Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale
The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making.
References
[1]
Hutchinson, C.F. Uses of satellite data for famine early warning in sub-Saharan Africa. Int. J. Remote Sens 1991, 12, 1405–1421.
[2]
Quarmby, N.A.; Milnes, M.; Hindle, T.L.; Silleos, N. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens 1993, 14, 199–210.
[3]
Liu, W.T.; Kogan, F.N. Monitoring regional drought using the vegetation condition index. Int. J. Remote Sens 1996, 17, 2761–2782.
[4]
Prasad, A.K.; Singh, R.P.; Tare, V.; Kafatos, M. Use of vegetation index and meteorological parameters for the prediction of crop yield in India. Int. J. Remote Sens 2007, 28, 5207–5235.
[5]
Moulin, S.; Bondeau, A.; Delecolle, R. Combining agricultural crop models and satellite observations: From field to regional scales. Int. J. Remote Sens 1998, 19, 1021–1036.
Flowers, M.; Weisz, R.; Heiniger, R.; Tarleton, B.; Meijer, A. Field validation of a remote sensing technique for early nitrogen application decisions in wheat. Agron. J 2003, 95, 167–176.
[10]
Reyniers, M.; Vrindts, E. Measuring wheat nitrogen status from space and ground-based platform. Int. J. Remote Sens 2006, 27, 549–567.
[11]
Vicente-Serrano, S.M.; Cuadrat-Prats, J.M.; Romo, A. Early prediction of crop production using drought indices at different time-scales and remote sensing data: Application in the Ebro Valley (north-east Spain). Int. J. Remote Sens 2006, 27, 511–518.
[12]
Wall, L.; Larocque, D.; Leger, P.M. The early explanatory power of NDVI in crop yield modelling. Int. J. Remote Sens 2008, 29, 2211–2225.
[13]
Lobell, D.B.; Asner, G.P.; Ortiz-Monasterio, J.I.; Benning, T.L. Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties. Agr. Ecosyst. Environ 2003, 94, 205–220.
[14]
Liu, L.; Wang, J.; Bao, Y.; Huang, W.; Ma, Z.; Zhao, C. Predicting winter wheat condition, grain yield and protein content using multi-temporal EnviSat-ASAR and Landsat TM satellite images. Int. J. Remote Sens 2006, 27, 737–753.
[15]
Benedetti, R.; Rossini, P. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ 1993, 45, 311–326.
[16]
Sharma, T.; Sudha, K.S.; Ravi, N.; Navalgund, R.R.; Tomar, K.P.; Chakravarty, N.V.K.; Das, D.K. Procedures for wheat yield prediction using Landsat MSS and IRS-1A data. Int. J. Remote Sens 1993, 14, 2509–2518.
[17]
Sridhar, V.N.; Dadhwal, V.K.; Chaudhari, K.N.; Sharma, R.; Bairagi, G.D.; Sharma, A.K. Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh, India. Int. J. Remote Sens 1994, 15, 1307–1316.
[18]
Hamar, D.; Ferencz, C.; Lichtenberger, J.; Tarcsai, G.; Ferencz-Arkos, I. Yield estimation for corn and wheat in the Hungarian Great Plain using Landsat MSS data. Int. J. Remote Sens 1996, 17, 1689–1699.
[19]
Rudorff, B.F.T.; Batista, G.T. Wheat yield estimation at the farm level using TM Landsat and agro-meteorological data. Int. J. Remote Sens 1991, 12, 2477–2484.
[20]
Manjunath, K.R.; Potdar, M.B.; Purohit, N.L. Large area operational wheat yield model development and validation based on spectral and meteorological data. Int. J. Remote Sens 2002, 23, 3023–3038.
[21]
Bastiaanssen, W.G.M.; Ali, S. A new crop yield forecasting model on satellite measurements applied across Indus Basin, Pakistan. Agr. Ecosyst. Environ 2003, 94, 321–340.
[22]
Lobell, D.B.; Asner, G.P. Comparison of Earth Observing-1 ALI and Landsat ETM+ for crop identification and yield prediction in Mexico. IEEE Trans. Geosci. Remote Sens 2003, 41, 1277–1282.
[23]
Patel, N.R.; Bhattacharjee, B.; Mohammed, A.J.; Tanupriya, B.; Saha, S.K. Remote sensing of regional yield assessment of wheat in Haryana, India. Int. J. Remote Sens 2006, 27, 4071–4090.
[24]
Duchemin, B.; Maisongrande, P.; Boulet, G.; Benhadj, I. A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index. Environ. Model. Softw 2008, 23, 876–892.
[25]
Ferencz, C.; Bognar, P.; Lichtenberger, J.; Hamar, D.; Tarcsai, G.; Timar, G.; Molnar, G.; Pasztor, S.Z.; Steinbach, P.; Szekely, B.; et al. Crop yield estimation by satellite remote sensing. Int. J. Remote Sens 2004, 25, 4113–4149.
[26]
Doraiswamy, P.C.; Hatfield, J.L.; Jackson, T.J.; Akhmedov, B.; Prueger, J.; Stern, A. Crop condition and yield simulations using Landsat and MODIS. Remote Sens. Environ 2004, 92, 548–559.
[27]
Reitz, P.; Kutzbach, H.D. Investigations on a particular yield mapping system for combine harvesters. Comput. Electron. Agr 1996, 14, 137–150.
[28]
Arslan, S.; Colvin, T.S. An evaluation of the response of yield monitors and combines to varying yields. Precis. Agr 2002, 3, 107–122.
[29]
Lark, R.M.; Stafford, J.V.; Bolam, H.C. Limitations on the spatial resolution of yield mapping for combinable crops. J. Agr. Eng. Res 1997, 66, 183–193.
[30]
Thenkabail, P.S. Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images. Int. J. Remote Sens 2003, 24, 2879–2904.
[31]
Dobermann, A.; Ping, J.L. Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agron. J 2004, 96, 285–297.
[32]
Enclona, E.A.; Thenkabail, P.S.; Celis, D.; Diekmann, J. Within-field wheat yield prediction from IKONOS data: A new matrix approach. Int. J. Remote Sens 2004, 25, 377–388.
[33]
Reeves, M.C.; Zhao, M.; Running, S.W. Usefulness and limits of MODIS GPP for estimating wheat yield. Int. J. Remote Sens 2005, 26, 1403–1421.
[34]
Bullock, D.S.; Bullock, D.G. From agronomic research to farm management guidelines: A primer on the economics of information and precision technology. Precis. Agr 2000, 2, 71–101.
[35]
Robertson, M.J.; Lyle, G.; Bowden, J.W. Within-field variability of wheat yield and economic implications for spatially variable nutrient management. Field Crop. Res 2008, 105, 211–220.
[36]
Massey, R.E.; Myers, D.B.; Kitchen, N.R.; Sudduth, K.A. Profitability maps as an input for site-specific management decision making. Agron. J 2008, 100, 52–59.
[37]
Lyle, G.; Ostendorf, B. A high resolution broad scale spatial indicator of grain growing profitability for natural resource planning. Ecol. Indic 2011, 11, 209–218.
[38]
Ostendorf, B. Overview: Spatial information and indicators for sustainable management of natural resources. Ecol. Indic 2011, 11, 97–102.
[39]
Turner, N.C.; Asseng, S. Productivity, sustainability, and rainfall use efficiency in Australian rain-fed Mediterranean agricultural systems. Aust. J. Agr. Res 2005, 56, 1123–1136.
[40]
Sadras, V.O.; Rodriguez, D. The limit to wheat water-use efficiency in eastern Australia. II. Influences of rainfall patterns. Aust. J. Agr. Res 2007, 58, 657–669.
[41]
Passioura, J. Increasing crop productivity when water is scarce—From breeding to field management. Agr. Water Manag 2006, 80, 176–196.
[42]
Anderson, W.K.; Garlinge, J.R. The Wheat Book—Principles and Practice; Department of Agriculture and Food: South Perth, WA, Australia, 2000; p. 322.
[43]
Alexandridis, T.K.; Gitas, I.Z.; Silleos, N.G. An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data. Int. J. Remote Sens 2008, 29, 3589–3607.
[44]
Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62, 241–252.
[45]
Aparicio, N.; Villegas, D.; Araus, J.L.; Casadesus, L.; Royo, C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J 2000, 92, 83–91.
[46]
Aase, J.K.; Siddoway, F.H. Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sens. Environ 1981, 11, 267–277.
[47]
Aparicio, N.; Villegas, D.; Araus, J.L.; Casadesus, J.; Royo, C. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci 2002, 42, 1547–1555.
[48]
Scotford, I.M.; Miller, P.C.H. Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosys. Eng 2004, 87, 27–38.
[49]
Smith, R.C.G.; Adams, J.; Stephens, D.J.; Hick, P.T. Forecasting wheat yield in a Mediterranean-type environment from the NOAA Satellite. Aus. J. Agr. Res 1995, 46, 113–125.
[50]
Beeri, O.; Peled, A. Spectral indices for precise agriculture monitoring. Int. J. Remote Sens 2006, 27, 2039–2047.
[51]
Boissard, P.; Pointel, J.G. Reflectance, green leaf area index and ear hydric status of wheat from anthesis until maturity. Int. J. Remote Sens 1993, 14, 2713–2729.
[52]
Dawbin, K.W.; Evans, J.C.; Duggin, M.J.; Leggett, E.K. Classification of wheat areas and prediction of yields in north-western New South Wales by repetitive Landsat data. Aus. J. Agr. Res 1980, 31, 449–53.
[53]
Minasny, B.; McBratney, A.B.; Whelan, B.M. VESPER Version1.62—Variogram Estimation and Spatial Prediction plus Error; Australian Centre for Precision Agriculture: Sydney, NSW, Australia, 2005.
Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models. Part 1-A discussion of principles. J. Hydrol 1970, 10, 282–290.
[56]
Krause, P.; Boyle, D.P.; B？se, F. Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci 2005, 5, 89–97.
[57]
Ludwig, F.; Asseng, S. Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agr. Syst 2006, 90, 159–179.
[58]
Luo, Q.Y.; Bellotti, W.; Williams, M.; Bryan, B. Potential impact of climate change on wheat yield in South Australia. Agr. Forest Meteorol 2005, 132, 273–285.
[59]
Balaghi, R.; Tychon, B.; Eerens, H.; Jlibene, M. Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. Int. J. Appl. Earth Obs 2008, 10, 438–452.
[60]
Reyniers, M.; Vrindts, E. Measuring wheat nitrogen status from space and ground-based platform. Int. J. Remote Sens 2006, 27, 549–567.
[61]
Ren, J.; Chen, Z.; Zhou, Q.; Tang, H. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int. J. Appl. Earth Obs 2008, 10, 403–413.
[62]
Reyniers, M.; Vrindts, E.; De Baerdemaeker, J. Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat. Eur. J. Agron 2006, 24, 87–94.
[63]
Reyniers, M.; Vrindts, E. Measuring wheat nitrogen status from space and ground-based platform. Int. J. Remote Sens 2006, 27, 549–567.
[64]
McMaster, G.S.; Wilhelm, W.W.; Frank, A.B. Developmental sequences for simulating crop phenology for water-limiting conditions. Aus. J. Agr. Res 2005, 56, 1277–1288.
[65]
Gomez-Macpherson, H.; Richards, R.A. Effect of sowing time on yield and agronomic characteristics of wheat in south-eastern Australia. Aus. J. Agr. Res 1995, 46, 1381–1399.
