Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements

Navarro, Ana; Rolim, João; Miguel, Irina; Catalão, João; Silva, Joel; Paìnho, Marco; Vekerdy, Z.

doi:10.3390/rs8060525

Cited by 74 publications

(53 citation statements)

References 27 publications

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“…Sentinel-1A was launched on 3 April 2014, while Sentinel-1B was launched on 25 April 2016 [3]. Sentinel-1A provides C-band images in both singular and dual polarization with a 12-day repeat cycle [36], and has four imaging modes: strip map (SM), interferometric wide swath (IW), extra wide swath (EW), and wave (WV), each with different resolutions.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

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Section: Study Area and Datasetsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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“…Furthermore, introducing hyperspectral data is possible to improve the accuracy of LST computation, while the use of microwave data during the days affected by weather could make an LST estimation feasible. An integrated use of multi-source, multi-scale remote sensing data to expand temporal scale of ET r F based on the characteristics of study area, is a breakthrough for future research [77][78][79]. On such basis, this study provides the framework for future analysis elsewhere.…”

Section: Resultsmentioning

confidence: 99%

Mapping Evapotranspiration Coefficients in a Temperate Maritime Climate Using the METRIC Model and Landsat TM

Spiliotopoulos

Holden

Λουκάς

2017

Water

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Abstract:The applicability of a land surface temperature (LST)-evapotranspiration (ET) regression model to estimate ET fraction (ETrF) was tested in the temperate maritime climate of Central Ireland. In this study, the Mapping ET at high Resolution and with Internalized Calibration (METRIC) model was applied to calculate evapotranspiration from a mixed land cover area in Central Ireland. The ETrF values estimated on a pixel-by-pixel basis using two different surface roughness maps derived from two different estimates of Leaf Area Index (LAI) showed no significant differences. The METRIC based ETrF values from a Landsat Enhanced Thematic Mapper Plus (ETM+) image from 12 July 2013 were in similar range with those obtained from a regression model using land surface temperature (LST)-ET relationship derived from a Landsat 5 Thematic Mapper (TM) image of 17 July 2006. Results indicated that ETrF values could be estimated by using LST values from Landsat TM, even when stress conditions were prevailing in the region.

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“…According to [15], sigma naught of ALOS data or commonly known as backscattering can be obtained by radiometric calibrating using Equation (1). Meanwhile, backscattering value of Sentinel-1A is converting from the gamma-calibrated backscattering coefficient as shown in Equation (2) [16].…”

Section: Sar Image Pre-processingmentioning

confidence: 99%

Application of SAR data for oil palm tree discrimination

Kee

Shariff

Sood

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Oil palm tree discrimination is an important step for tree counting. In order for proper planning and management of the plantation, it is important to identify and classify oil palm tree species distinctively from other tree species and weeds. As oil palm trees are green leafy plants, it is difficult to differentiate between oil palm trees and weeds through color classification alone. Tree height can be determined through proper photogrammetric method. However, it is time consuming. SAR data processing is becoming a promising technology in the field of geospatial. Backscatter coefficient value is influenced by the roughness of surface, type of target and moisture content of target. The aim of this research is to utilize L-band ALOS PALSAR-2 dataset and open source C-band Sentinel-1 SAR datasets to discriminate oil palm trees from weeds as there is significant height difference between them. This research determines the backscatter coefficient value range of oil palm trees and weeds, it investigates the suitability of utilizing C-band and L-band SAR data on oil palm tree discrimination. Several existing oil palm field parameters are tested based on the backscatter value of SAR image. The results and discussion of backscatter value ranges between oil palm tree species and weed species will be further discussed in the paper.

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Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements

Cited by 74 publications

References 27 publications

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Mapping Evapotranspiration Coefficients in a Temperate Maritime Climate Using the METRIC Model and Landsat TM

Application of SAR data for oil palm tree discrimination

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