Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data

Silva, Wagner F.; Rudorff, Bernardo Friedrich Theodor; Formaggio, Antônio Roberto; Paradellá, Waldir Renato; Mura, José Cláudio

doi:10.1016/j.isprsjprs.2008.07.005

Cited by 30 publications

(27 citation statements)

References 17 publications

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“…Compared to other crop classes, pasture had the lowest mean backscattering and the lowest standard deviation in the HH polarization (Figure 4), which influenced the formation of the groups and the discrimination of classes. Silva et al (2009) also found that pasture was best distinguished with polarization HH which also provided the best Kappa. McNairn et al (2009) utilized PALSAR L-band images acquired on three different dates throughout the growing season in order to distinguish hay-pasture, soybean, corn and cereals.…”

Section: Discussionmentioning

confidence: 87%

“…Although Silva et al (2009) found the best Kappa index for the HH-VV combination, when they consider combination of two polarizations, the best distinction for the Coffee-|| class was obtained for the VV-HV and HH-VV combinations and for the Coffee-⊥ class the best combination was HH-VV. To distinguish crop classes using supervised classification, Silva et al (2009) considered two cotton classes (different development stages).…”

Section: Discussionmentioning

confidence: 95%

“…In SAR backscatter, various factors such as frequency, incidence angle, planting row spacing and direction, soil and crop water content, and crop phenology, among others, jointly influence backscatter and, therefore, must be taken into consideration in the analyses (Brown et al, 1992;Baghdadi et al, 2009;Silva et al, 2009). In this sense, polarization is a relevant parameter influencing the interaction of microwave radiation with vegetation and for this reason it is an important source of information to distinguish among agricultural crops.…”

Section: Discussionmentioning

confidence: 99%

“…The previous study made by Silva et al (2009) exploited the fully polarimetric data from R99B airborne system for classification of agricultural crops. In this first study three attributes of the microwave signal were used (amplitude, polarization and phase).…”

Section: Study Area and Agricultural Cropsmentioning

confidence: 99%

“…Various researchers have demonstrated the potential of SAR images for agricultural applications, with the best results obtained using multipolarized images (Formaggio et al, 2001;McNairn and Brisco, 2004;Stankiewicz, 2006;Silva et al, 2009;Ulaby et al, 1980). However, there is a lack of studies on the use of SAR considering the management characteristics and crops typically cultivated under Brazilian conditions.…”

Section: Introductionmentioning

confidence: 99%

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Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California

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Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude-Pottier and Freeman-Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.

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Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data

Cited by 30 publications

References 17 publications

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Simulated multipolarized MAPSAR images to distinguish agricultural crops

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California

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