BOX 10.1

The Landsat Satellite Program

While weather satellites have been around since the 1960s, there was no systematic remote monitoring of Earth’s terrain until the Landsat program (Figure 10.1). Landsat 1 was launched in July 1972 and acquired more than 300,000 images of Earth’s land surface using the Multispectral Scanner (MSS) instrument, which recorded data in four spectral bands with 79-m spatial resolution. Seven Landsat missions have been launched since then, with Landsat 7 continuing today. Landsat 1, 2, and 3 missions used the MSS instrument and demonstrated the usefulness of the acquired data for cartography, land surveys, agricultural forecasting, water resource management, forest management, and mapping sea-ice movement. Launched in 1982, Landsat 4 carried the Thematic Mapper (TM) instrument, which is still in wide use today for mapping land-cover change over large areas. The 30-m pixel size combined with seven spectral bands in the visible, near infrared, and midinfrared are well suited for mapping disturbance patterns. The value of Landsat data in land-cover mapping is highlighted by the fact that the current “data gap” in Landsat 7 data due to an instrument malfunction has been a major setback for the scientific community. Landsat 7 is currently not collecting research-grade data, and a follow-up Landsat Data Continuity Mission is therefore being planned.

FIGURE 10.1 Timeline of the Landsat satellite series. SOURCE: NASA.

The high cost and effort involved in processing Landsat data over large regions, however, led to the use of coarse- and moderate-resolution sensors (e.g., the Advanced Very High Resolution Radiometer [AVHRR], the Moderate Resolution Imaging Spectroradiometer [MODIS]) during the 1990s and early 2000s. Interestingly, the use of high-resolution commercial data (~1 m; e.g., IKONOS, QUICKBIRD) has become more common recently. Finally, while optical data are best suited for land-cover mapping, active sensors such as radar (e.g., the Japanese Earth Resources Satellite [JERS-1]) are valuable in cloudy regions and also can help derive structural characteristics of vegetation. In summary, technology seems to drive much of the research and applications, but there is always a trade-off in terms of cost and effort involved in processing the data.


Monitoring food production and forecasting droughts and famines are critical for human societies. Some of the earliest applications of Landsat data included agricultural monitoring and forecasting (Landgrebe 1997). One of the most successful early experiments was LACIE (Large Area Crop Inventory Experiment), begun in November 1974. The capabilities of remote sensing in large-area crop monitoring were demonstrated by LACIE’s estimate of wheat production in the Soviet Union during the 1977 growing season to within 6 percent of the reported Soviet figures (MacDonald and Hall 1980). In 1980 this program was broadened to form AgriSTARS (Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing), which included crop commodity forecasting of all major grains. Similar programs in crop monitoring continue today, such as PECAD

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