Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis

Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998–2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.

[1]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[2]  Changsheng Li,et al.  Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data , 2002 .

[3]  Toshihiro Sakamoto,et al.  Agro-ecological Interpretation of Rice Cropping Systems in Flood-prone Areas using MODIS Imagery , 2009 .

[4]  Keith R. McCloy,et al.  Resource management information systems : remote sensing, GIS and modelling , 1995 .

[5]  John Tenhunen,et al.  Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT) , 2004 .

[6]  C. Justice,et al.  Analysis of the dynamics of African vegetation using the normalized difference vegetation index , 1986 .

[7]  I. Savin,et al.  The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[8]  A. Boudraa Dynamic estimation of number of clusters in data sets , 1999 .

[9]  Analysing the vegetation cover variation of China from AVHRR‐NDVI data , 2008 .

[10]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[11]  Herman Van Keulen,et al.  Disaggregating and mapping crop statistics using hypertemporal remote sensing , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Hiroyuki Ohno,et al.  Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers , 2006 .

[13]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[14]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[15]  Toshihiro Sakamoto,et al.  Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery , 2009 .

[16]  Alexandre Bouvet,et al.  Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Joseph M. Piwowar,et al.  Hypertemporal analysis of remotely sensed sea-ice data for climate change studies , 1995 .

[18]  Thuy Le Toan,et al.  The Use of SAR Data for Rice Crop Monitoring A Case Study of Mekong River Delta – Vietnam , 2005 .

[19]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[20]  D. Peddle,et al.  TEMPORAL MIXTURE ANALYSIS OF ARCTIC SEA ICE IMAGERY: A NEW APPROACH FOR MONITORING ENVIRONMENTAL CHANGE , 1998 .

[21]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[22]  C. Justice,et al.  Characterization and classification of South American land cover types using satellite data , 1987 .

[23]  R. Quiroz,et al.  Understanding precipitation patterns and land use interaction in Tibet using harmonic analysis of SPOT VGT‐S10 NDVI time series , 2005 .

[24]  Herman Eerens,et al.  Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Alexandre Bouvet,et al.  Rice monitoring using ENVISAT-ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam , 2007 .

[26]  C. Woodcock,et al.  The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from landsat TM☆ , 1996 .

[27]  C. Justice,et al.  The 1 km resolution global data set: needs of the International Geosphere Biosphere Programme† , 1994 .

[28]  Louise van Leeuwen,et al.  Mapping crop key phenological stages in the North China Plain using NOAA time series images , 2002 .

[29]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[30]  V. Wuwongse,et al.  Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data , 2005 .

[31]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .