Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS MODerate Resolution Imaging Spectroradiometer-TERRA/EVI Enhanced Vegetation Index time series are applied 1 to map agricultural areas and 2 to identify five crop classes. These classes represent agricultural practices involving three commercial crops soybean, maize and cotton planted in single or double cropping systems. Both classifications are based on five steps: 1 analysis of the MODIS/EVI time series, 2 application of a smoothing algorithm, 3 application of a feature selection/extraction process to reduce the data set dimensionality, 4 application of a classifier and 5 application of a post-classification treatment. The first classification detected 95% of the agricultural areas 5 617 250 ha during the 2006–2007 harvest and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification overall accuracy = 74% and kappa index = 0.675 allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.

Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

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Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one-to-many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Another advantage over previous map comparison methods is that application-specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user-defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel-by-pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. It would also be possible for several users to input their knowledge into the process, leading to a potentially more robust comparison of land cover products. The methodology of map comparison is illustrated using different land cover products including Global Land Cover 2000 (GLC-2000) and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia, which showed that the MODIS land cover map overestimates forest cover in comparison with the GLC-2000. The agricultural example, on the other hand, served to illustrate that for Sudan, MODIS tends to underestimate crop areas while GLC-2000 overestimates them.

Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China’s Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS–EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS–EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS–EVI time series image of maize, a standard MODIS–EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS–EVI image and mean MODIS–EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated ( R 2  = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS–EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale.

Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

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.

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

The use of MODIS data to derive acreage estimations for larger fields : A case study in the south-western Rostov region of Russia

Since 2001, the MARS Unit of the Joint Research Centre of the European Commission has developed a system for crop monitoring and forecasting in food insecure regions. This communication first provides an overall description of the system and then focuses on one monthly bulletin prepared and published by FOOD-SEC action of the MARS Unit in East Africa. The main example is taken from Ethiopia. Basic data, models and information are presented as well as some important parameters for crop monitoring.

Michel Massart

Papers

The use of MODIS data to derive acreage estimations for larger fields : A case study in the south-western Rostov region of Russia

The Use of Remote Sensing Data and Meteorological Information for Food Security Monitoring, Examples in East Africa

Comparing different land cover data sets for agricultural monitoring in Africa