Isabella Mariotto | USGS Science

USGS2013

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ 1 (400–2500 nm) versus λ 2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm). Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%). Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

Syr Darya River Basin↗

USGS2012

Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?

Although global food production has been rising, the world sti ll faces a major food security challenge. Over one billion people are currently undernourished (Wheeler and Kay, 2010). By the 2050s, the human population is projected to grow to 9.1 billion. Over three-quarters of these people will be living in developing countries, in regions that already lack the capacity to feed their populations . Under current agricultural practices, the increased demand for food would require in excess of one billion hectares of new cropland, nearly equivalent to the land area of the United States, and would lead to significant increases in greenhouse gases (Tillman et al. , 2011). Since climate is the primary determinant of agricultural productivity, changes to it will influence not only crop yields, but also hydrologic balances and supplies of inputs to managed farming systems, and may lead to a shift in the geographic location of some crops . Therefore, not only must crop productivity (yield per unit of land; kg/m 2 ) increase, but water productivity (yield per unit of water or "crop per drop"; kg/m 3 ) must increase as well in order to feed a burgeoning population against a backdrop of changing dietary consumption patterns, a changing climate and the growing scarcity of water and land (Beddington, 2010). The impact from these changes wi ll affect the viability of both dryland subsistence and irrigated commodity food production (Knox, et al. , 2010a). Since climate is a primary determinant of agricultural productivity, any changes will influence not only crop yields, but also the hydrologic balances, and supplies of inputs to managed farming systems as well as potentially shifting the geographic location for specific crops . Unless concerted and collective action is taken, society risks worldwide food shortages, scarcity of water resources and insufficient energy. This has the potential to unleash public unrest, cross-border conflicts and migration as people flee the worst-affected regions to seck refuge in "safe havens", a situation that Beddington described as the "perfect storm" (2010).

Photogrammetric Engineering and Remote Sensing↗