Crop yield estimation by satellite remote sensing

Ferencz, Csaba; Bognár, Péter; Lichtenberger, János; Hamar, D.; Tarcsai, Gy.; Timár, Gábor; Molnár, Gábor; Pásztor, Sz; Steinbach, Péter; Székely, Balázs; Ferencz, Orsolya E.; Ferencz-Árkos, I.

doi:10.1080/01431160410001698870

Cited by 143 publications

(96 citation statements)

References 37 publications

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“…As has been well documented, for many empirical remote sensing based yield models it is assumed that the canopy vigour of winter wheat estimated by spectral measurements (e.g., NDVI) is directly related to final winter wheat yields (Ferencz et al, 2004). The fact that the maximum GAI value was listed as an explanatory variable for all three best models in this study also follows this assumption.…”

Section: Discussionsupporting

confidence: 50%

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Kouadio

Duveiller

Djaby

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tEarth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha −1 , representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

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Section: Discussionsupporting

confidence: 50%

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Kouadio

Duveiller

Djaby

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

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“…Reasonable relationships have been observed between yields collected at the farm, field or geo-referenced hand-sampled scale and spectral vegetation indices [14,18,19,22,25]. However, spatial mismatches occur with the matching of high resolution spectral indices with broad scale farm and field estimates, as well with yield estimation at the plant level which is also labour intensive, expensive to collect [26] and usually limited to a small spatial extent.…”

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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“…In recent years much attention has been paid on monitoring phenological changes and classification of different crop types using remote sensing observations (Bouvet and Le Toan, 2009;Hoekman, 2003;Liu et al, 2013;Juan M. Lopez-Sanchez et al, 2011). Issues like recognizing growth behaviour, cultivation problems and crop yield estimation have been worked by different researchers (Dhar et al, 2009;Ferencz et al, 2004;Silleos et al, 2002). Among different sensor data, radar remote sensing with its reliable and frequent imaging capability all-weather functionality, sensitivity to target geometrical structure and orientation, has enhanced further our capabilities for agricultural monitoring.…”

Section: Introductionmentioning

confidence: 99%

Phenological tracking og agricultural feilds investigated by using dual polarimetry tanDEM-X images

Mirzaee

Motagh

Arefi

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remote sensing plays a key role in monitoring and assessing environmental changes. Because of its special imaging characteristics such as high-resolution, capabilities to obtain data in all weather conditions and sensitivity to geometrical and dielectric properties of the features, Synthetic Aperture Radar (SAR) technology has become a powerful technique to detect small scale changes related to earth surface.SAR images contain the information of both phase and intensity in different modes like single, dual and full polarimetric states which are important in order to extract information about various targets. In this study we investigate phenological changes in an agricultural region using high-resolution X-band SAR data. The case study is located in Doroud region of Lorestan province, west of Iran. The purpose is to investigate the ability of copolar and interferometric coherence extracted from TanDEM-X dual polarimetry (HH/VV) in bistatic StripMap mode for tracking the phenological changes of crops during growing season. The data include 11 images acquired between 12. 06.2012 and 02.11.2012 and 6 images acquired between 30.05.2013 and 04.08.2013 in the CoSSC format. Results show that copolar coherence is almost able to follow phenological changes but interferometric coherence has a near constant behaviour with fluctuations mainly related to baseline variations.

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Crop yield estimation by satellite remote sensing

Cited by 143 publications

References 37 publications

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

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

Phenological tracking og agricultural feilds investigated by using dual polarimetry tanDEM-X images

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