Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

  • NAAS Rating 5.60

  • SJR 0.293

Frequency :
Bi-monthly (February, April, June, August, October and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Indian Journal of Agricultural Research, volume 56 issue 3 (june 2022) : 268-275

Temporal and Spatial Delineation the Rice Growing Stages for Cropping Calendar Estimation in the Southern of Vietnam using Remote Sensing

Vo Quang Minh1,*, Tran Thi Hien2, Nguyen Thi Hong Diep1, Huynh Thi Thu Huong1, Phan Kieu Diem1
1Department of Land Resources, College of Environment Natural Resources, Cantho University, Cantho, 900000, Vietnam.
2Department of Resources and Environment, Bentre Province, 930000, Vietnam.
Cite article:- Minh Quang Vo, Hien Thi Tran, Diep Hong Thi Nguyen, Huong Thu Thi Huynh, Diem Kieu Phan (2022). Temporal and Spatial Delineation the Rice Growing Stages for Cropping Calendar Estimation in the Southern of Vietnam using Remote Sensing . Indian Journal of Agricultural Research. 56(3): 268-275. doi: 10.18805/IJARe.A-660.
Background: MODerate resolution imaging spectroradiometer (MODIS) is the crucial instrument aboard. It provides global maps of several land surface characteristics. 

Method: The study uses MODIS to delineate the rice sowing and progress of the rice cropping calendar in the Vietnamese Mekong Delta. The study used multi-time series of normalized difference vegetation index (NDVI) images from 250 m spatial resolution MOD13Q1 images with 16-day combination to determine rice sowing/planting and harvesting schedules (from 01/01/2008 to 30/09/2009). Using 82 MODIS images, the study calculates the NDVI time series for rice sowing/transplanting stages in the Mekong Delta. Over time, the relationship between NDVI values and the rice cropping stages determines each cropping season starting and ending time. 

Result: As a result, we delineate three (3) major rice cropping systems and eight (8) cropping seasons. In which Main Winter-Spring and Early Summer-Autumn and Late Main Winter-Spring and Main Summer-Autumn cropping seasons dominated. MODIS satellite images are efficient and helpful for determining the current state of rice evolution. It is suitable for the regional or national level, which can provide quick and low-cost information for managers and decision-makers to select the proper strategies for crop management.
Rice plays an essential role in ensuring food security and socio-economic stability in Vietnam. The country has nearly 89 million consumers and is a necessary source of income for more than 60 million agricultural producers in rural areas (ADB, 2012). However, rice production in the Mekong Delta (MRD) has increased continuously, averaging 0.02%/year in planted area and 0.04%/year in yield over the past 20 years (1995-2015). During this period, rice production focuses on meeting production goals rather than improving quality. In 2020, the Government expected to slightly reduce and shift from increasing quantity to enhancing the quality and adding value to the rice value chain (Nguyen, 2017). However, the rice pest outbreak has affected rice yield and production. Therefore, the Government needs to adopt the results to monitor and supervise each locality and early forecast rice pest situations at each level.
       
Usually, the Government adopts the monitoring for cropping season mainly by surveys, reports from local officers. However, it requires time-consuming funding and less accuracy. The techniques use remote sensing images to delineate natural features in many countries worldwide (Burrough, 1986). The results can monitor the sowing progress and cropping calendar on a large scale, thereby proposing solutions to manage the rice crop. The study mainly uses MODIS images as a source of data to develop the procedure to monitor the rice crop progress for cropping calendar estimation in the MKD.
The study area
 
The Vietnamese MKD (12% of the country’s total area) is necessary for national food security (Cosslett and Cosslett, 2014Anthony et al., 2015Smajgl et al., 2015). Besides, approximately one-fifth of the Vietnamese population represents 17.6 million people GSOV, 2015). The delta is a tropical monsoon climate, with a dry season from December to May and a wet season from June to November (Cosslett and Cosslett, 2014). The MKD covers 39,000 km2 (Fabrice and Kuenzer 2012).
       
The study was carried out at Cantho University, Vietnam, for images collection and processing during 2020. The University locates in the Centre of the Mekong delta (Fig 1).
 

Fig 1: The Mekong river delta in Vietnam.


 
The satellite images collection
 
The images collected from NASA, code MOD13Q1, the images spatial resolution of 250 m and temporal resolution with the 16 days from 1 January (2008) to 14 September (2009), 4800 × 4800 pixels/image rectified. Eighty-two (82) images were collected. We developed the NDVI time-series images for Rice showing stages and cropping calendar determination. Red and Near-Infrared bands processed for calculation NDVI (Table 1).
 

Table 1: The spectral bands of MODIS sensors used in the calculation of vegetation indices.


 
The development of the normalized different vegetation index (NDVI)
 
NDVI is an active vegetation index for the global range of activities, partly due to its “rates” nature. It allows NDVI to filter out most of the noise caused by clouds, shade, changes in sun angle, topography and atmospheric conditions (Huete and Justice 1999).  The study use spectrum bands of visible, near-infrared, infrared and red to create NDVI. Besides, it differentiates the intermediate evaluation for characteristics of materials such as biomass, leaf area index and photosynthetic capacity seasonally. Those characteristics are relevant and highly dependent on the type of plant cover and weather, physiological characteristics, biochemical and pests. Thus, NDVI average in time series data is the primary tool to monitor changes in vegetation status, which can assist in understanding the impact of weather and climate on the biosphere. The calculation of NDVI shows as follows:
 
 ..........(1)

The relationship between NDVI values and rice growing stages
 
Because NDVI value of crops changing in a specific situation and the same direction. Based on Parida et al., (2008) research results, the proposed NDVI values are shown in Table 2.
 

Table 2: Criteria used to classify land use (Parida et al., 2008).


 
Monitoring the rice sowing for cropping calendar delineation
 
The study calculates and delineates the time-series NDVI values to determine the relationship between NDVI indexes for rice growing stages. The low NDVI value at the early stage gradually increased and got peaked at the tillering stage. The NDVI value decreased when rice began to mature and be harvested (Fig 2).
 

Fig 2: NDVI change at different rice growing stages in Winter-Spring and Summer-Autumn crops.


 
Data validation and accuracy assessment
 
The kappa coefficient of Cohen (κ) is a statistic formula to calculate inter-rater reliability (Intra-rater reliability) for qualitative (categorical) items (McHugh, 2012). However, some studies have recommended that assessing disagreement between items becomes conceptually more straightforward (Pontius and Millones 2011).
       
Cohen’s Kappa measures agreement between two raters, who classify N items into mutually exclusive C. The definition of κ is:
 
 

Where
po:   The relative observed agreement among raters (identical to accuracy).
pe:   The theoretical probability of chance agreement.
       
Using the observed data to calculate the probabilities of each observer randomly seeing each category.
       
Among the rates, if there is no agreement other than expected (as given by pe), κ=0. Therefore, the statistic may be negative (Sim and Wright 2005), which means there is no effective agreement between the two reviewers.
       
The study collected 479 locations for validation during 2020 for accuracy assessment and comparison with the government statistical data of the interpreted year data.
Temporal NDVI image interpretation
 
Each NDVI images allow monitoring changes in vegetation status, represented by the difference of NDVI index at a particular time, corresponding to the level of low or high values. By analyzing NDVI images in 2008, a few images where homogenous tone (light or dark) remain changes over time. The NDVI maps for the Mekong delta created from MODIS images of 12 months are shown in Fig 3. The first six months of 2008 showed that the coastal areas and the Ca Mau peninsula have very low NDVI (brightness color) and did not significantly change. In contrast, in AnGiang, DongThap, CanTho, HauGiang provinces have high NDVI values in January, February and decreased in March, April and increased in May and June. The color of difference NDVI values tends to darken over time (lowering NDVI) in the coastal area, especially in the last six months of 2008. In contrast, the NDVI value decreased in September, October and November in AnGiang, DongThap and Kien Giang provinces. The results in 2009 also showed similar variation.
 

Fig 3: NDVI map for the Mekong delta created from MODIS images of 12 months of 2008.


 
Analysis of variation in NDVI values
 
Usually, suppose the NDVI index peaks (from 0.5 to 0.9) are the regions with well-developed. In that case, Rice is in tillering/maturing/flowering or industrial crops/fruit trees/forests. If NDVI<0.5, there is no poor plant growth,  specialized areas such as shrimp, salt, waterlogging, or sowing rice. The NDVI values of uncultivated are often stable throughout the year-the NDVI values of objects that do not have high fluctuations over time split into separate objects.  The changes in NDVI sinusoidal shape reached maximum values at 0.8-1.0, corresponds to the complete growing stage and decreased to about 0-0.4 when the crops were harvested. This value continues to increase as a rule when starting a new season. Depending number of rotations per year, there were mono, double and triple rice crops in the Mekong delta (Fig 4, 5, 6, respectively).
 

Fig 4: Example of changing of NDVI index for one crops.


 

Fig 5: Example of changing of NDVI index for two crops.


 

Fig 6: Example of changing of NDVI index for three crops.


 
The progress of rice sowing
 
Based on the temporal variation of NDVI, the study delineates the rice sowing progress in different provinces (Fig 7 and Table 3) for the rice cropping calendar estimation for mono, double and triple rice cropping calendars (Fig 10, 11, 12, respectively). The rice cropping calendar helps the Government decide the date of rice seedling, or transplanting, depending on the freshwater supplied from the river or saline water intrusion.
 

Fig 7: Time of rice sowing at different date intervals for one cropping season delineated from NDVI maps.


 

Table 3: Rice sowing area (ha) intepreted from MODIS images in May and June, 2009 in different districts.


 
Interpretation accuracy assessment
 
The interpreted image accuracy assessment adopts the Cohen’s kappa coefficient (κ) and 479 locations. The validation results show:
Overall accuracy = 90,351%; Kappa coefficient = 0.902
 
Comparison with statistical data
 
The results compared with official data for 2009, supported by Southern Center for Crop protection, which showed very high determination coefficiency (R2=0.812**; n=481, df=479) (Fig 8 and 9). So then, the results are pretty reliable and can be used in monitoring the status of rice cropping stages.
 

Fig 8: The correlation between the area of statistical and interpreted data.


 

Fig 9: Comparison between interpreted and Statistical data of rice seedling area from week 10 to week 27 (1 March to 30 June 2009).


 
Main rice cropping calendars in the Mekong Delta
 
We determine each cropping season’s starting and ending time-based on the relationship between NDVI values and the rice cropping stages over time. It can assist in developing the major rice cropping calendars for the study area. 
 
Mono rice cropping (Traditional rice)
 
The Mono rice crops distribute along the coast from LongAn, BenTre, TraVinh and Soctrang, BacLieu and CaMau provinces. The transplanting period ranges from August to the middle of September (Rice sowing in June, July) (Fig 10).
 

Fig 10: Typical mono rice cropping calendar.


 
Double rice crops
 
This rice cropping calendar is most popular in the MKD  (freshwater and slightly saline areas). The image interpretation showed that the NDVI index reached its maximum twice a year at different intervals depending on sowing time (Fig 11).
 

Fig 11: Some typical double rice cropping calendars.


 
Summer-Autumn and Autumn-Winter
 
The cropping calendar scatters distributed in the coastal areas. The Autumn-Winter starting date of rice sowing around the middle of September of the previous year (2007) ended at the end of January after (2008). Then, the soil will be followed for three months (due to salinity or flooding) and start sowing the summer-autumn rice crop in May. Then it creates for the next rotation.
 
Main winter-spring and early summer-autumn
 
The winter-spring cropping season started from the previous year in November. After harvesting, it starts sowing for the next cropping season from 21/3 to 6/4. Then this cropping season, the soil is left fallow from early August to the end of October and will begin the Winter-Spring cropping season for the next rotation of 2009. 
 
Early winter-spring and late summer-autumn
 
The rice sowing of the first cropping season is within October. The soil will be fallowed after this cropping season for two months before starting the summer-autumn cropping season (from 8/5 to 24/5). After harvest, the farmer let the soils fallowed for one month and then it starts the Winter-Spring cropping season of 2009.
 
Late Winter Spring - Main Summer-Autumn
 
The starting time of rice sowing for Winter Spring (2008) within December and the main Summer-Autumn starting from 21 Mar-6 April. In 2009, the Sumer-Autumn rice sowing relatively later than in 2008 (middle to end of April). 
 
Triple rice crops
 
The triple rice cropping season in the freshwater alluvial soil ecosystem is most popular and almost year-round. Therefore, farmers divide the rice sowing calendar into early Winter-Spring, primary Winter-Spring and late Winter-Spring rice cropping (Fig 12).
 

Fig 12: Some triple rice cropping calendars.


 
Winter Spring-Spring Summer-Late Summer Autumn
 
At the ending of the Summer-Autumn crop, the soil is left fallow for 1 or 2 months before starting the Winter-Spring rice crop of next year. Thus, the sowing time for three cropping seasons is described as (1) Winter Spring-starting from the end of November up to the beginning of December, (2) the second rice crop starting from the middle of March, (3) the third rice crop starting from the beginning of June.
 
Main Winter-Spring and Summer-Autumn and Autumn-Winter
 
The Winter-Spring crop sowing time starts at the beginning of December, the Summer-Autumn from sixth/April to 22nd/April, Autumn-Winter sowing from the end of July to the first half of August. When farmers harvested the Autumn Winter rice crop in 2008, they let the soil fallowed for a month before starting the Winter-Spring crop of 2009.
 
Late Winter-Spring and Summer-Autumn and Autumn-Winter
 
Winter-Spring season starts sowing from late January to early February. The Summer-Autumn crop begins to plant from the end of May. In comparison, the Autumn-Winter season starts from mid-September. 
       
Table 4, the extent of rice cropping season area shows that the double rice crops dominated (>63.8%) and followed by triple rice crop (30.7%) and finally mono rice crop (5.5%). In which the early Winter-Spring and Summer-Autumn cropping season populated (>27.9%), followed by the main Spring-Summer and Eearly Summer-Autumn cropping season (>25.5%). The third is main Winter-Spring and Summer-Autumn and Autumn-Winter rice cropping seasons (22.0%). The lowest is double rice crops of Summer-Autumn and Autumn-Winter cropping season (0.6%).
 

Table 4: The extent of rice cropping season area in the MKD 2008-2009 interpreted from MODIS images.


       
Within eight major rice cropping calendars represented in the Mekong Delta, the double and triple rice crops dominated, which account for about three-quarters of the total area. The remaining rice cropping calendar occupies negligible.
       
The extend of rice cropping seasons and major rice cropping in the Mekong delta are sown in Fig 13 and 14, respectively.
 

Fig 13: Distribution of rice cropping seasons and rice sowing in the MKD in 2008-2009.


 

Fig 14: Map of rice cropping seasons 2008-2009 in the MKD, Vietnam.

The MOD13Q1 (250-m spatial resolutions, 16-day composite) monitored and delineated rice sowing/transplanting and then the cropping calendars using the time-series of NDVI images. As a result, the study outlined three (3) major Rice cropping systems and eight (8) rice cropping seasons. The cropping seasons of Main Winter Spring-Early Summer Autumn and Late Main Winter-Spring-Main Summer Autumn mainly dominated.  
       
The application of temporal variation of NDVI from MODIS images is efficient and helpful for determining the current state of rice evolution for rice sowing stages and rice cropping calendar delineation. Multispectral and Temporal Remote Sensing Data, MODIS most suitable for research at the regional level. The results assist the managers in selecting appropriate and low-cost strategies for better crop management.
       
It needs further studies and more field validation to enhance the accuracy. The cloud occurrence and images low spatial resolution impaired the accuracy of the results.
This study is under-supported by the CanTho University and Ministry of Education and VLIR (CTU-Belgium) projects.

  1. Anthony, E., Brunier, J.G., Besset,  M., Goichot,  M., Dussouillez, P., Nguyen,  V.L. (2015). Linking rapid erosion of the Mekong River delta to human activities. Sci. Rep. 5: 1-12. doi:10.1038/srep14745.  

  2. ASEAN Development Bank (ADB). (2012). The Rice Situation in VietNam. Technical Assistance Consultant’s Report. Project Number: TA 7495-REG. Support for the Association of Southeast Asian Nations Plus Three Integrated Food Security Framework (Financed by the Japan Fund for Poverty Reduction).

  3. Burrough, P.A. (1986). Geographic Information Systems for Land Ressource Assessment. Prentice-Hall. Geocart International. 1(3). doi.org/10.1080/10106048609354060.

  4. Cosslett, T.L., Cosslett, P.D. (2014). The Mekong Delta. In: Water Resources and Food Security in the Vietnam Mekong Delta. Natural Resource Management and Policy, vol 44. Springer, Cham. doi.org/10.1007/978-3-319-02198-0_1.

  5. Fabrice G. Renaud, Kuenzer, C. (2012). The Mekong Delta System - Interdisciplinary Analyses of a River Delta. Springer Netherlands. Print ISBN: 978-94-007-3961-1. Electronic doi: 10.1007/978-94-007-3962-8.

  6. GSOV, (2015). General Statistic Office of Vietnam (GSOV), (2015). Statistics. Available from: https://gso.gov.vn/default.aspx?tabid=714.

  7. Huete, A.R., Justice C. (1999). (MODIS vegetation index (MOD13) algorithm theoretical basis document. Ver. 3. doi: 10.4236 /gep.2016.46008.

  8. McHugh, M.L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica. 22(3): 276-282. doi: 10.11613/BM. 2012.031. 

  9. Nguyen, T.H. (2017). An Overview of Agricultural Pollution in Vietnam: The Crops Sector. Prepared for the World Bank, Washington, DC

  10. Parida, B.R., Oinam, B., Patel, N.R., Sharma, N., Kandwal, R. and Hazarika, M.K. (2008). Land surface temperature variation in relation to vegetation type using MODIS satellite data in the Gujarat state of India. International Journal of Remote Sensing, 29: 4219-4235. doi.org/10.1080/01431160701871096.

  11. Pontius, R., Millones, M. (2011). Death to Kappa: the birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing. 32(15): 4407-4429. doi:10.1080/01431161.2011.552 923. S2CID 62883674. 

  12. Sim, J., Wright, C.C. (2005). The Kappa statistic in reliability studies: Use, interpretation and sample size requirements. Physical Therapy. 85(3): 257-268. doi:10.1093/ptj/85.3.257. ISSN 1538-6724. PMID 15733050.

  13. Smajgl. A, Toan, T.Q., Nhan, D.K., Ward, J., Trung, N.H., Tri, L.Q., Tri, V.P.D., Vu, P.T. (2015). Responding to rising sea levels in the Mekong Delta. Nat. Clim. Change. 1-8, doi:10.1038/nclimate2469.

  14. Southern Center for Crop Protection. (2009). Statistical data on the progress of rice sowing in the Mekong river delta 2008 to 2009. Ministry of Agricultural and Rural Development. Vietnam.

Editorial Board

View all (0)