Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France (original) (raw)
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Remote Sensing
The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern...
Operational Near Real Time Rice Area Mapping Using Multi-Temporal SENTINEL-1 Sar Observations
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Spatio-temporal crop phenological information helps in understanding trends in food supply, planning of seed/fertilizer inputs, etc. in a region. Rice is one of the major food sources for many regions of the world especially in monsoon Asia and accounts for more than 11% of the global cropland. Accurate, on-time and early information on spatial distribution of rice would be useful for stakeholders (cultivators, fertilizer/pesticide manufacturers and agriculture extension agencies) to effectively plan supply of inputs, market activities. Also, government agencies can plan and formulate policies regarding food security. Conventional methods involves manual surveying for developing spatio-temporal crop datasets while remote sensing satellite observations provide cost effective alternatives with better spatial extent and temporal frequency. Remote sensing is one of the effective technologies to map the areal extent of the crops using optical as well as microwave/Synthetic Aperture RADAR (SAR) sensors. Cloud cover is the major problem faced in using the optical datasets during monsoon (June to Sept. locally called Kharif season). Hence, Sentinel-1 C-band (center frequency: 5.405 GHz) RADAR sensor launched by European Space Agency (ESA) which has an Interferometric Wide-swath mode (IW) with dual polarization (VV and VH) has been used for rice area mapping. Limited studies have attempted to establish operational early season rice area mapping to facilitate local governance, agri-input management and crop growers. The key contribution of this work is towards operational near real time and early season rice area mapping using multi-temporal SAR data on GEE platform. The study has been carried out in four districts viz., Guntur, Krishna, East Godavari and West Godavari from Andhra Pradesh (AP), India during the period of Kharif 2017. The study region is also called as coastal AP where rice transplanting during the Kharif season is carried out during mid Jun. till Aug. and harvesting during Oct. to mid Dec. months. The training data for various classes viz, Rice, NonRice-Agriculture, Waterbodies, Settlements, Forest and Aquaculture have been obtained from GEE, Global Land Cover (GLC) layers developed by ESA and field observations. We have evaluated the performance of Random Forest (RF) classifier by varying the number of trees and incrementally adding the SAR images for model training. Initially the model has been trained considering two images available from mid June 2017. Further, various models have been trained by adding one consecutive image till end of August 2017 and classification performance has been evaluated on validation dataset. The classified output has been further masked with agriculture non-agriculture layer derived from global land-cover layer obtained from ESA. Analysis shows that incremental addition of temporal observations improves the performance of the classifier. The overall classification accuracy ranges between 78.11 to 87.00%. We have found that RF classifier with 30 trees trained on six images available from mid June till end August performed better with classification accuracy of 87.00%. However, accuracy assessment performed using independent stratified random sampling approach showed the classification accuracy of 84.45%. An attempt is being made to follow the proposed approach for current (i.e. 2018) season and provide incremental rice area estimates in near real-time.
International Journal of Remote Sensing
Sentinel-1A Synthetic Aperture Radar (SAR) data present an opportunity for acquiring crop information without these restrictions, 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 Synthetic Aperture Radar (SAR) data and Landsat-derived normalised 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 to October). A co-registered set of ten dual polarisation (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 polarisation 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 polarisation and NDVI data. The SVM classifier was most effective when applied to the dual polarisation (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 10m 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.
Rice-Planted Area Mapping Using Small Sets of Multi-Temporal SAR Data
IEEE Geoscience and Remote Sensing Letters, 2013
A rice-planted area map is a basic information resource for rice production management. Synthetic aperture radar (SAR) is an appropriate technique for rice mapping and so far is mostly based on extracting time series changes of backscattering (σ 0) in a rice-planted area. However, sometimes there is not enough data to extract the σ 0 curve for the area. To overcome this problem of a lack of data, we propose a method to detect rice-planted area by using small sets of multi-temporal SAR data. This method also addresses the fluctuation of σ 0 values between SAR measurements. We have applied the method using multitemporal ALOS/PALSAR data acquired over five years during the dry season. The rice-planted area was well detected and the viability of this method was demonstrated. Index Terms-Normalization of backscatter, rice mapping, small sets of temporal data, synthetic aperture radar (SAR).
Rice crop monitoring using new generation Synthetic Aperture Radar (SAR) imagery
2009
Rice cultivation systems in various countries of the world have been changing in recent years. These changes have been observed in the Mekong River Delta, Vietnam, specifically in An Giang province. The changes in rice cultural practices have impacts on remote sensing methods developed for rice monitoring, in particular, methods using new generation radar data. The objectives of the study were a) to understand the relationship between radar backscatter coefficients and selected parameters (e.g. plant age and biomass) of rice crops over an entire growth cycle, b) to develop algorithms for mapping rice cropping systems, and c) to develop a rice yield prediction model using time-series Envisat (Environmental Satellite) Advanced Synthetic Aperture Radar (ASAR) imagery. Ground data collection and in situ measurement of rice crop parameters were conducted at 35 sampling fields in An Giang province, Mekong River Delta, Vietnam. The average values of the radar backscattering coefficients that corresponded to the sampling fields were extracted from the ASAR Alternative Polarisation Precision (APP) images (C band, spatial resolution of 30 m, and swath width of 100 km). The temporal rice backscatter behaviour during the cropping seasons, including Winter Spring (WS), Summer Autumn (SA), and Autumn Winter (AW), were analysed for HH (Horizontal transmit and Horizontal receive), VV (Vertical transmit and Vertical receive), and polarisation ratio data. In addition, the relationships between rice biomass and backscattering coefficient of HH, VV, and polarisation ratio were established. The methods were examined for rice identification and mapping in the study area by using ASAR APP and Wide Swath (WS) imagery. ASAR APP data were firstly used to determine the best method with high accuracy for rice delineation. Then, the proposed method was applied for ASAR WS data (C band, 150 m spatial resolution, and 450 km swath width), covering the entire agricultural region of the An Giang province. Based on the discovered relationships between rice parameters and radar backscattering, a thresholding method applied for polarisation ratio and VV polarisation values of single-date ASAR APP data acquired in the middle of v crop season was found to be the best method among various classification methods. Another threshold, i.e. the "normalised difference polarisation ratio (NDRa) index",
C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems
International Journal of Remote Sensing, 2020
Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites-including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies-can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user's and producer's accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for ARTICLE HISTORY
Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
Remote Sensing
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.
Spatial Information Research, 2019
Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3-77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108-126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and accuracy assessment of LULC maps revealed that the SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.
Remote Sensing
This paper shows the efficiency of machine learning for improving land use/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 1...
Classification of Orchard Crop Using SENTINEL-1A Synthetic Aperture Radar Data
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47 %, 0.47 %, 28.3 %, 28.3 % and 25.5 % respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.