Abstract
This study aims to provide accurate information about changes in agricultural systems (AS) using phenological metrics derived from the NDVI time series. Use of such information could help land managers optimize land use choices and monitor the status of agricultural lands, under a variety of environmental and socioeconomic conditions. For this purpose, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data were used to derive phenological metrics over the Oum Er-Rbia basin (central Morocco). Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers were explored and compared on their ability to classify AS classes over the study area. Four main AS classes have been considered: (1) irrigated annual crop (IAC), (2) irrigated perennial crop (IPC), (3) rainfed area (RA), and (4) fallow (FA). By comparing the accuracy of the three classifiers, the RF method showed the best performance with an overall accuracy of 0.97 and kappa coefficient of 0.96. The RF method was then chosen to examine time variations in AS over a 16-year period (2000–2016). The AS main variations were detected and evaluated for the four AS classes. These variations have been found to be linked well with other indicators of local agricultural land management, as well as the historical agricultural drought changes over the study area. Overall, the results present a tool for decision makers to improve agricultural management and provide a different perspective in understanding the spatiotemporal dynamics of agricultural systems.
Similar content being viewed by others
References
Adole T, Dash J, Atkinson PM (2018) Characterising the land surface phenology of Africa using 500 m MODIS EVI. Appl Geogr 90:187–199. https://doi.org/10.1016/j.apgeog.2017.12.006
Akhtar F, Awan UK, Tischbein B, Liaqat UW (2017) A phenology based geo-informatics approach to map land use and land cover (2003–2013) by spatial segregation of large heterogenic river basins. Appl Geogr 88:48–61. https://doi.org/10.1016/j.apgeog.2017.09.003
Alcantara C, Kuemmerle T, Prishchepov AV, Radeloff VC (2012) Mapping abandoned agriculture with multi-temporal modis satellite data. Remote Sens Environ 124:334–347. https://doi.org/10.1016/j.rse.2012.05.019
Atkinson PM, Jeganathan C, Dash J, Atzberger C (2012) Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens Environ 123:400–417. https://doi.org/10.1016/j.rse.2012.04.001
Atzberger C, Klisch A, Mattiuzzi M, Vuolo F (2013) Phenological metrics derived over the European continent from NDVI3g data and MODIS time series. Remote Sens 6:257–284. https://doi.org/10.3390/rs6010257
Bachoo A, Archibald S (2007) Influence of using date-specific values when extracting phenological metrics from 8-day composite ndvi data. In: 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images, 18–20 July 2007, pp 1–4. https://doi.org/10.1109/MULTITEMP.2007.4293044
Bai ZG, Dent DL, Olsson L, Schaepman ME (2008) Proxy global assessment of land degradation. Soil Use Manag 24:223–234. https://doi.org/10.1111/j.1475-2743.2008.00169.x
Benabdelouahab T, Balaghi R, Hadria R, Lionboui H, Minet J, Tychon B (2015) Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions. Int J Remote Sens 36:4018–4036. https://doi.org/10.1080/01431161.2015.1072650
Benabdelouahab T, Derauw D., Lionboui H., Hadria R., Tychon B., Boudhar A., Balaghi R., Lebrini Y., Maaroufi H., Barbier C. (2019a) Using SAR data to detect wheat irrigation supply in an irrigated semi-arid area vol 11. https://doi.org/10.5539/jas.v11n1p21
Benabdelouahab T, Gadouali F, Boudhar A, Lebrini Y, Hadria R, Salhi A (2020) Analysis and trends of rainfall amounts and extreme events in the western Mediterranean region. Theor Appl Climatol. https://doi.org/10.1007/s00704-020-03205-4
Benabdelouahab T, Lebrini Y, Boudhar A, Hadria R, Htitiou A, Lionboui H (2019b) Monitoring spatial variability and trends of wheat grain yield over the main cereal regions in morocco: a remote-based tool for planning and adjusting policies. Geocarto Int:1–20. https://doi.org/10.1080/10106049.2019.1695960
Boudhar A, Hanich L, Boulet G, Outaleb K, Arioua A, Hakkani B (2014) Etude de la disponibilité des ressources en eau à l'aide de la télédétection et la modélisation: Cas du bassin versant d'oum er rbia (maroc). J Int Sci Tech Environ:43–47
Boudhar A et al (2020) In: Rebai N, Mastere M (eds) Hydrological response to snow cover changes using remote sensing over the Oum Er Rbia upstream basin, Morocco. Springer International Publishing, pp 95–102. https://doi.org/10.1007/978-3-030-21166-0_9
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Carrão H, Gonçalves P, Caetano M (2008) Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens Environ 112:986–997. https://doi.org/10.1016/j.rse.2007.07.002
Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ 91:332–344. https://doi.org/10.1016/j.rse.2004.03.014
Chen JL, Wilson CR, Seo KW (2006) Optimized smoothing of Gravity Recovery and Climate Experiment (GRACE) time-variable gravity observations. J Geophys Res Solid Earth 111:n/a-n/a. https://doi.org/10.1029/2005JB004064
Clauss K, Yan H, Kuenzer C (2016) Mapping paddy rice in China in 2002, 2005, 2010 and 2014 with MODIS time series. Remote Sens 8:434
Cui T, Martz L, Guo X (2017) Grassland phenology response to drought in the Canadian prairies. Remote Sens 9:1258. https://doi.org/10.3390/rs9121258
del Barrio G et al (2016) Land degradation states and trends in the northwestern Maghreb drylands, 1998–2008. Remote Sens 8:603. https://doi.org/10.3390/rs8070603
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ 118:259–272. https://doi.org/10.1016/j.rse.2011.11.020
Eklundh L, Jönsson P (2015) Timesat 3.2 software manual. Lund and Malmö University, Sweden
Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182. https://doi.org/10.1016/j.rse.2009.08.016
Fu Y, He H, Zhao J, Larsen D, Zhang H, Sunde M, Duan S (2018) Climate and spring phenology effects on autumn phenology in the Greater Khingan Mountains, northeastern China. Remote Sens 10:449. https://doi.org/10.3390/rs10030449
Gao F et al (2008) An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci Remote Sens Lett 5:60–64. https://doi.org/10.1109/LGRS.2007.907971
Geng L, Ma M, Wang X, Yu W, Jia S, Wang H (2014) Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe River basin, China. Remote Sensing 6:2024
Gu Y, Brown JF, Verdin JP, Wardlow B (2007) A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the Central Great Plains of the United States. Geophys Res Lett 34:34. https://doi.org/10.1029/2006GL029127
Hadria R et al (2019) Derivation of air temperature of agricultural areas of Morocco from remotely land surface temperature based on the updated Köppen-Geiger climate classification. Mod Earth Syst Environ. https://doi.org/10.1007/s40808-019-00645-4
Hao P, Zhan Y, Wang L, Niu Z, Shakir M (2015) Feature selection of time series MODIS data for early crop classification using random forest: a case study in Kansas, USA. Remote Sens 7:5347–5369. https://doi.org/10.3390/rs70505347
Hentze K, Thonfeld F, Menz G (2016) Evaluating crop area mapping from MODIS time-series as an assessment tool for Zimbabwe’s “fast track land reform programme”. PLoS One 11:e0156630. https://doi.org/10.1371/journal.pone.0156630
Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification
Htitiou A, Boudhar A, Lebrini Y (2019) The performance of random forest classification based on phenological metrics derived from Sentinel-2 and Landsat 8 to map crop cover in an irrigated semi-arid region. Remote Sens Earth Syst Sci 2:208–224. https://doi.org/10.1007/s41976-019-00023-9
Htitiou A, Boudhar A, Lebrini Y, Hadria R, Lionboui H, Benabdelouahab T (2020) A comparative analysis of different phenological information retrieved from sentinel-2 time series images to improve crop classification: a machine learning approach. Geocarto Int:1–24. https://doi.org/10.1080/10106049.2020.1768593
Huang C, Davis LS, JRG T (2010) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749. https://doi.org/10.1080/01431160110040323
Jin Y, Sung S, Lee D, Biging G, Jeong S (2016) Mapping deforestation in North Korea using phenology-based multi-index and random forest. Remote Sens 8:997. https://doi.org/10.3390/rs8120997
Jönsson P, Cai Z, Melaas E, Friedl M, Eklundh L (2018) A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sens 10:635. https://doi.org/10.3390/rs10040635
Jönsson P, Eklundh L (2002) Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans Geosci Remote Sens:40
Jönsson P, Eklundh L (2004) Timesat a program for analyzing time-series of satellite sensor data. Comput Geosci 30:833–845. https://doi.org/10.1016/j.cageo.2004.05.006
Kariyeva J, van Leeuwen WJD (2012) Phenological dynamics of irrigated and natural drylands in central Asia before and after the USSR collapse. Agric Ecosyst Environ 162:77–89. https://doi.org/10.1016/j.agee.2012.08.006
Kuhn M (2008) Building predictive models in r using the caret package 2008 28:26%. J Stat Softw. https://doi.org/10.18637/jss.v028.i05
Lebrini Y, Benabdelouahab T, Boudhar A, Htitiou A, Hadria R, Lionboui H Farming systems monitoring using machine learning and trend analysis methods based on fitted NDVI time series data in a semi-arid region of Morocco. In: SPIE Remote Sensing, Strasbourg, France., 2019a. SPIE. https://doi.org/10.1117/12.2532928
Lebrini Y et al (2019b) Identifying agricultural systems using SVM classification approach based on phenological metrics in a semi-arid region of Morocco. Earth Syst Environ. https://doi.org/10.1007/s41748-019-00106-z
Li L, Friedl M, Xin Q, Gray J, Pan Y, Frolking S (2014) Mapping crop cycles in China using MODIS-EVI time series. Remote Sens 6:2473–2493. https://doi.org/10.3390/rs6032473
Lieth H (1974) Phenology and seasonality modeling vol 8. Springer-Verlag, New York
Lionboui H, Benabdelouahab T, Elame F, Hasib A, Boulli A (2016) Multi-year agro-economic modelling for predicting changes in irrigation water management indicators in the Tadla sub-basin. Int J Agric Manag 5:96–105. https://doi.org/10.5836/ijam/2016-05-96
Lionboui H, Benabdelouahab T, Hasib A, Fouad E, Abdelali B (2018) Dynamic agro-economic modeling for sustainable water resources management in arid and semi-arid areas. In: Handbook of environmental materials management. https://doi.org/10.1007/978-3-319-58538-3_114-1
Lionboui H, Benabdelouahab T, Htitiou A, Lebrini Y, Boudhar A, Hadria R, Elame F (2020) Spatial assessment of losses in wheat production value: a need for an innovative approach to guide risk management policies. Remote Sens Applic: Soc Environ 18:18. https://doi.org/10.1016/j.rsase.2020.100300
Meyer H, Reudenbach C, Hengl T, Katurji M, Nauss T (2018) Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ Model Softw 101:1–9. https://doi.org/10.1016/j.envsoft.2017.12.001
NASA LP DAAC (2017) mod13q1. Version 6. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.Usgs.Gov), accessed april 13, 2017, . https://doi.org/10.5067/MODIS/MOD13Q1.006
Ouatiki H, Boudhar A, Ouhinou A, Arioua A, Hssaisoune M, Bouamri H, Benabdelouahab T (2019) Trend analysis of rainfall and drought over the Oum Er-Rbia River Basin in Morocco during 1970–2010. Arab J Geosci 12:12. https://doi.org/10.1007/s12517-019-4300-9
Ouatiki H et al (2017) Evaluation of TRMM 3b42 v7 rainfall product over the Oum Er Rbia watershed in Morocco. Climate 5:1. https://doi.org/10.3390/cli5010001
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222. https://doi.org/10.1080/01431160412331269698
Qian Y, Zhou W, Yan J, Li W, Han L (2014) Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sens 7:153–168. https://doi.org/10.3390/rs70100153
Qiu B et al (2017) Mapping cropping intensity trends in china during 1982–2013. Appl Geogr 79:212–222. https://doi.org/10.1016/j.apgeog.2017.01.001
R Core Team (2017) R: A language and environment for statistical computing
Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Salhi A, Benabdelouahab T, Martin-Vide J, Okacha A, el Hasnaoui Y, el Mousaoui M, el Morabit A, Himi M, Benabdelouahab S, Lebrini Y, Boudhar A, Casas Ponsati A (2020) Bridging the gap of perception is the only way to align soil protection actions. Sci Total Environ 718:137421. https://doi.org/10.1016/j.scitotenv.2020.137421
Samworth RJ (2012) Optimal weighted nearest neighbour classifiers. Ann Stat 40:2733–2763. https://doi.org/10.1214/12-AOS1049
Schwartz MD (2003) Phenology: an integrative environmental science. Tasks for vegetation science. Kluwer academic publishers, Dordrecht, The Netherlands, p 39
Shao Y, Lunetta RS (2012) Comparison of support vector machine, neural network, and cart algorithms for the land-cover classification using limited training data points. ISPRS J Photogramm Remote Sens 70:78–87. https://doi.org/10.1016/j.isprsjprs.2012.04.001
Suepa T, Qi J, Lawawirojwong S, Messina JP (2016) Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon southeast asia. Environ Res 147:621–629. https://doi.org/10.1016/j.envres.2016.02.005
Sun H, Wang Q, Wang G, Lin H, Luo P, Li J, Zeng S, Xu X, Ren L (2018) Optimizing kNN for mapping vegetation cover of arid and semi-arid areas using Landsat images. Remote Sens 10:1248. https://doi.org/10.3390/rs10081248
Vapnik VN (2006) Estimation of dependence based on empirical data: empirical inference science afterword of 2006. Information science and statistics. Springer-Verlag, New York. https://doi.org/10.1007/0-387-34239-7
Wang D et al (2018) Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sens 10:1468. https://doi.org/10.3390/rs10091468
Wessel M, Brandmeier M, Tiede D (2018) Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sens 10:1419. https://doi.org/10.3390/rs10091419
Wessels KJ, Bachoo A, Archibald S (2009) Influence of composite period and date of observation on phenological metrics extracted from MODIS data. Paper presented at the 33rd International Symposium on Remote Sensing of Environment: Sustaining the Millennium Development Goals, Stresa, Lago Magglore, Italy, 4-8 May 2009
Winkler K, Gessner U, Hochschild V (2017) Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000–2016: rainfall anomalies and vegetation condition in the context of ENSO. Remote Sens 9:831
Yu L, Su J, Li C, Wang L, Luo Z, Yan B (2018) Improvement of moderate resolution land use and land cover classification by introducing adjacent region features. Remote Sens 10:414. https://doi.org/10.3390/rs10030414
Acknowledgments
The authors are grateful to NASA’s team, for creating and making the vegetation index product MOD13Q1 freely available. We acknowledge the CNRST (National Center of Scientific and Technical Researches, Morocco) for financial support as a scholarship for the first author, Youssef Lebrini.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Lebrini, Y., Boudhar, A., Htitiou, A. et al. Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy. Arab J Geosci 13, 796 (2020). https://doi.org/10.1007/s12517-020-05789-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-020-05789-7