A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data
- excess rainfall, and consequent floods and damage to crops;
- hail, destroying growing plants;
- extreme drought, leading to death of plants.
- avoidance of local datasets; local datasets, such as municipality-level records of crop seeding, or surveying results from local authorities, are obviously not reusable out of their own geographical scope, but especially they may be severely inhomogeneous across different countries or regions. Global datasets were to be used, albeit at the cost of lower precision, accuracy, or resolution.
- leveling of expected quality; highly-refined components of the risk equation are of little use where they have to be forcibly combined with much coarser ones in the same risk computation.
2. General Context: Global Exposure Data for Risk Assessment
- the general model used for overall risk assessment, and
- the accuracy level of the other datasets incorporated in our production.
- it was important to balance the different components of the risk model in order to avoid mixing datasets that score too differently in terms of accuracy and precision; in this case, finer data or finer models would indeed be underused;
- our goal was to hit the optimal trade-off between data availability and the specific needs of the vulnerability component in order to capture the main factors affecting risk.
4. Space-Based Crop Mapping
4.1. Scientific Background
4.2. Our Approach
- open availability;
- high temporal frequency (roughly every second day in the equatorial band, daily elsewhere).
- lower temporal frequency; 5-days revisit time may appear very short, but in areas where cloud coverage is frequent such as the predominantly tropical Caribbean areas, the daily revisit time of Terra/Aqua can be crucial in preventing data gaps;
- higher spatial resolution, while unnecessary to the foreseen application, results in gigantic files to be stored and processed. This is only worthwhile when the additional data makes a difference in terms of separability of relevant land cover classes.
5. Agro-Climatic Mapping
- Definition of the agro-climatic conditions required for each crop to achieve its potential production through the regression analysis of reference data from public sources. The parameters that define the agro-climatic conditions are: (1) annual precipitation, (2) monthly temperature (minimum and maximum), (3) elevation over sea level, and (4) edaphology.
- Estimation of crop potential areas that are those cropland areas where all the ranges of agro-climatic conditions are fulfilled. In this step, the agro-climatic parameters defined previously and the cropland classification are contained in different layers that are spatially crossed to unify all in a single element .
5.1. Input Data
5.2. Definition of Agro-Climatic Conditions
5.3. Estimation of Crop Potential Areas
- The International Geosphere Biosphere Programme (IGBP) scheme, in which seventeen land covers were identified, eleven of them being vegetation, three terrain classes and three more vegetation-free classes. Their stated objective was to provide a global land cover dataset that was more up-to-date, of known accuracy and with higher spatial resolution and greater internal consistency than any other existing dataset. The scheme is based on definitions of three canopy components: above-ground biomass, leaf longevity, and leaf type process. The land-cover categories identified by the IGBP are related to the needs of gas exchange studies; vegetation attributes for modeling Net Primary Production (NPP); burn emissions and gas exchange; wetlands cover and wetland water regimes; changes in vegetation/land-cover over time; biological attributes; physical attributes, and landscape characteristics [38,39,40,41].
- The University of Maryland land cover classification (UMD) dataset, with fourteen classes, two of which have no vegetation. The approach taken involved a hierarchy of pairwise class trees where a logic based on vegetation form was applied until all classes were depicted. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used multitemporal metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not .
- The LAI/FPAR scheme, with nine vegetation classes and two vegetation- free ones. This scheme uses a method for the estimation of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (FPAR) from atmospherically corrected Normalized Difference Vegetation Index (NDVI) observations. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one half of the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photo-synthetically active radiation (400–700nm) absorbed by the green elements of a vegetation canopy. The method requires stratification of global vegetation into cover types that are compatible with the radiative transfer model .
- The Net Primary Production scheme (NPP) with nine vegetation classes and two vegetation-free ones. NPP defines the rate at which all plants in an ecosystem produce net useful chemical energy. In other words, NPP equals the difference between the rate at which plants in an ecosystem produce useful chemical energy (or GPP, Gross Primary Production), and the rate at which they expend some of that energy for respiration. The Primary Production products are designed to provide an accurate regular measure of the growth of the terrestrial vegetation. Version-55 Terra/MODIS NPP products are validated to Stage-3; this means that its accuracy was assessed and uncertainties in the product were well-established via independent measurements made in a systematic and statistically robust way that represents global conditions. These data are deemed ready for use in science applications .
- The Functional Type Plant scheme (FTP) with nine vegetation classes, two vegetation-free and one ice-water class. While most land models developed for use with climate models represent vegetation as discrete biomes, this is, at least for mixed life-form biomes, inconsistent with the leaf-level and whole-plant physiological parameterizations needed to couple these bio-geophysical models with bio-geochemical and ecosystem dynamics models. In the calculation of this scheme, the authors present simulations with the National Center for Atmospheric Research land surface model (NCAR LSM) that examined the effect of representing vegetation as patches of plant functional types (PFTs) that coexist within a model grid cell. This approach is consistent with ecological theory and models and allows for unified treatment of vegetation in climate and ecosystem models .
6. Information Fusion Strategy
7. Results and Validation
7.1. Results of Estimated Areas
7.2. Verification with Checkpoints
7.3. Comparison of Estimated Areas vs. FAO’s Statistics
Conflicts of Interest
|DAAC||Distributed Active Archive Center|
|EOS||Corine Land Cover|
|EOS||Earth Observing System|
|EOSDIS||Earth Observing System Data and Information System|
|FAO||Food and Agriculture Organization|
|FTP||Functional Type Plant|
|GIS||Geographic Information System|
|GMES||Global Monitoring for Environment and Security|
|IGBP||International Geosphere-Biosphere Programme|
|LAADS||Level-1 and Atmosphere Archive and Distribution System|
|LAI||Leaf Area Index|
|MODIS||Moderate Resolution Imaging Spectroradiometer|
|NASA||National Aeronautics and Space Administration|
|NDVI||Normalized Differential Vegetation Index|
|SIFT||Scale-Invariant Feature Transform|
|SPOT||Système Pour l’Observation de la Terre (System for Earth Observation, in French)|
|SRTM||Shuttle Radar Topography Mission|
|UMD||University of Maryland Department of Geography|
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|Spaceborne EO System||Ground Resolution (m)||Swath Width (km)||Revisit Time (days)||Usage in Project|
|WORLD CLIM||monthly minimum,|
mean and maximum
wind speed and
water vapour pressure
to 10 min
|SRTM||elevation data||1 s (∼30 m)||Global,|
|DSMW||soil data||5 min|
|Sugar Cane||550||1800||17.5||29||1550||3500||Eutric Nitosols,|
|Temporal coverage (V051)||2001–2013|
|Earth-gridded tile area||∼1200 × 1200 km (∼10 × 10 at the equator)|
|Image dimensions||2400 × 2400 rows/columns|
|File size||∼88 MB|
|Data type||8-bit unsigned integer|
|Science Data Set (SDS) layers||16|
|Non-vegetated land||Cereal crops|
|10||Grasslands||Grasslands||Urban||Snow and ice|
|15||Snow and ice|
|Croplands||Croplands||Broadleaf crops||Broadleaf crops|
Deciduous Broadleaf trees
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Dell’Acqua, F.; Iannelli, G.C.; Torres, M.A.; Martina, M.L.V. A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data. Sensors 2018, 18, 591. https://doi.org/10.3390/s18020591
Dell’Acqua F, Iannelli GC, Torres MA, Martina MLV. A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data. Sensors. 2018; 18(2):591. https://doi.org/10.3390/s18020591Chicago/Turabian Style
Dell’Acqua, Fabio, Gianni Cristian Iannelli, Marco A. Torres, and Mario L.V. Martina. 2018. "A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data" Sensors 18, no. 2: 591. https://doi.org/10.3390/s18020591