This special issue is dedicated to the memory of Professor H. 0. Hartley who made a significant contribution to the development of sample survey methodology for crop estimation from remotely sensed satellite data, while serving as a statistical consultant to the National Aeronautics and Space Administration (NASA). The authors of all except one paper published in this issue had an opportunity to work with Professor Hartley for 6 years (1974-80) during the course of two programs, LACIE (Large Area Crop Inventory Experiment) and AgRISTARS (Agricultural Resources Inventory Surveys Through Aerospace Remote Sensing) jointly sponsored by NASA, the National Oceanic and Atmospheric Administration (NOAA), and the United States Department of Agriculture (USDA). LACIE was a feasibility study for a global crop survey based on satellite acquired data. During LACIE, wheat acreage, yield and production were estimated in the United States Great Plains for crop years 1974-77 using data acquired by Landsat satellites. It was shown that such surveys are feasible; however, the data process¬ing technology and estimation methodology needed to be improved to achieve operationally reliable crop estimates. The follow-on pro¬gram, AgRISTARS, consisted of several exploratory studies; for example, estimation of corn and soybeans in addition to wheat in some U. S. states as well as in Argentina and Brazil. Some new approaches and methods of sampling frame development and crop acreage estimation were proposed and tested. The survey techniques developed in LACIE have previously been published in the proceedings of Technical Sessions of the LACIE Symposium held in October 1978 at the Johnson Space Center, Houston, Texas. The papers in this issue primarily focus on the sampling de¬signs, and the data analysis and crop acreage estimation procedures developed during AgRISTARS. Ideas set forth by Professor Hartley have influenced the contents of most of the papers published in this issue. Some of the ideas advocated by Hartley were: (i) the use of multitemporal models to improve crop proportion estimation in an area, (ii) calibration of satellite-acquired data to ground observed data and building of a
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