A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam

Son, N. T.; Chen, Chi Farn; Chen, Cheng Ru; Duc, Huynh Ngoc; Chang, Li-Yen

doi:10.3390/rs6010135

Cited by 121 publications

(107 citation statements)

References 37 publications

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“…We found good agreement in the spatial distribution of single, double and triple rice-cropping schemes compared to previous studies in the Mekong Delta that reported cropping scheme maps with accuracies similar to our product [6,8,49,66,77]. The major driving force behind a farmer's choice of cropping scheme is the availability and management of water [66].…”

Section: Discussionsupporting

confidence: 64%

“…We suspect that these dynamic changes are also one of the error sources contributing to the low producer accuracy of the single-cropped class compared to the 2005´2010 intersection. Single-cropped, rainfed rice is increasingly diminished in favor of more profitable shrimp and fish farming [8,66,78] and could thusly be misclassified when such a change occurred during our study period. In addition, this cropping scheme is less widely distributed and spatially homogeneous than double or triple crop rice and might therefore be underrepresented in the 2005 land-use map which is based on moderate resolution data.…”

Section: Discussionmentioning

confidence: 99%

“…This abundance of water combined with the ability to manage the flooding enables the cultivation of two or three rice crops per year. Son et al [8] found that triple cropping increased by about 12% at the expense of double cropping under irrigated conditions for the 2007 to 2011 period. The method we employed has limited ability to show these changes since it detects the dominant cropping scheme over the studied time period.…”

Section: Discussionmentioning

confidence: 99%

“…Rice area extent in the Mekong Delta can be regarded as mostly stable based on our long-term in situ knowledge and previous studies also showing regular changes in cropping scheme affect only small areas at the fringes of irrigated triple-crop and rainfed single-crop areas [8]. To create a time series dense enough for rice mapping, we combined Envisat ASAR WSM data from multiple years into one dataset.…”

Section: Time-series Creation and Filteringmentioning

confidence: 99%

“…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%

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

et al. 2015

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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.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Time-series Creation and Filteringmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2015

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Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification

Wu,

Tetzlaff,

Daempfling

et al. 2023

Hydrological Processes

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Vegetation classification is an essential prerequisite for understanding vegetation‐water relations at a range of spatial scales. However, in site‐specific applications, such classifications were mostly based on a single Unmanned Aerial Vehicle (UAV) flight, which can be challenging in grasslands and/or herbaceous‐dominated systems, as those communities are small in size and highly mixed. Here, we conducted monthly UAV flights for two years in a riparian wetland in Germany, with acquired imagery used for vegetation classification on a monthly basis under different strategies (with or without auxiliary information from other flights) to increase understanding in ecohydrology. The results show that multi‐flight‐based classification outperformed single‐flight‐based classification due to the higher classification accuracy. Moreover, improved sensitivity of temporal changes in community distribution highlights the benefits of multi‐flight‐based classification ‐ providing a more comprehensive picture of community evolution. From reference to the monthly community distribution, we argue that a combination of two or three flights in early‐ and late‐summer is enough to achieve comparable results to monthly flights, while mid‐summer would be a better timing in case only one flight is scheduled. With such detailed vegetation mapping, we further interpreted the complex spatio‐temporal heterogeneity in NDVI and explored the dominant areas and developmental progress of each community. Impacts from management (mowing events) were also evaluated based on the different responses between communities in two years. Finally, we explored how such vegetation mapping could help understand landscape ecohydrology, and found that the spatio‐temporal distribution of minimal soil moisture was related to NDVI peaks of local community, while grass distribution was explained by both topography and low moisture conditions. Such bi‐directional relationships proved that apart from contributing to an evidence base for wetland management, multi‐flight UAV vegetation mapping could also provide fundamental insights into the ecohydrology of wetlands.

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Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity

Huang

Zhang

2022

Remote Sens Ecol Conserv

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Large river deltas are usually ecologically important wetland habitats, but also fertile agricultural exploitation areas, creating a conflict of long‐running substantial interest between agricultural expansion and wetland conservation. Over the past several years, large‐scale cultivation of water‐consuming rice has been growing rapidly in the semi‐arid climate of the Yellow River Delta (YRD). Timely monitoring of rice cultivation dynamics is of great significance for sustainable ecological conservation of the delta, which has insufficient freshwater resources. This study proposed a stratified metrics‐based method that integrates statistical spectral indices and phenological metrics at different growing stages to improve the accuracy of rice paddy classification in areas where rice and wetlands coexist. We applied the method to time‐series Sentinel‐1/2 images to produce annual rice paddy maps of the YRD from 2016 to 2021. Together with rice paddy data from 2011 to 2015 from Statistical Yearbooks of Dongying Bureau of Statistics, we investigated the expansion dynamics over the past decade and in this paper discuss the advantages and disadvantages of rice cultivation expansion over wetland ecosystem conservation. Rapid expansion of rice cultivation intensifies water conflicts, and adversely affects wetland restoration in the YRD. Considering the important ecological services of rice paddies as alternative habitats, we argue for maintaining a reasonable scale of rice paddies and optimizing their distribution as a potential solution to achieving the overall sustainable conservation of the YRD in the context of water scarcity.

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A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam

Cited by 121 publications

References 37 publications

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

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

Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification

Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity

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