Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring

Nasirzadehdizaji, Rouhollah; Çakır, Ziyadin; Şanlı, Füsun Balık; Abdikan, Saygın; Pepe, Antonio; Calò, Federica

doi:10.1016/j.compag.2021.106118

Cited by 46 publications

(33 citation statements)

References 28 publications

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“…The VH‐coherence was not used as it is less sensitive to changes occurring in the fields. This is also observed by Kavats et al (2018, 2019) and Nasirzadehdizaji et al (2021). One of the reasons for this is that VV is more sensitive to vegetation than VH due to vertical orientation of most vegetation.…”

Section: Methodssupporting

confidence: 79%

Identification of early abandonment in cropland through radar‐based coherence data and application of a Random‐Forest model

et al. 2022

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In the context of increased pressures on land for food and non‐food production, it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper, we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar‐based analysis is a relatively simple method to detect land abandonment in an early to longer term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions shows that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like Land Parcel Identification Systems data, in regions is one of the challenges in this respect.

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Section: Methodssupporting

confidence: 79%

Identification of early abandonment in cropland through radar‐based coherence data and application of a Random‐Forest model

et al. 2022

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“…S2 images have been widely used for crop mapping and monitoring, e.g., for the detection of land use anomalies [28], the estimation of biophysical parameters such as the leaf area index (LAI) [2,56] or more generally to provide information on the vegetation status [12]. Since S1 images can provide information regarding water content and the structure of the vegetation [29], they have been also largely investigated for crop monitoring and mapping [34,40,58]. Finally, the joint use of these sensors has been motivated by their complementary [14,27,39,41,55].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Mouret,

Albughdadi,

Duthoit

et al. 2021

Preprint

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Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning techniques, which generally assume that the feature matrix does not have missing values. This paper proposes a Gaussian Mixture Model (GMM) for the reconstruction of parcel-level features extracted from multispectral images. A robust version of the GMM is also investigated, since datasets can be contaminated by inaccurate samples or features (e.g., wrong crop type reported, inaccurate boundaries, undetected clouds, etc). Additional features extracted from Synthetic Aperture Radar (SAR) images using Sentinel-1 data are also used to provide complementary information and improve the imputations. The robust GMM investigated in this work assigns reduced weights to the outliers during the estimation of the GMM parameters, which improves the final reconstruction. These weights are computed at each step of an Expectation-Maximization (EM) algorithm by using outlier scores provided by the isolation forest (IF) algorithm. Experimental validation is conducted on rapeseed and wheat parcels located in the Beauce region (France). Overall, we show that the GMM imputation method outperforms other reconstruction strategies. A mean absolute error (MAE) of 0.013 (resp. 0.019) is obtained for the imputation of the median Normalized Difference Index (NDVI) of the rapeseed (resp. wheat) parcels. Other indicators (e.g., Normalized Difference Water Index) and statistics (for instance the interquartile range, which captures heterogeneity among the parcel indicator) are reconstructed at the same time with good accuracy. In a dataset contaminated by irrelevant samples, using the robust GMM is recommended since the standard GMM imputation can lead to inaccurate imputed values. An application to the monitoring of anomalous crop development in the presence of missing data is finally considered. In this application, using the proposed method leads to the best detection results, especially when SAR data are used jointly with multispectral images. Exploiting the information contained in cloudy multispectral images instead of removing these images is beneficial for this application.

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“…The acquired data are recorded in digital numbers, which are then processed into the backscattering measurements for analysis, and this process is known as radiometric measurement. A major benefit is that the estimation method can be easily applied over another sensor [118]. However, this transformation from one sensor to another requires the same model of the system and is not compatible with all models.…”

Section: Sar Backscattering Techniquementioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring

Cited by 46 publications

References 28 publications

Identification of early abandonment in cropland through radar‐based coherence data and application of a Random‐Forest model

Identification of early abandonment in cropland through radar‐based coherence data and application of a Random‐Forest model

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

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