Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition

Jiao, Xianfeng; McNairn, Heather; Yekkehkhany, Bahareh; Robertson, Laura Dingle; Ihuoma, Samuel

doi:10.1080/01431161.2022.2142077

Cited by 9 publications

(4 citation statements)

References 59 publications

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“…3 outlines the general processing steps to generate the CCD images from both sensors. For the Sentinel-1 images an orbit file was applied and images were burst merged and subset to the Area of Interest (AOI) [ 32 , 34 ]. The first step to create the coherence products was to co-register the two SLC images selected to create each CCD image pair.…”

Section: Methodsmentioning

confidence: 99%

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Robertson

McNairn

Kooij

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Robertson

McNairn

Kooij

et al. 2023

Heliyon

Self Cite

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“…In cases where a reliable S2 NDVI could not be acquired due to thick clouds, NDVIs from the S1 SAR were obtained instead, following a similar approach to that used by Dobrinic et al in their study [44]. Furthermore, a study conducted by Jiao et al [45] demonstrated a high correlation coefficient of 0.94 between the S2 NDVI and the S1 NDVI, thereby justifying this course of action.…”

Section: Stage 2a: Open-source Satellite Data Acquisition For the Est...mentioning

confidence: 99%

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Ballaran,

Ohara,

Rasmy

et al. 2024

AgriEngineering

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Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

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“…Optical Sentinel-2 is often preferred due to its capability to provide access to multispectral and multitemporal data. In some instances, Sentinel-2 data is combined with SAR data [23][24][25][26][27] to address issues related to inadequate resolution, substandard image quality, as well as limitations caused by cloud cover or the inability to collect data under low-light conditions. Among the various DL models utilized in the reviewed studies, LSTM networks and their variations, as well as CNNs, were the most commonly employed.…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition

Cited by 9 publications

References 59 publications

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

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