Application of Multi-Frequency Synthetic Aperture Radar (SAR) in Crop Classification

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Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development

Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P

Leaf nutrient traits of planted forests demonstrate a heightened sensitivity to environmental changes compared to natural forests

Leaf nutrient content (nitrogen, phosphorus) and their stoichiometric ratio (N/P) as key functional traits can reflect plant survival strategies and predict ecosystem productivity responses to environmental changes. Previous research on leaf nutrient traits has primarily focused on the species level with limited spatial scale, making it challenging to quantify the variability and influencing factors of forest leaf nutrient traits on a macro scale. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the differences in leaf nutrient traits between forest types (planted forests, natural forests) and their driving factors. Results show that leaf nutrient traits (leaf nitrogen content (LN), leaf phosphorus content (LP), and leaf N/P ratio) of planted forests are significantly higher than those of natural forests (P< 0.05). The impact of climatic and soil factors on the variability of leaf nutrient traits in planted forests is greater than that in natural forests. With increasing forest age, natural forests significantly increase in leaf nitrogen and phosphorus content, with a significant decrease in N/P ratio (P< 0.05). Climatic factors are key environmental factors dominating the spatial variability of leaf nutrient traits. They not only directly affect leaf nutrient traits of planted and natural forest communities but also indirectly through regulation of soil nutrients and stand factors, with their direct effects being more significant than their indirect effects

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.This research was funded in part by the Canadian Space Agency SOAR-E program (Grant No. SOAR-E-5489), the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 41531068), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P

The First Release of the CSTAR Point Source Catalog from Dome A, Antarctica

In 2008 January the 24th Chinese expedition team successfully deployed the Chinese Small Telescope ARray (CSTAR) to DomeA, the highest point on the Antarctic plateau. CSTAR consists of four 14.5cm optical telescopes, each with a different filter (g, r, i and open) and has a 4.5degree x 4.5degree field of view (FOV). It operates robotically as part of the Plateau Observatory, PLATO, with each telescope taking an image every 30 seconds throughout the year whenever it is dark. During 2008, CSTAR #1 performed almost flawlessly, acquiring more than 0.3 million i-band images for a total integration time of 1728 hours during 158 days of observations. For each image taken under good sky conditions, more than 10,000 sources down to 16 mag could be detected. We performed aperture photometry on all the sources in the field to create the catalog described herein. Since CSTAR has a fixed pointing centered on the South Celestial Pole (Dec =-90 degree), all the sources within the FOV of CSTAR were monitored continuously for several months. The photometric catalog can be used for studying any variability in these sources, and for the discovery of transient sources such as supernovae, gamma-ray bursts and minor planets.Comment: 1 latex file and 9 figures The paper is accepted by PAS

The sky brightness and transparency in i-band at Dome A, Antarctica

The i-band observing conditions at Dome A on the Antarctic plateau have been investigated using data acquired during 2008 with the Chinese Small Telescope ARray. The sky brightness, variations in atmospheric transparency, cloud cover, and the presence of aurorae are obtained from these images. The median sky brightness of moonless clear nights is 20.5 mag arcsec^{-2} in the SDSS $i$ band at the South Celestial Pole (which includes a contribution of about 0.06 mag from diffuse Galactic light). The median over all Moon phases in the Antarctic winter is about 19.8 mag arcsec^{-2}. There were no thick clouds in 2008. We model contributions of the Sun and the Moon to the sky background to obtain the relationship between the sky brightness and transparency. Aurorae are identified by comparing the observed sky brightness to the sky brightness expected from this model. About 2% of the images are affected by relatively strong aurorae.Comment: There are 1 Latex file and 14 figures accepted by A

Comprehensive genomic profiling reveals prognostic signatures and insights into the molecular landscape of colorectal cancer

BackgroundColorectal cancer (CRC) is a prevalent malignancy with diverse molecular characteristics. The NGS-based approach enhances our comprehension of genomic landscape of CRC and may guide future advancements in precision oncology for CRC patients.MethodIn this research, we conducted an analysis using Next-Generation Sequencing (NGS) on samples collected from 111 individuals who had been diagnosed with CRC. We identified somatic and germline mutations and structural variants across the tumor genomes through comprehensive genomic profiling. Furthermore, we investigated the landscape of driver mutations and their potential clinical implications.ResultsOur findings underscore the intricate heterogeneity of genetic alterations within CRC. Notably, BRAF, ARID2, KMT2C, and GNAQ were associated with CRC prognosis. Patients harboring BRAF, ARID2, or KMT2C mutations exhibited shorter progression-free survival (PFS), whereas those with BRAF, ARID2, or GNAQ mutations experienced worse overall survival (OS). We unveiled 80 co-occurring and three mutually exclusive significant gene pairs, enriched primarily in pathways such as TP53, HIPPO, RTK/RAS, NOTCH, WNT, TGF-Beta, MYC, and PI3K. Notably, co-mutations of BRAF/ALK, BRAF/NOTCH2, BRAF/CREBBP, and BRAF/FAT1 correlated with worse PFS. Furthermore, germline AR mutations were identified in 37 (33.33%) CRC patients, and carriers of these variants displayed diminished PFS and OS. Decreased AR protein expression was observed in cases with AR germline mutations. A four-gene mutation signature was established, incorporating the aforementioned prognostic genes, which emerged as an independent prognostic determinant in CRC via univariate and multivariate Cox regression analyses. Noteworthy BRAF and ARID2 protein expression decreases detected in patients with their respective mutations.ConclusionThe integration of our analyses furnishes crucial insights into CRC’s molecular characteristics, drug responsiveness, and the construction of a four-gene mutation signature for predicting CRC prognosis

AGRICULTURE LAND USE MAPPING USING MULTI-SENSOR AND MULTI- TEMPORAL EARTH OBSERVATION DATA

ABSTRACT Large area mapping of crop information is a crucial source of information on agricultural land use, and can be done most efficiently using Earth observation (EO) technology. Agriculture and Agri-Food Canada (AAFC) is developing a crop inventory system based on EO data. Detailed crop type identification relies on image data acquired during key crop phenological stages in order to capture the unique temporal signature of individual crop types. Optical data such as Landsat and SPOT can provide valuable information for crop classification, however, cloud cover is a reoccurring obstacle to the application of optical data, hindering mapping and monitoring at regional and national scales. Consequently, the integration of radar with optical EO data is essential for operational monitoring over many areas. Crop information mapping from EO data was conducted by AAFC for two consecutive years (2004 and 2005) over an Eastern Ontario pilot site. Landsat, SPOT, RADARSAT (standard mode), and Envisat ASAR (VV, VH) data were acquired during the growing seasons for both years. This site consists primarily of corn, soybean, cereal, forage production, and animal pasture. Three classification techniques (MaximumLikelihood Classifier, Decision-Tree Classifier, and Neural Network Classifier) were applied to various image combinations. Overall, these results show that data acquired later in the growing season provide a better classification accuracy for both optical and radar data. The integration of radar and optical data resulted in a synergistic effect, producing an increase in classification accuracies (individual class accuracy, overall accuracy, and Kappa coefficient)

Urban land use mapping using high resolution SAR data based on density analysis and contextual information.

This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building�road, urban block, and suburban commercial�industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial�industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%