Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Peng, Dailiang; Huete, Alfredo; Huang, Jingfeng; Wang, Fuming; Sun, Hongxiang

doi:10.1016/j.jag.2010.06.001

Cited by 137 publications

(89 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variable EVI/LSWI2130 function was used for paddy rice monitoring and detection during the transplanting period [2,47,48]. A series of MODIS VI data were collected from day 001 to day 361.…”

Section: Paddy Rice Detectionmentioning

confidence: 99%

“…The time series profiles of LSWI2130 and EVI were constructed throughout the year 2013 ( Figure 5) for analyzing the rice growth phenology, in order to identify the paddy rice field area. In this study, the crop phenology was categorized into three main stages [47]: flooding and transplanting, growing, and harvesting stages using time series profiles of EVI and LSWI2130. During the fallow stage, the LSWI2130 value is generally lower than that of the EVI (day 001 to day 145).…”

Section: Paddy Rice Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

View full text Add to dashboard Cite

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

show abstract

Section: Paddy Rice Detectionmentioning

confidence: 99%

Section: Paddy Rice Detectionmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Satisfactory classification accuracy was obtained on a local scale with the aforementioned high spatial resolution satellite data. However, the temporal analysis of land cover was constrained by the lower temporal resolution, limited coverage extent, and high cost of the images (Peng et al, 2011). In recent years, moderate resolution imaging spectroradiometer (MODIS) on board the Terra and Aqua satellites was widely applied to monitoring rice areas and detecting the phenology stage on a regional scale due to its advantages of moderate spatial resolution and high revisited periods (Sakamoto et al, 2005;Xiao et al, 2005;Sun et al, 2009;Peng et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

Shi

Huang

Zhang

2013

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

Abstract:The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer (MODIS) data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index (EVI) and land surface water index with a central wavelength at 2 130 nm (LSWI 2130 ). In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination (R 2 ) value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.

show abstract

“…Authors of optical remote sensing-based rice studies have frequently emphasized the need of using sensors with very high revisit times in order to acquire a sufficient amount of cloud-free observations to create reliable time series [6][7][8][9][10][11][12][13][14][15]. Microwave-based remote sensing techniques, on the other hand, have the advantage of being non-susceptible to cloud cover.…”

mentioning

confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

View full text Add to dashboard Cite

Abstract:Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double-and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.

show abstract

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Cited by 137 publications

References 24 publications

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

Contact Info

Product

Resources

About