Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

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SITE SUITABILITY ANALYSIS FOR GROWING SHORT DURATION CROPS IN POST KHARIF RICE-RABI FALLOWS: AN EXPLORATORY STUDY FOR FOUR DISTRICTS OF EASTERN INDIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-659-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 659-663

Author(s):

M. Prashnani ◽

S. Saxena ◽

K. Chaudhary ◽

C. S. Murthy ◽

S. S. Ray

Keyword(s):

Remote Sensing ◽

Food Security ◽

Short Duration ◽

Exploratory Study ◽

Physical Parameters ◽

Landsat 8 ◽

Suitability Analysis ◽

Indian States ◽

Rice Area ◽

Suitable Area

Abstract. India is one of the leading rice producing countries in the world. In eastern part of the country, after harvesting of kharif rice crops, large area remains fallow, mostly due to unavailability of irrigation facility. Approximately 30 percent of total kharif rice area (i.e. around 12&thinsp;Mha) is left fallow during rabi season. Government of India, in order to improve the land productivity, cropping intensity and food security, has taken up various initiatives to grow pulses in these areas. A project was launched under the National Food Security Mission for use of satellite remote sensing for suitability analysis towards crop intensification in 6 eastern Indian states. Before execution of the project an exploratory study was carried out for 4 selected districts of 2 major rice-fallow states of the country. The selected districts were Raipur and Surguja districts of Chhattisgarh state and Mayurbhanj and Balasore districts of Odisha state. Kharif rice area was mapped using RISAT-1 SAR data, while rabi fallow area was mapped using Resourcesat-2 AWiFS and Landsat-8 OLI data. Suitable area for growing pulses were analysed with respect to various soil, weather and land parameters such as, mean monthly air temperature (&deg;C), Slope (%), Surface wetness, Plant available water capacity (mm) and proximity to drainage network. The post kharif rice-rabi fallow area, in these districts ranged between 20–40% of the geographical area, while the suitable area was found to be in range of between 8–12%. Thus, this exploratory study could show, with remote sensing and other agro-physical parameters, it is possibly not only to map the rice–fallow area, but also to assess the suitability of growing short duration rabi crops.

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RICE AREA ESTIMATION USING PARAMETERIZED CLASSIFICATION OF SENTINEL 1A SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-141-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 141-147 ◽

Cited By ~ 1

Author(s):

M. G. Raman ◽

R. Kaliaperumal ◽

S. Pazhanivelan ◽

B. Kannan

Keyword(s):

Satellite Data ◽

Tamil Nadu ◽

Accuracy Assessment ◽

Kappa Index ◽

Transplanted Rice ◽

Cropping Season ◽

Rice Area ◽

Multi Temporal ◽

Sar Data ◽

Crop Parameters

Abstract. A research study was conducted during Rabi 2016 (Samba season) to estimate rice area using SAR data in Tiruvarur district of Tamil Nadu. Multi temporal Sentinel 1A satellite data with VV and VH polarization at 20&thinsp;m spatial resolution was acquired between September 2016 and January 2017 at 12 days interval and processed using rule-based Parameterized classification in MAPscape-RICE software. Continuous monitoring for crop parameters and validation exercise was done for accuracy assessment. Spectral dB curve of rice was generated and the dB values ranged from &minus;12.76 to &minus;9.95 for VV and from &minus;19.25 to &minus;15.15 for VH polarization with an average primary variation of 1.3 and 2.5&thinsp;dB respectively. Start of Season (SOS) map was derived from satellite data showing rice emergence dates for the cropping season. A total rice area of 106773&thinsp;ha was estimated in Tiruvarur district using VV polarization with an overall accuracy of 79.5% and 0.59 kappa index, while in VH polarization, the rice area was estimated to be 91007&thinsp;ha with 82.1% over all accuracy and 0.64 kappa index. The lesser accuracy in VV polarization was due to underestimate of direct seeded rice area and in VH polarization, it was due to underestimate in Transplanted rice area. The VV and VH rice area maps were then integrated to derive a VV-VH rice area map in MAPscape-RICE software and it recorded a total rice area of 124551 ha with an accuracy of 91.5% and 0.83-kappa index.

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RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-863-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 863-865 ◽

Cited By ~ 6

Author(s):

C. F. Chen ◽

N. T. Son ◽

C. R. Chen ◽

L. Y. Chang ◽

S. H. Chiang

Keyword(s):

Accuracy Assessment ◽

Close Correlation ◽

Rice Crop ◽

Crop Acreage ◽

Study Region ◽

Rice Area ◽

Multi Temporal ◽

Crop Mapping ◽

Cropping Seasons ◽

Phenological Metrics

Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R2 > 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.

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RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-863-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 863-865

Author(s):

C. F. Chen ◽

N. T. Son ◽

C. R. Chen ◽

L. Y. Chang ◽

S. H. Chiang

Keyword(s):

Accuracy Assessment ◽

Close Correlation ◽

Rice Crop ◽

Crop Acreage ◽

Study Region ◽

Rice Area ◽

Multi Temporal ◽

Crop Mapping ◽

Cropping Seasons ◽

Phenological Metrics

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Applied Sciences ◽

10.3390/app11156923 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6923

Author(s):

Rui Zhang ◽

Zhanzhong Tang ◽

Dong Luo ◽

Hongxia Luo ◽

Shucheng You ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Identification Accuracy ◽

Optical Remote Sensing ◽

Sar Image ◽

Single Time ◽

Growing Period ◽

Sensing Technology ◽

Sar Imagery ◽

Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

Remote Sensing ◽

10.3390/rs12111772 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1772

Author(s):

Brian Alan Johnson ◽

Lei Ma

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Image Segmentation ◽

Online Survey ◽

Accuracy Assessment ◽

Learning Approaches ◽

Special Issue ◽

Object Based Image Analysis ◽

Object Based ◽

Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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