Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?

Bégué, Agnès; Todoroff, Pierre; Pater, Johanna

doi:10.1007/s11119-008-9063-3

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…Annually linked factors may include anomalies in planting, emergence, or weather conditions. Seasonally linked factors can include plant diseases, weed development, severe climatic events or irrigation system malfunctions (Bégué et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Yield variability prediction by remote sensing sensors with different spatial resolution

Kumhálová¹,

Matějková²

2017

International Agrophysics

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Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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Section: Introductionmentioning

confidence: 99%

Yield variability prediction by remote sensing sensors with different spatial resolution

Kumhálová¹,

Matějková²

2017

International Agrophysics

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“…Studies based on in situ measurements, e.g., [13], highlight the relationship between sugarcane yield and vegetation indices adjusted by the thermal time. In a context of precision agriculture, information about the NDVI can be used to delineate areas within a field where unequal yields can be expected [14]. More recently, some work [15] based on relationships between NDVI, meteorological indicators and sugarcane yield in the Sã o Paulo region, underlined the reliability of indicators based on spectral indices to access yield at municipality scale.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring

2013

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A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of Sã o Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in Sã o Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.

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“…Spectral reflectance of the soil or crops that were measured in the laboratory (Daniel et al 2004), from hand-held devices (Read et al 2002), from aerial photography (Fleming et al 2000), and from satellite observations (Bhatti et al 1991;Salisbury and D'Aria 1992;Seelan et al 2003;Sullivan et al 2005) have been widely used in developing variable rate application maps. Multi-temporal images by satellites within a growing season have also been used to study within-field variability (Bégué et al 2008). Despite these theoretical advances and successful applications, access to and use of remote sensing data by end users require considerable technical knowledge about computing and remote sensing, and remain as a challenge (Moreenthaler et al 2003;Zhang et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Zone mapping application for precision-farming: a decision support tool for variable rate application

et al. 2009

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A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.

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Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?

Cited by 25 publications

References 13 publications

Yield variability prediction by remote sensing sensors with different spatial resolution

Yield variability prediction by remote sensing sensors with different spatial resolution

Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring

Zone mapping application for precision-farming: a decision support tool for variable rate application

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