Analysis of the applicability of multi-temporal full polarimetric airborne L-band SAR scattering to paddy rice field mapping

ABSTRACT

A Synthetic Aperture Radar (SAR) is an all-weather imaging system that is often used for mapping paddy rice fields and estimating the area. Fully polarimetric SAR is used to detect the microwave scattering property. In this study, a simple threshold analysis of fully polarimetric L-band SAR data was conducted to distinguish paddy rice fields from soybean and other fields. We analysed a set of ten airborne SAR L-band 2 (Pi-SAR-L2) images obtained during the paddy rice growing season (in June, August, and September) from 2012 to 2014 using polarimetric decomposition. Vector data for agricultural land use areas were overlaid on the analysed images and the mean value for each agricultural parcel computed. By quantitatively comparing our data with a reference dataset generated from optical sensor images, effective polarimetric parameters and the ideal observation season were revealed. Double bounce scattering and surface scattering component ratios, derived using a four-component decomposition algorithm, were key to extracting paddy rice fields when the plant stems are vertical with respect to the ground. The alpha angle was also an effective factor for extracting rice fields from an agricultural area. The data obtained during August show maximum agreement with the reference dataset of estimated paddy rice field areas.     

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.     

基于SAR与Landsat TM的小区域稻田的识别研究——以南京市江宁区为例

以南京市江宁区为研究区域,根据区域特征、作物物候期和水稻的生长特点,采用分层分类的方法提取稻田分布信息。通过比较多时相SAR数据、TM和多时相SAR融合与TM和单时相SAR融合数据识别水稻的精度和提取的水稻种植面积,分析了不同数据对区域多云雨,不同种植方式、面积小且分布破碎的水稻稻田的识别程度,并根据野外实地走访调查分析了主要影响因素。结果表明:多时相SAR数据、TM和多时相SAR数据的水稻识别精度都高于72%,高于TM和单时相SAR融合数据的结果;前两者提取的水稻种植面积和稻田分布接近,主要影响因素是地物分布、不同种植方式水稻物候期和水稻稻田面积小且分布破碎。     

Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta

Because of the importance of rice for the global food security and because of the role of inundated paddy fields in greenhouse gases emissions, monitoring the rice production world-wide has become a challenging issue for the coming years. Local rice mapping methods have been developed previously in many studies by using the temporal change of the backscatter from C-band synthetic aperture radar (SAR) co-polarized data. The studies indicated in particular the need of a high observation frequency. In the past, the operational use of these methods has been limited by the small coverage and the poor acquisition frequency of the available data (ERS-1/2, Radarsat-1). In this paper, the method is adapted for the first time to map rice at large scale, by using wide-swath images of the Advanced SAR (ASAR) instrument onboard ENVISAT. To increase the observation frequency, data from different satellite tracks are combined. The detection of rice fields is achieved by exploiting the high backscatter increase at the beginning of the growing cycle, which allows the production of rice maps early in the season (in the first 50 days). The method is tested in the Mekong delta in Vietnam. The mapping results are compared to existing rice maps in the An Giang province, with a good agreement (higher than 81%). The rice planted areas are retrieved from the maps and successfully validated with the official statistics available at each province (R² = 0.92). These results show that the method is useful for large scale early mapping of rice areas, using current and future C band wide-swath SAR data.     

Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.     

Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R ², value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice.     

A study of the use of COSMO-SkyMed SAR PingPong polarimetric mode for rice growth monitoring

The sensitivity of COSMO-SkyMed (CSK) incoherent dual-polarimetric synthetic aperture radar (SAR) data to the rice growth cycle is investigated here. State-of-the-art scattering models are used, together with a time series of 24 CSK SAR images collected in Mekong Delta, Vietnam in 2014, to interpret the behaviour of multi-polarization features with respect to the different phenological stages that characterize rice growth. Experimental results show the multi-polarization features sensitivity with respect to rice growth cycle and witness that a joint use of the co-polarized channels (i.e. co-polarized ratio or correlation between co-polarized channels) allows identifying scattering behaviours that are compatible with four stages of the rice growth cycle.     

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi-temporal ERS-2 synthetic aperture radar

We explored the use of the European Remote Sensing Satellite 2 Synthetic Aperture Radar (ERS-2 SAR) to trace the development of rice plants in an irrigated area near Niono, Mali and relate that to the density of anopheline mosquitoes, especially An. gambiae. This is important because such mosquitoes are the major vectors of malaria in sub-Saharan Africa, and their development is often coupled to the cycle of rice development. We collected larval samples, mapped rice fields using GPS and recorded rice growth stages simultaneously with eight ERS-2 SAR acquisitions. We were able to discriminate among rice growth stages using ERS-2 SAR backscatter data, especially among the early stages of rice growth, which produce the largest numbers of larvae. We could also distinguish between basins that produced high and low numbers of anophelines within the stage of peak production. After the peak, larval numbers dropped as rice plants grew taller and thicker, reducing the amount of light reaching the water surface. ERS-2 SAR backscatter increased concomitantly. Our data support the belief that ERS-2 SAR data may be helpful for mapping the spatial patterns of rice growth, distinguishing different agricultural practices, and monitoring the abundance of vectors in nearby villages.     

Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data

A unique physical feature of paddy rice fields is that rice is grown on flooded soil. During the period of flooding and rice transplanting, there is a large proportion of surface water in a land surface consisting of water, vegetation and soils. The VEGETATION (VGT) sensor has four spectral bands that are equivalent to spectral bands of Landsat TM, and its mid-infrared spectral band is very sensitive to soil moisture and plant canopy water content. In this study we evaluated a VGT-derived normalized difference water index (NDWI VGT =(B3-MIR)/ (B3+MIR)) for describing temporal and spatial dynamics of surface moisture. Twenty-seven 10-day composites (VGT- S10) from 1 March to 30 November 1999 were acquired and analysed for a study area (175 km by 165 km) in eastern Jiangsu Province, China, where a winter wheat and paddy rice double cropping system dominates the landscape. We compared the temporal dynamics and spatial patterns of normalized difference vegetation index (NDVI VGT ) and NDWI VGT . The NDWI VGT temporal dynamics were sensitive enough to capture the substantial increases of surface water due to flooding and rice transplanting at paddy rice fields. A land use thematic map for the timing and location of flooding and rice transplanting was generated for the study area. Our results indicate that NDWI and NDVI temporal anomalies may provide a simple and effective tool for detection of flooding and rice transplanting across the landscape.     

The relationship between the leaf area index (LAI) of rice and the C‐band SAR vertical/horizontal (VV/HH) polarization ratio

We investigated the relationship between the leaf area index (LAI) of rice and the ENVISAT Advanced Synthetic Aperture Radar (ASAR) vertical/horizontal (VV/HH) polarization ratio. Four alternating polarization ASAR images of swaths IS4 and IS5 over rice fields were used in the study. The VV/HH polarization ratio correlates well with the field‐measured LAI and an empirical relationship was established to estimate the LAI of rice using the VV/HH polarization ratio. A theoretical radiative transfer model was adopted to analyse the relationship. The error of the estimated LAI was 0.17 for the test site and a better correlation was found when LAI was less than 3.5. The results suggest that ASAR alternating polarization data can be used to estimate the LAI of rice for wide‐area monitoring of rice growth.     

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.     

Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

The phenological stages of onion fields in the first year of growth are estimated using polarimetric observables and single-polarization intensity channels. Experiments are undertaken on a time series of RADARSAT-2 C-band full-polarimetric synthetic aperture radar (SAR) images collected in 2009 over the Barrax region, Spain, where ground truth information about onion growth stages is provided by the European Space Agency (ESA)-funded agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) field campaign conducted in that area. The experimental results demonstrate that polarimetric entropy or copolar coherence when used jointly with the cross-polarized intensity allows unambiguously distinguishing three phenological intervals.     

Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model

Recent developments in unmanned aerial system (UAS) require an urgent introduction to monitoring technologies of crop diagnostic information because of their advantage in manoeuvering tasks at a high-spatial resolutions and low costs in a user-friendly manner. In this study, an advanced application method of an UAS remote sensing system was performed using the grid GRAMI-rice model such that it can be driven using weather and remote sensing data to monitor the spatiotemporal productivities of rice (Oryza sativa). Remotely sensed data for the model were supplied, along with normalized difference vegetation index images obtained using the UAS remote sensing system. The model was first evaluated using paddy data from experimental fields (treated with two nitrogen (N) applications) at Chonnam National University, Gwangju, Republic of Korea (ROK). Practical application was then performed using paddy data from farm fields under conventional farm management practices at the Gimje plain in ROK. The grid GRAMI-rice model statistically well reproduces the field conditions of spatiotemporal rice productivities, showing an acceptable statistical accuracy in the comparison of growth between the simulated and observed values, using a Nash–Sutcliffe efficiency range of 0.113–0.955. According to t-tests (α = 0.05), there were no significant differences between the simulated and observed grain yields from both the evaluation and practical applications. The scientific approach adopted here is unique, advanced, and practical, in a way that UAS remote sensing methods were effectively incorporated with crop modelling techniques. Therefore, it was concluded that the UAS-based remote sensing techniques proposed in this study could represent an innovative way of projecting reliable spatiotemporal crop productivities for precision agriculture.     

基于RadarSat-2全极化数据的水稻识别

极化信息是雷达数据的独特优势,为雷达遥感应用研究开辟了新的途径。极化分解是一种新型的极化数据处理方法,它从数学物理的角度分析目标的散射机制。基于RadarSat\|2全极化数据,以贵州高原丘陵为试验区,研究水稻的极化响应特征及其时域变化规律,根据极化分解理论分析水稻及典型地物的散射机制及其差异,并根据水稻散射机制的特点提取水稻信息。     

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.     

Application of neural network swarm optimization for paddy-field classification from remote sensing data

Monitoring changes in paddy areas is important for economic and environmental research, since rice is a staple food in Asia and paddy agriculture is a major cropping system. Recently, remote sensing has been used to observe changes in the areas of paddy. However, monitoring paddy areas by remote sensing is difficult owing to the temporal changes in paddy, and the differences in the spatiotemporal characteristics of paddy agriculture between countries or regions. In our previous research using a multilayered perceptron and spatiotemporal satellite sensor data, the proposed classifier yielded a correct classification rate of 90.8%. In this article, we proposed a cooperative learning method using particle swarm optimization as the global search method and a multilayered perceptron as the local search method in order to improve the classification accuracy for practical use.     

Early estimation of rice area using temporal ERS-1 synthetic aperture radar data a case study for the Howrah and Hughly districts of West Bengal,India

The characteristic temporal backscattering signature of rice crop grown under flooded condition was used to estimate rice acreage for a region in West Bengal, India. To date ERS-1 Synthetic Aperture Radar (SAR) data, one acquired within 30 days of transplantation and another after 30-40 days was found to be optimum for early estimation of rice acreage. The rice crop was found to be distinctly separable from forest, tree vegetation, village/urban areas. Misclassification of rice was observed mainly with water, waterlogged areas and fallow fields.     

A SAR image method of detecting target changes using two-dimensional scattering centre characteristics

Remote-sensing image interpretations and applications require information on changes in the target. In high-resolution synthetic aperture radar (SAR) images, multi-scattering centres reflect the characteristics of target scattering, but not those of point targets or point scattering. Total scattering is the vector summation of each scattering centre. These scattering centres include shape and structural information of the target. When a target changes, both the scattering characteristics and the scattering centres change. In this way, changes in the centres may cancel out changes in the target. This article proposes a new method of change detection for SAR image targets using the two-dimensional scattering centre characteristics (TDSCC). This method is here called the TDSCC algorithm. This algorithm differs from other change detection algorithms that are based on image fields. General change detection algorithms require accurate registration. Otherwise, the change information is inaccurate. The TDSCC method is a feature-level or target-level change detection algorithm and it does not require registration operation. Therefore, it avoids errors in change information. The experimental data have confirmed the feasibility of the proposed algorithm.     

Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.