A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of S？o Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in S？o Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.
References
[1]
Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens 2013, 5, 949–981.
[2]
Rembold, F.; Atzberger, C.; Rojas, O.; Savin, I. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens. 2013. under review.
[3]
Galv？o, L.S.; Formaggio, A.R.; Tisot, D.A. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sens. Environ 2005, 94, 523–534.
[4]
Fortes, C.; Demattê, J.A.M. Discrimination of sugarcane varieties using Landsat 7 ETM+ spectral data. Int. J. Remote Sens 2006, 27, 1395–1412.
[5]
Rudorff, B.F.T.; De Aguiar, D.A.; Da Silva, W.F.; Sugawara, L.M.; Adami, M.; Moreira, M.A. Studies on the rapid expansion of sugarcane for ethanol production in S？o Paulo State (Brazil) using Landsat data. Remote Sens 2010, 2, 1057–1076.
[6]
Romani, L.A.S.; Goncalves, R.R.V.; Amaral, B.F.; Chino, D.Y.T.; Zullo, J.; Traina, C.; Sousa, E.P.M.; Traina, A.J.M. Clustering Analysis Applied to NDVI/NOAA Multitemporal Images to Improve the Monitoring Process of Sugarcane Crops. Proceedings of the 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), Trento, Italy, 12–14 July 2011; pp. 33–36.
[7]
Xavier, A.C.; Rudorff, B.F.T.; Shimabukuro, Y.E.; Berka, L.M.S.; Moreira, M.A. Multi-temporal analysis of MODIS data to classify sugarcane crop. Int. J. Remote Sens 2006, 27, 755–768.
[8]
de Aguiar, D.A.; Rudorff, B.F.T.; Adami, M.; Shimabukuro, Y.E. Imagens de sensoriamento remoto no monitoramento da colheita da cana-de-a？úcar. Engenharia Agrícola 2009, 29, 440–451.
[9]
Mobasheri, M.R.; Chahardoli, M.; Farajzadeh, M. Introducing PASAVI and PANDVI methods for sugarcane physiological date estimation, using ASTER images. J. Agr. Sci. Tech 2010, 12, 309–320.
[10]
Gon？alves, R.R.V.; Zullo, J.; Romani, L.A.S.; Nascimento, C.R.; Traina, A.J.M. Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil. Int. J. Remote Sens 2012, 33, 4653–4672.
[11]
Almeida, T.I.R.; De Souza Filho, C.R.; Rossetto, R. ASTER and Landsat ETM+ images applied to sugarcane yield forecast. Int. J. Remote Sens 2006, 27, 4057–4069.
[12]
Picoli, M.C.A.; Rudorff, B.F.T.; Rizzi, R.; Giarolla, A. índice de vegeta？？o do sensor MODIS na estimativa da produtividade agrícola da cana-de-a？úcar. Bragantia 2009, 68, 789–795.
[13]
Lofton, J.; Tubana, B.S.; Kanke, Y.; Teboh, J.; Viator, H.; Dalen, M. Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index. Sensors 2012, 12, 7529–7547.
[14]
Bégué, A.; Todoroff, P.; Pater, J. Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series? Precision Agr 2008, 9, 161–171.
[15]
Fernandes, J.L.; Rocha, J.V.; Lamparelli, R.A.C. Sugarcane yield estimates using time series analysis of spot vegetation images. Sci. Agr 2011, 68, 139–146.
[16]
Cheavegatti-Gianotto, A.; De Abreu, H.M.C.; Arruda, P.; Bespalhok Filho, J.C.; Burnquist, W.L.; Creste, S.; Di Ciero, L.; Ferro, J.A.; De Oliveira Figueira, A.V.; De Sousa Filgueiras, T.; et al. Sugarcane (Saccharum X officinarum): A reference study for the regulation of genetically modified cultivars in Brazil. Trop. Plant Biol 2011, 4, 62–89.
[17]
Mello, M.; Adami, M.; Rudorff, B.; Aguiar, D. Canasat Project Accuracy Assessment of Sugarcane Thematic Maps. Proceeding of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Florianopolis, Brazil, 10–13 July 2012; pp. 281–286.
[18]
Weiss, M.; Baret, F.; Eerens, H.; Swinnen, E. FAPAR over Europe for the Past 29 Years: A Temporally Consistent Product Derived from AVHRR and VEGETATION Sensor. Proceeding of the third RAQRS Workshop, Valencia, Spain, 27 September–1 October 2010; pp. 428–433.
[19]
Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Nino, F.; Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ 2007, 110, 275–286.
[20]
Baruth, B.; Royer, A.; Klisch, A.; Genovese, G. The use of remote sensing within the MARS crop yield monitoring system of the European Commission. Int. Arch. Photogramm. Rem. Sens. Spat. Inform. Sci 2008, 37, 935–940.
[21]
Holben, B.N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens 1986, 7, 1417–1434.
[22]
Cracknell, A.P. Synergy in remote sensing–What’s in a pixel? Int. J. Remote Sens 1998, 19, 2025–2047.
[23]
Duveiller, G.; Defourny, P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sens. Environ 2010, 114, 2637–2650.
[24]
Duveiller, G.; Baret, F.; Defourny, P. Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy. Remote Sens. Environ 2011, 115, 2686–2701.
[25]
Duveiller, G.; Baret, F.; Defourny, P. Using thermal time and pixel purity for enhancing biophysical variable time series: an inter-product comparison. IEEE Trans. Geosci. Remote Sens. 2013. in press.
[26]
Réaumur, R.A. Observations du thermomètre faites pendant l’année MDCCXXXV comparées à celles qui ont été faites sous la ligne à Isle-de-France, à Alger et en quelques-unes de nos Isles de l’Amérique. Mémoires de l’Académie Royale des Sciences 1735, 545–576.
[27]
Bonhomme, R. Bases and limits to using ‘degree.day’ units. Eur. J. Agron 2000, 13, 1–10.
[28]
Trudgill, D.L.; Honek, A.; Li, D.; Van Straalen, N.M. Thermal time—Concepts and utility. Ann. Appl. Biol 2005, 146, 1–14.
[29]
Purcell, L.C. Comparison of thermal units derived from daily and hourly temperatures. Crop Sci 2003, 43, 1874–1879.
[30]
Atwell, B.J.; Kriedemann, P.E.; Turnbull, C.G.N. Plants in Action: Adaptation in Nature, Performance in Cultivation; Palgrave MacMillan: Sydney, NSW, Australia, 1999.
[31]
Whittaker, E.T. On a new method of graduation. Proc. Edinb. Math. Soc 1923, 41, 63–75.
[32]
Eilers, P.H.C. A perfect smoother. Anal. Chem 2003, 75, 3631–3636.
[33]
Atzberger, C.; Eilers, P.H.C. Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements. Inter. J. Remote Sens 2011, 32, 3689–3709.
[34]
Atzberger, C.; Eilers, P.H.C. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit. Earth 2011, 4, 365–386.
[35]
Atkinson, P.M.; Jeganathan, C.; Dash, J.; Atzberger, C. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ 2012, 123, 400–417.
[36]
Marin, F.R.; Carvalho, G.L. de Spatio-temporal variability of sugarcane yield efficiency in the state of S？o Paulo, Brazil. Pesqui. Agropecu. Bras 2012, 47, 149–156.
[37]
Yin, X.; Kropff, M.J.; McLaren, G.; Visperas, R.M. A nonlinear model for crop development as a function of temperature. Agr. Forest Meteorol 1995, 77, 1–16.
[38]
Yan, W.; Hunt, L.A. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot 1999, 84, 607–614.
[39]
Mulianga, B.; Begue, A.; Simoes, M.; Todoroff, P. Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI. Remote Sens. 2013. under review.
[40]
Wolfe, R.E.; Roy, D.P.; Vermote, E. MODIS land data storage, gridding, and compositing methodology: Level 2 grid. IEEE Trans. Geosci. Remote Sens 1998, 36, 1324–1338.
[41]
Tan, B.; Woodcock, C.E.; Hu, J.; Zhang, P.; Ozdogan, M.; Huang, D.; Yang, W.; Knyazikhin, Y.; Myneni, R.B. The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ 2006, 105, 98–114.
[42]
Duveiller, G. Caveats in calculating crop specific pixel purity for agricultural monitoring using MODIS time series. Proc. SPIE 2012, 8531, 85310J–1.
[43]
Baret, F.; Weiss, M.; Verger, A.; Kandasamy, S. BioPar Methods Compendium LAI, FAPAR and FCOVER from VEGETATION P Product Series. Geoland 2-Towards an Operational GMES Land Monitoring Core Service. g2-BP-RP-BP038. Version 3.0. Geoland2 Consortium, 2012.
[44]
Meroni, M.; Marinho, E.; Sghaier, N.; Verstrate, M.; Leo, O. Remote sensing based yield estimation in a stochastic framework—Case study of durum wheat in Tunisia. Remote Sens 2013, 5, 539–557.
