Large area operational wheat yield model development and validation based on spectral and meteorological data

Manjunath, K. R.; Potdar, M. B.; Purohit, N. L.

doi:10.1080/01431160110104692

Cited by 46 publications

(19 citation statements)

References 9 publications

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“…Such hybrid models show higher correlation and predictive capability than the models using remote sensing indicators only [66,67] as the input variables complement each other. The bio-climatic variables introduce information about solar radiation, temperature, air humidity and soil water availability while the spectral component introduces information about crop management, varieties and stresses not taken into consideration by the agro-meteorological models [57].…”

Section: Concomitant Use Of Remotely Sensed Indicators Together With mentioning

confidence: 99%

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

et al. 2013

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Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the OPEN ACCESS Remote Sens. 2013, 5 1705 availability of long time series as needed by the majority of the yield prediction methods used today.

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Section: Concomitant Use Of Remotely Sensed Indicators Together With mentioning

confidence: 99%

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

et al. 2013

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“…To date, the most accurate yield estimates from remotely sensed data have been reported in research that used models developed using regression analysis techniques and extensive multitemporal data sets (Das et al 1993, Wang et al 2001, Manjunath and Potdar 2002.…”

Section: Yield Forecasting Measuresmentioning

confidence: 99%

The early explanatory power of NDVI in crop yield modelling

Wall¹,

Larocque

Léger

2008

International Journal of Remote Sensing

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“…Past predictions of yield which incorporated a variety of remote sensing techniques have ranged from simple statistical relationships [11,12,[15][16][17][18]; more advanced relationships built on agronomic and metrological data [4,19,20], to models utilising absorbed photosynthetically active radiation [21][22][23][24]. The majority of these studies have employed low resolution sensors because of their low cost, availability, extensive spatial coverage and frequent acquisition dates.…”

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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Large area operational wheat yield model development and validation based on spectral and meteorological data

Cited by 46 publications

References 9 publications

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

The early explanatory power of NDVI in crop yield modelling

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

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