Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems - The Earth's Hydrological Cycle - page 138

Table 1 Characteristics of hydrological observations potentially available within the next decade

(see '' Appendix '' for details of sensor acronyms)

Hydrological
quantity

Remote
sensing
technique

Timescale Spatial

scale

Accuracy considerations Examples of

sensors

Precipitation Thermal

infrared

Hourly 4 km Tropical convective clouds

only

GOES, MODIS,
AVHRR,
Landsat, ASTER

1 day 1 km

15 days 60 m

Passive
microwave

3 h 10 km Land calibration problems TRMM, SSMI,

AMSR-E, GPM

Active
microwave

Daily 10 m Land calibration problems TRMM, GPM

Surface soil
moisture

Passive
microwave

1-3 days 25-50 km Limited to sparse

vegetation, low
topographic relief

AMSR-E, SMOS,
Aquarius, SMAP

Active
microwave

3 days 3 km Significant noise from

vegetation and roughness

ERS, JERS,
Radarsat,
ASCAT

30 days 10 m

Surface skin
temperature

Thermal
infrared

1 h 4 km Soil/vegetation average,

cloud contamination

GOES, MODIS,
AVHRR,
Landsat, ASTER

1 day 1 km

15 days 60 m

Snow cover Visible/

thermal
infrared

1 h 4 km Cloud contamination,

vegetation masking,
bright soil problems

GOES, MODIS,
AVHRR,
Landsat, ASTER

1 day 500 m-

1km

15 days 30-60 m

Snow water
equivalent
(SWE)

Passive
microwave

1-3 days 10 km Limited depth penetration AMSR-E

Active
microwave

30 days 100 m Limited spatial coverage SnoSat, SCLP,

Cryosat-2,
CoreH2O

Water level/
velocity

Laser 10 days 100 m Cloud penetration problems ICESAT,

ICESAT2,
SWOT,
DESDynl

Radar 30 days 1 km Limited to large rivers TOPEX/

POSEIDON

Total water
storage
changes

Gravity
changes

30 days 1,000 km Bulk water storage change GRACE, GOCS,

GRACEII

Evaporation 1 h 4 km Significant assumptions GOES, MODIS,

AVHRR,
Landsat, ASTER

1 day 1 km

15 days 60 m

Table updated from Houser et al. ( 2010 )

De Lannoy et al. 2010 ; Sahoo et al. 2013 ) typically using a land surface model (discussed
in Sect. 3 ) and/or land data assimilation (discussed in Sect. 4 ).
With the design of new sensors, one aims to gain resolution, increase the sensitivity to
the variables of interest and reduce instrument errors (USGEO 2010 ). Examples of new
missions for soil moisture observations are the SMOS and SMAP (Soil Moisture Active
and Passive) missions, both using L-band sensors and designed with a target uncertainty