Suchergebnisse

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 andWest 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.

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 andWest 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.