Remote Sensing of Global Croplands for Food Security

Monitoring of global croplands (GCs) is imperative for ensuring sustainable water and food security for the people of the world in the Twenty-first Century. However, the currently available cropland products suffer from major limitations such as: (1) Absence of precise spatial location of the cropped areas; (b) Coarse resolution nature of the map products and their significant uncertainties in areas, locations, and detail; (b) Uncertainties in differentiating irrigated areas from rainfed areas; (c) Absence of crop types and cropping intensities; and (e) Absence of a dedicated web-based data portal for dissemination of the cropland map products. This research aims overcome the above mentioned limitations through development of a set of Global Food Security-support analysis data at 30 m (GFSAD30) resolution consisting of four distinct products: Cropland extent/area, Crop types with focus on the 8 types that occupy 70% of the global cropland areas, Irrigated versus rainfed croplands, and Cropping intensities: single, double, triple, and continuous cropping. These products are produced using multi-resolution time-series remotely sensed data and a suite of automated and\or semi-automated cropland mapping algorithms (ACMAs). Data include Moderate Resolution Imaging Spectroradiometer (MODIS) time-series, and Landsat Time-series from various epochs. Methods include spectral matching techniques (SMTs), automated cropland classification algorithms (ACCA’s), decision tree algorithms (DTAs), and linear discriminant analysis algorithms (LDAA). Massively large big data (MLBD) are computed over several platforms that include parallel computing over NASA NEX supercomputers, and Google Earth Engine (GEE). Large volumes of ground data are sourced through various crowdsourcing mechanisms and integrated on a web platform: croplands.org.https://commons.und.edu/ss-colloquium/1056/thumbnail.jp

The use of remote sensing data for drought assessment and monitoring in southwest Asia

Drought / Monitoring / Indicators / Assessment / Remote sensing / Asia

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

Decadal variations in NDVI and food production in India

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In most countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world’s largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982–2002, would have required an increase in annual rainfall of at least 30% over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines

Remote Sensing 10th Anniversary Best Paper Award

Started in 2009, our journal will celebrate its 10th anniversary in 2019 [...

Remote Sensing Open Access Journal of MDPI: Current Progress and Future Vision

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Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution

In an increasingly food insecure world, there is a critical need for us to have a comprehensive understanding of global croplands. The reality that the “green revolution” has ended is beginning to be felt around the World. Whereas, global population continues to increase at a rate of about 100 million per year and is expected to reach around 10 billion by 2050, cropland areas are not increasing and have stagnated around 1.5 billion hectares globally. Indeed, cropland areas have even begun to decrease in some countries with important food contribution (e.g., USA) due to increasing demand of fertile arable lands for alternative uses such as bio-fuels, encroachment from urbanization, and industrialization. [...