Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey, Turkey

Türker, Mustafa; Arikan, M. A. Sahir

doi:10.1080/01431160500166391

Cited by 70 publications

(37 citation statements)

References 23 publications

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“…In West Africa, this situation is further aggravated by a heterogeneous landscape [12]. Recent studies have overcome this challenge by overlaying per-pixel classification results on parcel/field boundaries and assigning the modal class within each field as its class [5,23]. This approach has been found to improve classification accuracies [32,37].…”

Section: Methodological Approachmentioning

confidence: 99%

“…Periodic acquisition of RS data enables analysis to be conducted at regular intervals, which aids in identifying changes. Optical systems, which have largely been relied upon for agricultural land use mapping [5,6], measure reflectance from objects in the visible and infrared portions of the electromagnetic spectrum. The amount of reflectance is a function of the bio-physical characteristics of the reflecting feature (e.g., canopy moisture, leaf area and level of greenness of vegetation).…”

Section: Introductionmentioning

confidence: 99%

“…Separation between crop and non-crop segments was achieved by overlaying the segmentation results with the per-pixel crop mask derived in Step 1 (Section 4.1) and assigning the modal class in each segment to it [5,37,38]. For the crop segments, the percentage of crop pixels in each segment was extracted.…”

Section: Derivation Of Field Boundariesmentioning

confidence: 99%

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Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

et al. 2014

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Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%-15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.

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Section: Methodological Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

et al. 2014

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“…The methods exploit both the absolute greenness as well as the greenness dynamics, or land surface phenology, of the disparate crop types (Chang et al 2007;Shao et al 2010;Turker and Arikan 2005;Wardlow, Egbert, and Kastens 2007;Zhong, Gong, and Biging 2014). Data from the MODIS is well suited for mapping crops worldwide because of its daily temporal and moderate spatial resolution (250 m in visible and NIR bands); MODIS data have been used to map crop types across different parts of the world (Teluguntla et al 2017;Vintrou et al 2012;Wardlow and Egbert 2008).…”

Section: Introductionmentioning

confidence: 99%

“…There are also studies that combine MODIS data and moderate spatial resolution data, such as Landsat and the Indian Remote Sensing Advanced Wide Field Sensor (AWiFS), to discriminate crop types (Thenkabail and Wu 2012;USDA-NASS 2013). Other studies used higher spatial resolution imagery, such as Landsat and ASTER data, to differentiate crop types for a less extensive area (Peña-Barragan et al 2011;Serra and Pons 2008;Turker and Arikan 2005). Image selection for crop type mapping largely depends on the size of the study area, image availability, cost, and the level of diversity in crop types and management.…”

Section: Introductionmentioning

confidence: 99%

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Wallace

Thenkabail

Rodriguez

et al. 2017

GIScience & Remote Sensing

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An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user's and producer's accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America's Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

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Crop Classification in the Mixed Cropping Environment Using SAR Data and Machine Learning Algorithms

Goyal

Kesarwani²,

Neeti³

et al. 2022

Water Science and Technology Library

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Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey, Turkey

Cited by 70 publications

References 23 publications

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Crop Classification in the Mixed Cropping Environment Using SAR Data and Machine Learning Algorithms

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