity of the enhanced vegetation index (EVI) and NDVI to topographic effects: A case study in high-density Cypress forest. Sensors, 7(11), 2636-2651. https://doi.org/10.3390/s7112636
30. Morfitt, R., Barsi, J., Levy, R., Markham, B., Micijevic, E., Ong, L., Scaramuzza, P., & Vanderwerff, K. (2015). Landsat-8 operational land imager (OLI) radiometric performance on-orbit. Remote Sensors, 7, 2208-2237.
31. Myneni, R., & Williams, D. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49, 200-211. https://doi.org/10.1016/0034-4257(94)90016-7
32. Nguyen, T. T., Hoang, T. D., Pham, M. T., Vu, T. T., Nguyen, T. H., Huynh, Q. T., & Jo, J. (2020). Monitoring agriculture areas with satellite images and deep learning. Applied Soft Computing, 95, 1-16. https://doi.org/10.1016/j.asoc.2020.106565
33. Pageot, Y., Bau, F., Inglada, J., Baghdadi, N., & Demarez, V. (2020). Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing, 12, 1-19. https://doi.org/10.3390/rs12183044
34. Pastor-Guzman, J., Brown, L., Morris, H., Bourg, L., Goryl, P., Dransfeld, S., & Dash, J. (2020). The sentinel-3 OLCI terrestrial chlorophyll index (OTCI): algorithm improvements, spatiotemporal consistency and continuity with the MERIS archive. Remote Sensing, 12, 2652-2674. https://doi.org/10.3390/rs12162652
35. Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., & Dedieu, G. (2017). Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sensing, 9, 173. https://doi.org/10.3390/rs9020173
36. Peña-Arancibia, J. L., McVicar, T. R., Paydar, Z., Li, L., Guerschman, J. P., Donohue, R. J., Dutta, D., Podger, G. M., van Dijk, A. I. J. M., & Chiew, F. H. S. (2014). Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability. Remote Sensing of Environment, 154, 139-152. https://doi.org/10.1016/j.rse.2014.08.016
37. Rahimzadegan, M., & Pourgholam, M. (2016). Determining the area under saffron cultivation using Landsat images (Case study: City Torbat Heydariyeh). Remote Sensing and GIS in Natural Resource, 7(4), 97-115. (in Persian). https://doi.org/10.22048/jsat.2017.48518.1194
38. Schucknecht, A., Erasmi, S., Niemeyer, I., & Matschullat, J. (2013). Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series. Remote Sensing, 46, 40-59. https://doi.org/10.5721/EuJRS20134603
39. Sepulcre-Canto, G., Zarco-Tejada, P. J., Sobrino, J. A., Berni, J. A. J., Jimenez-Munoz, J. C., & Gastellu-Etchegorry, J. P. (2008). Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. Agricultural and Forest Meteorology, 149, 962-975. https://doi.org/10.1016/j.agrformet.2008.12.001
40. Shamal, S. A. M., & Weatherhead, K. (2014). Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping. IP Publication Ltd, 43(2), 109-114. https://doi.org/10.5367/oa.2014.0168
41. Tso, B., & Mather, P. (2009). Support Vector machines, in Classification Methods for Remotely sensed Data. 1st ed: CRC Press: 125-153.
42. Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., Meinshausen, N., & Frieler, K. (2019). The effects of climate extremes on global agricultural yields. Environmental Research Letters, 14(5), 1-13. https://doi.org/10.1088/1748-9326/ab154b
43. Vuolo, F., Dash, J., Curran, P. J., Lajas, D., & Kwiatkowska, E. (2012). Methodologies and uncertainties in the use of the terrestrial chlorophyll index for the Sentinel-3 mission. Remote Sensing, 4, 1112-1133. https://doi.org/10.3390/rs4051112
44. Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., & Ng, W. T. (2018). How much does multi-temporal Sentinel-2 data improve crop type classification? Applied Earth Observation and Geo-information, 72, 122-130. https://doi.org/10.1016/j.jag.2018.06.007
45. Wacker, A. G., & Landgrebe, D. A. (1972). Minimum Distance Classification in Remote Sensing. LARS Technical Reports. Paper 25. https://docs.lib.purdue.edu/larstech/25
46. Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021