Operational Near Real Time Rice Area Mapping Using Multi-Temporal Sentinel-1 Sar Observations

Abstract

<p><strong>Abstract.</strong> 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<span class="thinspace"></span>% 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 <i>Kharif</i> season). Hence, Sentinel-1 C-band (center frequency: 5.405<span class="thinspace"></span>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 andWest Godavari from Andhra Pradesh (AP), India during the period of <i>Kharif</i> 2017. The study region is also called as coastal AP where rice transplanting during the <i>Kharif</i> 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<span class="thinspace"></span>%. 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<span class="thinspace"></span>%. However, accuracy assessment performed using independent stratified random sampling approach showed the classification accuracy of 84.45<span class="thinspace"></span>%. 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.</p>

Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform

“…Due to the seasonal dynamics of growth stages, time series of SAR datasets are required to detect the unique pattern of rice fields. Previous studies have used different Sentinel-1 time series intervals, ranging from 1-3 months [22], to six months or one rice season [10,21]. These short-term datasets cannot capture rice growth patterns, especially for rice fields with double crops.…”

“…Various researchers have attempted to establish a Sentinel-1 time series database for rice field identification. Layer stacks of time series based on acquisition date is more commonly used, such as [10,21,22]. Another approach is to use temporal metrics, including the 50th percentile and the standard deviation of VH polarization time series, and the 10th percentile of VV polarized data [19].…”

“…Together, supervised classification methods at the pixel level and Sentinel-1 time series data can be used to automatically map rice fields [9,10,14,22]. Another approach is the application of phenology-based decision trees at the segment level that has resulted in superpixel segmentation using the simple linear iterative clustering (SLIC) algorithm [19].…”

“…• tackle food security issues, especially in countries where rice is the major staple food; • identify and forecast rice production in a region; • manage water security, as paddy rice consumes a large amount of water [1]; • with greenhouse gas accounting, as paddy rice releases methane (CH 4 ) to the atmosphere [2]; • form government policies. extent in India [21,22]. However, these studies only distinguished between rice and non-rice areas, and early and late rice.…”

Investigating the Performance of Random Forest and Support Vector Regression for Estimation of Cloud-Free Ndvi Using Sentinel-1 Sar Data

“…Continuous regional crop mapping and monitoring is essential especially in countries like India to keep a track on spatio-temporal coverage of various crops. This information can be consumed by various stakeholders like the government for the planning of various import-export activities, agri-input companies for facilitation of various fertilizers/chemicals, farmers to get the status of their crop in real-time (Mohite et al (2018)). Satellite based remote sensing sensors are being effectively used over the years for continuous crop mapping and monitoring.…”