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Landsats coupled with data inputs accurately track crop yields.
Doraiswami et al 4 (P.C., U.S. Dept. Agriculture, Hatfield, National Soil Tilth Lab, Jackson, U.S. Dept. Agriculture, Akhmedov, U.S. Dept. Agriculture, Prueger, Nat'l Soil Tilth Lab, Stern, U.S. Dept. Agriculture, http://ddr.nal.usda.gov/bitstream/10113/36735/1/IND44300354.pdf, accessed 7/6/11) CJQ
An improvement of this method is to use a radiative transfer model such as Scattering by Arbitrary Inclined Leaves (SAIL) to predict canopy reflectance (Verhoef, 1984). Simulation requires biophysical inputs, e.g., LAI, leaf optical properties, canopy architecture, sun-sensor-target geometry, and soil reflectance. These inputs can be measured or estimated and LAI is simulated in a crop growth model. Moulin et al. (1995) successfully used this approach for wheat by coupling a crop growth model with SAIL model simulating the VIS and NIR reflectance equivalent to SPOT/HRV satellite (20 m) by varying the crop model parameters. Simulated temporal reflectance profiles were compared with SPOT observations to select suitable leaf angle distribution for wheat crop. A similar approach by Moulin et al. (2002) coupled two process models and the SAIL model to simulate the energy balance, soil moisture, plant growth and canopy reflectance. The canopy reflectance results from the simulations were comparable with SPOT/HRV data. Constraining the model parameters with satellite observations enabled retrieval of key parameters of soil moisture and above ground plant biomass. Doraiswamy et al. (2003) simulated LAI for spring wheat in North Dakota using single date NOAA AVHRR data. Bands 1 and 2 reflectance from single date imagery was used to simulate LAI that was input to a crop yield model. The development of an accurate crop classification from Landsat imagery was critical for retrieval of crop specific reflectance. The spatial resolution (250 m) and temporal (daily) coverage of MODIS data offers potential for retrieval of crop biophysical parameters and improved accuracy in crop yield assessment. Although Landsat TM data would be more suitable in areas where the field sizes are small, the temporal frequency and cloud cover limits the retrieval of crop biophysical parameters that are changing during the season. Biophysical parameters such as LAI retrieved from satellite-measured reflectances coupled with a crop yield model facilitate analyses of temporal and spatial variability of crop conditions and yield.
Famine – Solvency – Agro Heg
Landsats are crucial to agricultural hegemony—glitches have made the US totally reliant on Indian, Chinese and French satellites.
NASA 7 (Laura Rocchio, http://landsat.gsfc.nasa.gov/news/news-archive/soc_0010.html, 7/6/11) CJQ
Over the past three decades, the objective global crop production estimates made with Landsat data have contributed to U.S. food security, economic security, national security, and more recently, homeland security. Post-9-11, the FAS mandate was expanded to include foreign crop supply estimates needed for critical response to any catastrophic crop failures or bio-terrorist attacks (think of the recent E. coli spinach scare on a much larger scale). Unfortunately, the FAS has become increasingly reliant on foreign Earth-observing satellites since 2004. Through 2003, FAS relied on about 3000 Landsat scenes per year for global crop production estimates and support of domestic programs.¹ In late May 2003, a hardware glitch aboard the Landsat 7 satellite reduced the amount of usable data per scene by about 25%, and forced FAS to look to foreign satellites for the data they required. Today, FAS is almost completely reliant on data purchased from an Indian satellite (IRS). Additionally, FAS uses data from the French SPOT satellite and they are investigating the use of data from a Brazilian and Chinese satellite (CBERS). With the current global coverage limitations of Landsat, data from Landsat 5 and Landsat 7 are only used for historical comparisons, domestic gap filling, and data validation and verification. And, after several changes in implementation strategy , the launch of the next U.S. Landsat-like satellite is still several years away. While it is fortunate that foreign satellites have been able to fill the void left by the Landsat 7 instrument anomaly, the merit of depending on foreign data for matters of national, economic, and homeland security is debatable. “A loss in the Landsat coverage is equivalent to losing an irreplaceable, objective, timely, and reliable intelligence source,” Doorn admits. “Increasingly global markets affect commodity prices and our imports/exports,” he continues. In 2005, the U.S. exported over $63B of agricultural products (approximately 10% of U.S. exports). But in this age of globalization, U.S. economic dominance in agriculture is being challenged and the marketing edge that FAS crop estimates give to U.S. producers has never been more important. In 2003, South America surpassed the U.S. in soybean production for the first time in history. A year later, a dispute with Brazilians over Brazil’s soybean production estimates highlighted how FAS crop production numbers affect the U.S.’s ability to effectively argue estimates. “The nature of how FAS uses Landsat imagery is most visible when problems, disagreements, or anomalies occur,” Doorn says. Today, FAS must increasingly rely on foreign-based satellite information. It remains to be seen if FAS’s reliance on foreign-based satellites will affect their ability to respond to events.
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