Measuring spatial variability of crops and soils at sub-paddock scale using remote sensing technologies

Abuzar, M.; Rampant, P.; Fisher, P.

doi:10.1109/igarss.2004.1370642

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…This work demonstrated the utility of relationships between EC a and crop yield to answer resource input questions. Rampant and Abuzar [ 286 ] predicted yield zones from a combination of geophysical (i.e., EM38, EM31, airborne gamma radiometrics) and terrain attributes with a decision tree classifier. Individually, the geophysical data were relatively poor predictors of the yield zones.…”

Section: Applications In Agriculturementioning

confidence: 99%

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

Heil

Schmidhalter

2017

Sensors

102

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Fast and accurate assessment of within-field variation is essential for detecting field-wide heterogeneity and contributing to improvements in the management of agricultural lands. The goal of this paper is to provide an overview of field scale characterization by electromagnetic induction, firstly with a focus on the applications of EM38 to salinity, soil texture, water content and soil water turnover, soil types and boundaries, nutrients and N-turnover and soil sampling designs. Furthermore, results concerning special applications in agriculture, horticulture and archaeology are included. In addition to these investigations, this survey also presents a wide range of practical methods for use. Secondly, the effectiveness of conductivity readings for a specific target in a specific locality is determined by the intensity at which soil factors influence these values in relationship to the desired information. The interpretation and utility of apparent electrical conductivity (ECa) readings are highly location- and soil-specific, so soil properties influencing the measurement of ECa must be clearly understood. From the various calibration results, it appears that regression constants for the relationships between ECa, electrical conductivity of aqueous soil extracts (ECe), texture, yield, etc., are not necessarily transferable from one region to another. The modelling of ECa, soil properties, climate and yield are important for identifying the location to which specific utilizations of ECa technology (e.g., ECa−texture relationships) can be appropriately applied. In general, the determination of absolute levels of ECa is frequently not possible, but it appears to be quite a robust method to detect relative differences, both spatially and temporally. Often, the use of ECa is restricted to its application as a covariate or the use of the readings in a relative sense rather than as absolute terms.

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Section: Applications In Agriculturementioning

confidence: 99%

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

Heil

Schmidhalter

2017

Sensors

102

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“…Our analysis demonstrates the potential of phenology to assess crop growth variability and to provide a comprehensive understanding of the joint role of soil, climate, and land use on crop seasonality. Single vegetation index images have been successfully utilized to recognize crop variability across a region [43,44]. However, the single image approach has been criticized for lacking information on intra-seasonal growth dynamics [45].…”

Section: Discussionmentioning

confidence: 99%

Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use

Bajocco

Vanino

Bascietto

et al. 2021

Land

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The exploration of crop seasonality across a region offers a way to help understand the phenological spatial patterns of complex landscapes, like agricultural ones. Knowing the role of environmental factors in influencing crop phenology patterns and processes is a key aspect for understanding the impact of climate and land use changes on agricultural landscape dynamics. We identified pixels with similar phenological behavior (i.e., pheno-clusters) and compared them to the land cover map of the study area to assess the role of the land management component in controlling the phenological patterns identified. Results demonstrated that soil texture is the most important factor for permanent crops, while large amount of rainfall and high values of available water content are the main drivers in spring cultivations (i.e., irrigated crops). Scarce water availability (in terms of soil texture, low annual precipitation and high minimum temperature) represented the main driving factor for non-irrigated crops, whose phenology is characterized by summer drought and fall-winter productivity. Compared to vegetation maps that use only land cover from a single season or period, using seasonality of the NDVI time series to classify the agricultural landscape provides different and more ecologically relevant information about croplands.

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“…The historical grain yields (from harvester) and biomass (from satellite imagery based on the normalized difference vegetation index, NDVI) during 1996-2002 were collected to define zones of yield variability and thus the yield-based management zones within the paddock were established (Abuzar et al, 2004;Fisher et al, 2009). The paddock area was then assigned into three yield classes (low, medium, and high) and each classified into two seasonal variability zones (variable and stable).…”

Section: Field Experimentsmentioning

confidence: 99%

Modification of a spatially referenced crop model to simulate the effect of spatial pattern of subsoil salinity

Dixit¹,

Chen²

2010

Computers and Electronics in Agriculture

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High levels of subsoil salinity limit the growth and yield of dryland cereals in the Victorian southern Mallee, Australia. Currently available crop simulation models of wheat production perform poorly in this region, presumably due to their inability to account for subsoil limitations, mainly salinity. The objective of this work was to modify a spatially referenced Water and Nitrogen Management Model (WNMM) to account for the spatial pattern of subsoil salinity, by adjusting crop water uptake, in order to explain the spatial variation in wheat yield in this area. Measurements of above-ground biomass and yield of wheat, and the profile of soil salinity (0-80 cm) were made at 40 locations across an 88 ha paddock (35.78 • S, 142.98 • E) in the Victorian southern Mallee. The S-shaped water stress response function for crop water uptake proposed by van Genuchten (1987) was explored to modify the WNMM by adjusting the water uptake due to salinity, which significantly improved yield simulation over the original WNMM. The improvement in the model's ability to simulate wheat yield indicates that the subsoil salinity limits crop performance in the area. The incorporation of a salinity function in spatial crop models offers potential for simulating yield across a landscape and thus practicing precision agriculture provided salinity impact is considered dynamically.

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Measuring spatial variability of crops and soils at sub-paddock scale using remote sensing technologies

Cited by 4 publications

References 2 publications

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use

Modification of a spatially referenced crop model to simulate the effect of spatial pattern of subsoil salinity

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