TABLE 4.1 Planned NPOESS Land-Cover Environmental Data Records

Environmental Data Record

System Capability

Threshold

Objective

Normalized Difference Vegetation Index (NDVI)

Horizontal resolution

Mapping accuracy

Measurement range

Measurement precision

Measurement accuracy

Refresh

Long-term stability

4 km

2 km

−1 to +1

0.04 NDVI

±0.05 NDVI

24 h

0.04 NDVI

1 km

0.5 km

0.01

±0.01

2× per day (9:30 a.m., 1:30 p.m.)

Snow Cover and Depth

Sensing depth

Horizontal resolution

Vertical sampling interval

Mapping accuracy

Measurement accuracy

Refresh

Long-term stability

0-50 cm

25 km

>10 cm

4 km

±10% clear /±20% cloudy

12 h

10% regional / 2% continental

0-1 m

1 km

>5, 10, 20, 30, 50, 100 cm

0.5 km

±30% snow depth

2× per day (5:30 a.m., 1:00 p.m.)

5% regional / 1% continental

Land Surface Temperature

Horizontal resolution

Mapping accuracy

Measurement range

Measurement precision

Measurement accuracy

Refresh

30 km cloudy /4 km clear

2 km

−90 to 70 °C

0.1 °C

Clear ±2.8 °C

Clear: 6 h

12.5 cloudy /1 km clear

0.5 km

0.025 °C

±1 °C

4 h

Vegetation Index/ Surface Type

Horizontal resolution

Mapping accuracy

Measurement range

Measurement accuracy

Refresh

4 km global / 4 km regional

2 km

21 types

70% correct

1× per year

1 km global / 0.25 km regional

1 km

0-100% vegetation + 21 types

90%

4× per year

Soil Moisturea

Sensing depth

Horizontal resolution

Vertical sampling

Mapping accuracy

Measurement accuracy

Refresh

Thermal IR, 1 cm

1 km

1 cm

0.5 km

±10% of total volume

2× per day (daytime)

Microwave, 0-5 cm

10 km

5 cm

5 km

±10% of total volume

Every other day

aFor soil moisture, “Thermal IR” replaces “Threshold,” and “Microwave” replaces “Objective,” as explained in NOAA (1997) p. 46.

SOURCE: Extracted from NOAA (1997).

In general, land use is harder to quantify from space than land cover, though certain types and intensities of land use can be determined directly or indirectly. Time-series satellite data are used to provide a temporal record of changes or trends in these characteristics and the underpinning for remotely sensed land-cover research. The remotely sensed data can be used independently, combined with ancillary or in situ data, or used to parameterize or validate process models. The different model types include soil vegetation atmosphere transfer (SVAT) models, ecosystem process models, vegetation canopy structure models, land-use models, and integrated assessment models.



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