Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%

An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising

Regional crop monitoring and discrimination based on simulated ENVISAT ASAR wide swath mode images

The current paper investigates the potential contribution of ENVISAT wide swath (WS) images for discrimination and monitoring of crops at a regional scale. The study was based on synthetic aperture radar (SAR) images acquired throughout an entire growing season. Advanced synthetic aperture radar sensor (ASAR) images in both narrow swath (NS) and WS modes were simulated based on 15 European Remote Sensing (ERS) satellite images recorded over Belgium. Unlike 'real' ASAR imagery, this exercise provided a consistent data set (i.e. same incidence angle, same acquisition date, same acquisition hour) to study the impact of spatial resolution on the SAR signal information content. A quantitative approach using 787 parcels of medium field size and various data combinations assessed monitoring and discrimination capabilities for six crop types: wheat, barley, grasses, sugar beet, maize and potato. The spatial resolution impact of the ASAR sensor was discussed with respect to the field size by comparing the results obtained from NS (30m) and WS (150m) mode images. WS temporal profiles were able to discriminate the various crops of interest and were representative of the crop development observed in the region. Furthermore, parcel-based unsupervised classifications successfully discriminated between grass, wheat, barley and other crops of large parcels (success rate of 83%). Dedicated interpretation schemes were developed in order to discriminate between cereal crops

Rigorous Derivation of Backscattering Coefficient.

A rigorous method for derivation of backscattering coefficient of SAR using the local digital derivation model is presented. Results with airborne and spaceborne data is shown

Remote sensing-based information and insurance for crop in emerging economics in Thailand (in Thai)

The Remote Sensing-based Information and Insurance for Crops in Emerging Economics (RIICE) is a project to find ways of helping Asian countries are faced with a natural disaster. Especially floods and droughts caused by the cooperation of the three organizations, namely International Research Institute (IRRI), SARMAP and Rice Department. By the year 2013-2015 in the area of responsibility of Suphan Buri Rice Research Center. Nakhon Ratchasrima Rice Research Center and the objecttive of the project is to reduce vulnerability of smallholders in rice production through better and cheaper information systems on crop growth which will in turn lead to applications such as micro-insurance schemes. On the long run rice production should have increased, thanks to better weather forecast in drought and flood prone areas and therefore better land management by farmers.In the year 2013 in Nakhon Ratchasima, using satellite COSMO Skymed, type stripmap, resolution 3-meter, width of the image 40x40 kilometers. In Suphanburi, using satellite COSMO Skymed, type scansar resolution of 15 meters, the width of the image 100x100 kilometers. In each field survey was conducted, collectting geographic coordinates, Managing of farmers, Environment and weather of 20 plots in each province. Leaf area index, crop cutting. After the end of the growing season, we survey in the fields if it was paddy field or non-paddy, totally of 100 points with the geographic coordinates to assess the accuracy of a program MapSCAPE Performance in 2013 for satellite imagery. The result of 2013 are, getting 7 imageries from Suphanburi, 11 imageries from Nakhon Ratchasima, They could be classified into cultivated area, flooded areas, The start of the growing season.The precision of the program by using Confusion Matrix computation, we found that the accurancy of the program in Suphanburi is 87.7%โครงการ Remote Sensing-based Information and Insurance for Crops in Emerging Economics (RIICE) เป็นโครงการหาแนวทางในการช่วยเหลือประเทศทางแถบเอเชียที่กำลังประสบกับปัญหาภัยธรรมชาติ โดยเฉพาะอุทกภัย และภัยแล้ง โดยเกิดจากความร่วมมือของ 3 องค์กรหลัก ได้แก่ International Research Institute(IRRI), SARMAP และกรมการข้าว โดยดำเนินการปี 2556-2558 ในพื้นที่รับผิดชอบของศูนย์วิจัยข้าวสุพรรณบุรี และศูนย์วิจัยข้าวนครราชสีมา โดยมีวัตถุประสงค์เพื่อ ลดความเสี่ยงของเกษตรกรรายย่อยในการผลิตข้าวโดยให้ได้รับข้อมูลด้านการผลิตพืช พื้นที่ปลูกข้าวที่ดีกว่าและแม่นยำ วิธีการดำเนินงานปี พ.ศ.2556 จังหวัดนครราชสีมา ใช้ภาพถ่ายดาวเทียม COSMO Skymed ประเภท stripmap ความละเอียดของภาพ 3 เมตร ความกว้างของภาพ 40x40 กิโลเมตร จังหวัดสุพรรณบุรี ใช้ภาพถ่ายดาวเทียม COSMO Skymed ประเภท scansar ความละเอียดของภาพ 15 เมตร ความกว้างของภาพ 100x100 กิโลเมตร ในแต่ละจังหวัดได้ดำเนินการสำรวจ เก็บพิกัดทางภูมิศาสตร์ การจัดการแปลงของเกษตรกร สภาพแวดล้อมและสภาพอากาศ จำนวน 20 แปลงในแต่ละจังหวัด เก็บข้อมูลดัชนีพื้นที่ใบข้าว ผลผลิตข้าวในแปลงเกษตรกร หลังจากสิ้นสุดฤดูปลูกทำการสำรวจพื้นที่นาข้าว และไม่ใช่นาข้าว จำนวน 100 จุด บันทึกพิกัดทางภูมิศาสตร์ เพื่อนำไปประเมินความแม่นยำของโปแกรม MapSCAPE ผลการดำเนินงานในปี 2556 ได้ภาพถ่ายดาวเทียมของจังหวัดสุพรรณบุรี จำนวน 7 ภาพ จังหวัดนครราชสีมา จำนวน 11 ภาพ ได้นำภาพถ่ายมาแปลเพื่อแสดงพื้นที่ปลูกข้าว พื้นที่ถูกน้ำท่วม การเริ่มฤดูปลูกข้าวของทั้ง 2 จังหวัด โดยใช้โปรแกรม MapSCAPE 5.0 ประมวลและแสดงพื้นที่ปลูกข้าว จากการวิเคราะห์ความแม่นยำจากการแปลภาพถ่ายดาวเทียม โดยใช้ Confusion Matrix Computation พบว่าการแปลภาพของจังหวัดสุพรรณบุรีมีความแม่นยำ 87.1

In-season early mapping of rice area and flooding dynamics from optical and SAR satellite data

Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r2 = 0.71 and flooded rice r2 = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage