Monitoring rice areas using LANDSAT MSS data

McCloy, Keith R.; Smith, Fredy; Robinson, M.

doi:10.1080/01431168708948685

Cited by 37 publications

(20 citation statements)

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“…Geographic Information System (GIS) data have been proved to be important to enhance the accuracy of land use and land cover classification [13,14]. RS data with coarse and medium resolution are widely used in rice cultivation research [12,[15][16][17][18][19][20][21][22][23][24][25][26]. However, the number of conducted studies on rice using high resolution RS images was limited in the past two decades [23,27,28].…”

Section: Introductionmentioning

confidence: 99%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error 237(RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

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Section: Introductionmentioning

confidence: 99%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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“…In this case, it is unrealistic to obtain both high areal and positional accuracy at the same time, so images with higher resolution are indispensable to satisfy the accuracy requirements. Although some authors represented very high overall areal accuracy in their papers, and the accuracy even exceeding 99 % (McCloy et al 1987;Quarmby et al 1992;Fang et al 1998;Okamoto and Kawashima 1999) because the underestimated and overestimated parts might cancel each other, as described in the report of Van Niel and McVicar (2001). It is very easy to get a high overall areal accuracy through adjusting the threshold t from the above analyses in this study.…”

Section: ) As Shown Inmentioning

confidence: 92%

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

Sun

2015

J Indian Soc Remote Sens

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This study analyzed the relationship between the spatial resolution and the hard classification effect based on pixel-based image classification, and then discussed how to determine appropriate spatial resolution. Thematic maps of winter wheat derived from 250 m MODIS image, 19.5 m China-Brazil Earth Resources Satellite (CBERS) image, and 2.44 m QuickBird image were used to examine the classification effect as a case study. It indicated that the BPareto Boundaries^and the Bwithin-class variability^could be used to determine the coarsest and the highest resolution for hard classification, respectively. The methods proposed in this study should be useful to guide how to select appropriate spatial resolution for land cover mapping.

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“…To date, many studies have been conducted to map paddy rice using fine-resolution satellite images, such as Landsat MSS, TM, ETM+ and NOAA/AVHRR images, by applying image classification procedures, but few of these studies have provided detailed information regarding the locations of multi-cropping [7][8][9][10]. Furthermore, because of the fine resolution of these images, it is difficult to obtain more comprehensive images covering an entire region simultaneously over a large area.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

Shi

Huang

2015

Remote Sensing

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Abstract:A large-area map of the spatial distribution of rice is important for grain yield estimations, water management and an understanding of the biogeochemical cycling of carbon and nitrogen. In this paper, we developed the Normalized Weighted Difference Water Index (NWDWI) for identifying the unique characteristics of rice during the flooding and transplanting period. With the aid of the ASTER Global Digital Elevation Model and the phenological data observed at agrometeorological stations, the spatial distributions of single cropping rice and double cropping early and late rice in the Yangtze River Delta region were generated using the NWDWI and time-series Enhanced Vegetation Index data derived from MODIS/Terra data during the 2000-2010 period. The accuracy of the MODIS-derived rice planting area was validated against agricultural census data at the county level. The spatial accuracy was also tested based on a land use map and Landsat ETM+ data. The decision coefficients for county-level early and late rice were 0.560 and 0.619, respectively. The MODIS-derived area of late rice exhibited higher consistency with the census data during the 2000-2010 period. The algorithm could detect and monitor rice fields with different cropping patterns at the same site and is useful for generating spatial datasets of rice on a regional scale.

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Monitoring rice areas using LANDSAT MSS data

Cited by 37 publications

References 1 publication

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

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