Early prediction of crop production using drought indices at different time‐scales and remote sensing data: application in the Ebro Valley (north‐east Spain)

Vicente‐Serrano, Sergio M.; Prats, José María Cuadrat; Romo, A.

doi:10.1080/01431160500296032

Cited by 96 publications

(70 citation statements)

References 29 publications

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“…This wide time period encapsulates critical phenological phases for predicting the final wheat yield, including the formation and organisation of the canopy structure after the emergence of the flag leaf [8,50], at the end of stem elongation and beginning of heading [47] and grain formation [51]. Other studies [11,14] and those undertaken in Australia [37,49,52] suggest that for cereal crops, biomass or LAI measures at the flowering or anthesis stage (September to November) are closely related to final yield. These studies show that wheat yield has a positive relationship with the biomass produced by the plant, i.e., low biomass leads to low yield, high biomass high yield.…”

Section: Wheat Phenology and Image Acquisition Datementioning

confidence: 99%

“…The collection of reliable and timely information of crop performance can also influence pricing policies, marketing and trading decisions [13,14] and is important for government, growers, insurance and agricultural companies [11,12] so that logistical issues can be anticipated [12].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Wheat Phenology and Image Acquisition Datementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…At the same time, remote-sensing technology has been developing steadily and its products can provide many applications in drought monitoring (Vicente-Serrano et al, 2006;Gouveia et al, 2009), agriculture, crop growth monitoring, yield modelling (Gouveia and Trigo, 2008) and crop identification. In particular, the time-series of satellite imagery efficiently provide a synoptic view of vegetation dynamics, namely the chestnut vegetative cycle that may be used for chestnut management.…”

Section: Introductionmentioning

confidence: 99%

Assessment of weather-related risk on chestnut productivity

Pereira

Caramelo

Gouveia

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. Due to its economic and nutritional value, the world production of chestnuts is increasing as new stands are being planted in various regions of the world. This work focuses on the relation between weather and annual chestnut production to model the role of weather, to assess the impacts of climate change and to identify appropriate locations for new groves. The exploratory analysis of chestnut production time series and the striking increase of production area have motivated the use for chestnut productivity. A large set of meteorological variables and remote sensing indices were computed and their role on chestnut productivity evaluated with composite and correlation analyses. These results allow for the identification of the variables cluster with a high correlation and impact on chestnut production. Then, different selection methods were used to develop multiple regression models able to explain a considerable fraction of productivity variance: (i) a simulation model (R 2 -value = 87 %) based on the winter and summer temperature and on spring and summer precipitation variables; and, (ii) a model to predict yearly chestnut productivity (R 2 -value of 63 %) with five months in advance, combining meteorological variables and NDVI. Goodness of fit statistic, cross validation and residual analysis demonstrate the model's quality, usefulness and consistency of obtained results.

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“…A number of studies have demonstrated variation in response of the SPI to soil moisture, river discharge, reservoir storage, vegetation activity, crop production and piezometric fluctuations at different time scales (e.g. Szalai et al, 2000;Sims et al, 2002;Ji and Peters, 2003;Vicente-Serrano and López-Moreno, 2005;Vicente-Serrano et al, 2006;Patel et al, 2007;Vicente-Serrano, 2007;Khan et al, 2008).…”

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confidence: 99%

A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index

2010

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Abstract. We propose a new climatic drought index: the Standardized PrecipitationEvapotranspiration Index (SPEI). The SPEI is based on precipitation and temperature data, and has the advantage of combining a multi-scalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed, and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a Log-logistic probability distribution.Mathematically, the SPEI is similar to the Standardized Precipitation Index (SPI), but includes the role of temperature. As the SPEI is based on a water balance, it can be compared to the self-calibrated Palmer Drought Severity Index (sc-PDSI). We compared time series of the three indices for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions only the sc-PDSI and SPEI identified an increase in drought severity associated with higher water demand due to evapotranspiration. Relative to the sc-PDSI, the SPEI has the advantage of being multi-scalar, which is crucial for drought analysis and monitoring.

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Early prediction of crop production using drought indices at different time‐scales and remote sensing data: application in the Ebro Valley (north‐east Spain)

Cited by 96 publications

References 29 publications

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

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

Assessment of weather-related risk on chestnut productivity

A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index

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