Within-field wheat yield prediction from IKONOS data: a new matrix approach

Enclona, Eden A; Thenkabail, Prasad S.; Celis, D.; Diekmann, J.

doi:10.1080/0143116031000102485

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…Up to two decimal places, the estimates in table 2 are identical to those reported by Enclona et al (2004). The confidence limits are very wide, and the intervals even contain zero in all cases.…”

Section: Examplesupporting

confidence: 53%

“…In the example given by Enclona et al (2004) (see table 1), multicollinearity is very severe and sample size is very small, causing the parameter estimates of expected class yields to be virtually worthless. Standard errors and 95% confidence intervals obtained by a linear model package are reported in table 2.…”

Section: Examplementioning

confidence: 98%

“…If errors e are assumed to be random variables, Table 1. Yield data and relative areas of four classes (Enclona et al 2004). …”

Section: Commentsmentioning

confidence: 99%

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Comment on the article on ‘Within‐field wheat yield prediction from IKONOS data, a new matrix approach’ by E. A. Enclona, P. S. Thenkabail, D. Celis and J. Diekmann, International Journal of Remote Sensing, 25, 377–388 (2004)

Piepho

2007

International Journal of Remote Sensing

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Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tres20 Comment on the article on 'Within-field wheat yield prediction from IKONOS data, a new matrix approach' by E. A. Enclona, P. S.This article comments on a method of within-field yield prediction from IKONOS data published in this Journal (International Journal of Remote Sensing, 25, 377-388). The authors propose what they call a new matrix approach, which solves a system of linear equations relating actual yield to expected yield in four classes defined based on spectral characteristics and the relative areas for these classes as measured by remote sensing. The authors use five observations to estimate four unknown parameters, and they propose that generally p + 1 observations could be used to estimate p parameters. This article shows that this approach will lead to very unreliable predictions of class yield. The problem is shown to be a standard multiple regression problem. It is argued that sample size must be considerably larger than the number of classes in order to obtain reliable yield estimates for the classes. Due to the very nature of the regressor variables, the relative areas of the classes, multicollinearity is expected to be a severe problem. Therefore, it may be useful to reduce the number of regressor variables using standard model selection procedures.

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Section: Examplesupporting

confidence: 53%

Section: Examplementioning

confidence: 98%

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Comment on the article on ‘Within‐field wheat yield prediction from IKONOS data, a new matrix approach’ by E. A. Enclona, P. S. Thenkabail, D. Celis and J. Diekmann, International Journal of Remote Sensing, 25, 377–388 (2004)

Piepho

2007

International Journal of Remote Sensing

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“…Yield and spatial position are collected via a combine harvester every one to three seconds, allowing maps of yield to be produced at a high spatial resolution that is similar to the resolution of the spectral indices produced by the Landsat sensor. Several studies have reported a strong relationship between yield and Landsat and IKONOS imagery [10,[30][31][32] but most studies are from individual fields and rarely have these relationships been used to predict yields elsewhere.…”

Section: Introductionmentioning

confidence: 99%

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

2013

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Abstract: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.

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“…Spectral vegetation indices (SVIs) derived from satellite imagery collected from various platforms have been used to predict crop yields across a range of spatial scales. The platforms have included Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) [23][24][25], Landsat Thematic Mapper (TM) [23,26,27], Satellite Pour l'observation de la Terre (SPOT) [28,29], IKONOS [30,31], WorldView 2, and RapidEye imagery. For an exhaustive list of satellites and other remote sensing platforms, refer to Konecny [32].…”

Section: Introductionmentioning

confidence: 99%

Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed

2015

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Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg· ha −1 showed reduction of tile NO3-N and sediment

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Within-field wheat yield prediction from IKONOS data: a new matrix approach

Cited by 18 publications

References 9 publications

Comment on the article on ‘Within‐field wheat yield prediction from IKONOS data, a new matrix approach’ by E. A. Enclona, P. S. Thenkabail, D. Celis and J. Diekmann, International Journal of Remote Sensing, 25, 377–388 (2004)

Comment on the article on ‘Within‐field wheat yield prediction from IKONOS data, a new matrix approach’ by E. A. Enclona, P. S. Thenkabail, D. Celis and J. Diekmann, International Journal of Remote Sensing, 25, 377–388 (2004)

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed

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