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Detection of seasonal land use pattern and irrigated crops in drylands using multi-temporal sentinel images

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Abstract

The objective of this research is to detect seasonal agricultural land use and cropping pattern of irrigated crops during the 2017/2018 growing season in the Mujib basin in Jordan. The Mujib basin is considered as one of the major sources for water production in Jordan, while the agricultural production is the main consumer sector for water in the basin. High multi-temporal multi-spectral resolutions Sentinel-2b satellite images were used to identify the land use types using the supervised classification that applied on spring and summer growing season. The results show that the temporal NDVI curve is very useful in distinguishing between different crops type with high details, and is used in assisting the agricultural land use pattern. Multi-temporal NDVI images were used to identify the cropping pattern within the agricultural land using the unsupervised classification approach. The results show that the rainfed agriculture is confined in the high rainfall zones with one period of growing season, while the irrigated agricultural crops are spreading in the basin over the whole year with different periods of growing seasons. Irrigated vegetables and tree crops are the dominant agricultural crops in the basin with more than 70% of cultivated land. Most of the irrigated vegetables and trees are grown in the summer seasons under high temperatures conditions which consumes the highest percentage of irrigated water. These results show that the patterns of agricultural land use under Dryland regions are very essential issue in determining water consumption and management alternatives.

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References

  • Abdulla F, Al-Assa’d T (2006) Modeling of groundwater flow for Mujib aquifer, Jordan. J Earth Syst Sci 115(3):289–297

    Article  Google Scholar 

  • Abu-Allaban M, El-Naqa A, Jaber MH, N, (2015) Water scarcity impact of climate change in semi-arid regions: a case study in Mujib basin, Jordan. Arabian J Geosci 8(2):951–959

    Article  Google Scholar 

  • Ahmed SA, Kheiry MA, Hassan IM (2018) Applications of remote sensing on vegetation cover changes in arid regions. Gum Arabic. https://doi.org/10.1016/B978-0-12-812002-6.00004-X

    Article  Google Scholar 

  • Alkhatib J, Engelhardt I, Ribbe L, Sauter M (2019) An integrated approach for choosing suitable pumping strategies for a semi-arid region in Jordan using a groundwater model coupled with analytical hierarchy techniques. Hydrogeol J 27(2):1–15

    Google Scholar 

  • Anderson JR, Hardy E, Roach J, Witmer R (1976) A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Profession Paper, 964. Washington, DC. 24 p

  • Bégué A, Arvor D, Bellon B, Betbeder J, De Abelleyra D, Ferraz R, Verón S (2018) Remote sensing and cropping practices: A review. Remote Sens 10(1):99

    Article  Google Scholar 

  • Belgiu M, Csillik O (2018) Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ 204:509–523

    Article  Google Scholar 

  • Cabello KE, Germentil MQ, Blanco AC, Macatulad EG, Salmo SG (2021) Post-Disaster Assessment of Mangrove Forest Recovery in Lawaan-Balangiga, Eastern Samar using NDVI time series analysis. ISPRS Ann Photogramm Remote Sens Spatial Inform Sci 3:243–250

    Article  Google Scholar 

  • Chen Z, Xu Y, He Y, Zhou X, Fan J, Yu H, Ding W (2018) Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: a greater role of organic over inorganic fertilizer. Soil Biol Biochem 116:253–264

    Article  Google Scholar 

  • Clark M, Tilman D (2017) Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environm Res Lett 12(6):064016

    Article  Google Scholar 

  • Dash J, Mathur A, Foody GM, Curran PJ, Chipman JW, Lillesand TM (2007) Land cover classification using multi-temporal MERIS vegetation indices. Int J Remote Sens 28(6):1137–1159

    Article  Google Scholar 

  • Demarez V, Helen F, Marais-Sicre CB (2019) In-season mapping of irrigated crops using landsat 8 and Sentinel-1 Time Series. Remote Sens 11(2):118

    Article  Google Scholar 

  • Denize J, Hubert-Moy L, Corgne S, Betbeder J, Pottier E (2018) Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series. In: IGARSS- IEEE Int Geosci Remote Sens Symposium. https://doi.org/10.1109/IGARSS.2018.8517673

  • Dheeravath V, Thenkabail PS, Chandrakantha G, Noojipady P, Reddy GPO, Biradar CM, Velpuri M (2010) Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS J Photogramm Remote Sens 65(1):42–59

    Article  Google Scholar 

  • Du T, Kang S, Sun J, Zhang XZ (2010) An improved water use efficiency of cereals under temporal and spatial deficit irrigation in north China. Agric Water Manag 97(1):66–74

    Article  Google Scholar 

  • Estel S, Kuemmerle T, Levers C, Baumann M, Hostert P (2016) Mapping cropland-use intensity across Europe using MODIS NDVI time series. Environm Res Lett 11(2):024015

    Article  Google Scholar 

  • EXACT (2006) Application of methods for analysis of rainfall intensity in areas of Israeli, Jordanian, and Palestinian interest. Executive Action Team, Middle East Water Data Banks Project. http://exactme.org/ri/rain2/EXACT.Rainfall.Intensity.rev1.pdf

  • Ferrant S, Selles A, Le Page M, Herrault PA, Pelletier C, Al-Bitar A, Dewandel B (2017) Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in South India. Remote Sens 9(11):1119

    Article  Google Scholar 

  • Garibaldi LA, Pérez-Méndez N, Garratt MP, Gemmill-Herren B, Miguez FE, Dicks LV (2019) Policies for ecological intensification of crop production. Trends Ecol Evol 34(4):282–286

    Article  Google Scholar 

  • Griffiths P, Nendel CH (2019) Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens Environ 220:135–151

    Article  Google Scholar 

  • Heupel K, Spengler D, Itzerott S (2018) A progressive crop-type classification using multitemporal remote sensing data and phenological information. PFG J Photogram Remote Sens Geoinform Sci 86(2):53–69

    Google Scholar 

  • Ijam A, Tarawneh E (2012) Assessing of Sediment yield for wala dam catchment area in Jordan. Eur Water 38:43–58

    Google Scholar 

  • Jägermeyr J, Gerten D, Heinke J, Schaphoff S, Kummu ML (2015) Water savings potentials of irrigation systems: dynamic global simulation. Hydrol Earth Syst Sci Discuss 12(4):3593–3644

    Google Scholar 

  • Jain M, Mondal P, DeFries RS, Small C, Galford GL (2013) Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sens Environ 134:210–223

    Article  Google Scholar 

  • Kumaraswamy PR, Singh M (2018) Jordan’s food security challenges. Mediterr Q 29(1):70–95

    Article  Google Scholar 

  • Kussul N, Skakun S, Shelestov A, Lavreniuk M, Yailymov B, Kussul O (2015) Regional scale crop mapping using multi-temporal satellite imagery. Int Arch Photogram Remote Sens Spatial Inform Sci 40(7):45

    Article  Google Scholar 

  • Kussul N, Lemoine G, Gallego FJ, Skakun SV, Lavreniuk M, Shelestov AY (2016) Parcel-based crop classification in ukraine using landsat-8 data and sentinel-1A data. IEEE J Selected Topics Appl Earth Observ Remote Sens 9(6):2500–2508

    Article  Google Scholar 

  • Leff B, Ramankutty N, Foley JA (2004) Geographic distribution of major crops across the world. Global Biogeochem Cycles 18(1):GB1009. https://doi.org/10.1029/2003GB002108

    Article  Google Scholar 

  • Makhamreh Z (2018) Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images. Environm Earth Sci 77:384. https://doi.org/10.1007/s12665-018-7534-z)

    Article  Google Scholar 

  • Makhamreh Z (2010) Monitoring the vegetation characteristics and dynamic as a response to climatic variability in the Eastern Mediterranean region of Jordan. Int Confer Climate Change and Food Security in Dryland Regions, 1–4 Feburary Amman, Jordan

  • Mather PM (1999) Computer processing of remotely-sensed images: An introduction, 3rd edn. John-Willy and Sons, Chichester

    Google Scholar 

  • Meroni M, Fasbender D, Rembold F, Atzberger C, Klisch A (2019) Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options. Remote Sens Environm 221:508–521

    Article  Google Scholar 

  • Mishrai VN, Rai PK, Kumar P, Prasad R (2016) Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images. Forum geografic. Studii și cercetări de geografie și protecția mediului. 15(1): 45–53 https://doi.org/10.5775/fg.2016.137.i 45

  • MOA (2017) Ministry of Agriculture, annual book, Amman, Jordan

  • MWI (2018) Jordanian Ministry of Water and Irrigation, Jordan Water Sector - Facts and Figures, Amman, Jordan

  • Namrouqa H (2014) Jordan world’s second water-poorest country. Jordan Times 22

  • Ottosen TB, Lommen ST, Skjøth CA (2019) Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2. Comput Electron Agric 157:232–238

    Article  Google Scholar 

  • Ouzemou JE, El Harti A, Lhissou R, El Moujahid A, Bouch N, El Ouazzani R, El Ghmari A (2018) Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sens Appl Soc Environm 11:94–103

    Google Scholar 

  • Patel J, Oza M (2014) Deriving crop calendar using NDVI time-Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-8. ISPRS Technical Commission VIII Symposium, 09–12 December, Hyderabad, India

  • 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 Sens 9(2):173

    Article  Google Scholar 

  • Reddy GO, Ramamurthy V, Singh SK (2018) Integrated remote sensing, GIS, and GPS applications in agricultural land use planning. In geospatial technologies in land resources mapping, monitoring and management (pp. 489–515). Springer, Cham.

  • Rufin P, Frantz D, Ernst S, Rabe A, Griffiths P, Özdoğan M (2019) Mapping cropping practices on a national scale using intra-annual landsat time series binning. Remote Sens 11(3):232

    Article  Google Scholar 

  • Song XP, Potapov PV, Krylov A, King L, Di Bella CM, Hudson A, Hansen MC (2017) National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sens Environ 190:383–395

    Article  Google Scholar 

  • Sonobe R, Miura Y, Sano T, Horie H (2018) Estimating leaf carotenoid contents of shade-grown tea using hyperspectral indices and PROSPECT–D inversion. Int J Remote Sens 39(5):1306–1320

    Article  Google Scholar 

  • USGS (1992) Standards for digital line graphs for land use and land cover technical instructions. Referral STO-1–2. Washington, DC: US Government Printing Office. 60 p

  • Vivekananda GN, Swathi R, AVLNS (2020) Multi-temporal image analysis for LULC classification and change detection. Eur J Remote Sens. https://doi.org/10.1080/22797254.2020.1771215

    Article  Google Scholar 

  • Vrable J (1996) Multispectral imagery band sharpening study. Photogramm Eng Remote Sens 52:1075–1083

    Google Scholar 

  • Vuolo F, Neuwirth M, Immitzer M, Atzberger C, Ng WT (2018) How much does multi-temporal Sentinel-2 data improve crop type classification? Int J Appl Earth Obs Geoinf 72:122–130

    Article  Google Scholar 

  • Wang G, Wang J, Zou X, Chai G, Wu M, Wang Z (2019) Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands. Int J Appl Earth Observ Geoinform 76:154–166

    Article  Google Scholar 

  • Whitcraft AK, Vermote EF, Becker-Reshef I, Justice CO (2015) Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations. Remote Sens Environ 156:438–447

    Article  Google Scholar 

  • White MA, Nemani RR, Thornton PE, Running SW (2002) Satellite evidence of phenological differences between urbanized and rural areas of the Eastern United States deciduous broadleaf forest. Ecosystems 5:260–277

    Article  Google Scholar 

  • Zhang Y, Hepner GF (2017) Short-term phenological predictions of vegetation abundance using multivariate adaptive regression splines in the Upper Colorado River Basin. Earth Interact 21(1):1–26

    Article  Google Scholar 

  • Zhang X, Xiao P, Feng X, Yuan M (2017) Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area. Remote Sens Environm 201:243–255

    Article  Google Scholar 

  • Zheng B, Myint SW, Thenkabail PS, Aggarwal RM (2015) A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int J Appl Earth Obs Geoinf 34:103–112

    Article  Google Scholar 

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Authors and Affiliations

  1. Geography Department, The University of Jordan, Amman, Jordan

    Zeyad Makhamreh & Abd Al-Azeez Hdoush

  2. Food and Agriculture Organization of the United Nations, Rome, Italy

    Feras Ziadat

  3. French Department, The University of Jordan, Amman, Jordan

    Shereen Kakish

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  1. Zeyad Makhamreh
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  2. Abd Al-Azeez Hdoush
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  3. Feras Ziadat
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  4. Shereen Kakish
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Correspondence to Zeyad Makhamreh.

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Makhamreh, Z., Hdoush, A.AA., Ziadat, F. et al. Detection of seasonal land use pattern and irrigated crops in drylands using multi-temporal sentinel images. Environ Earth Sci 81, 120 (2022). https://doi.org/10.1007/s12665-022-10249-4

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