Frequently Asked Questions about the Measurement

There are three different bands for soil water content. What is a ‘band’ and which one should I use?

We use passive microwave observations to measure soil water content. A microwave signal is a light signal with a frequency that is much lower than the frequency of visible light. The lower the frequency of the passive microwaves, the more they tell us about what happens below the ground. A “band” refers to the frequency band we use to measure SWC. The lowest frequency band we deliver is the L-band (1.4 GHz), which is measured by the SMAP satellite. This measurement shows the moisture in the top 5 cm of the soil. Other frequencies we deliver are the C-band (6.9 GHz), which shows the moisture in the top 2cm, and X-band (10.7 GHz), showing the top cm of the soil. The X-band and C-band measurements come from the AMSR-2 satellite. For most use cases, L-band measurements are the best choice:

  • They are representative of a deeper layer than the other bands.

  • Vegetation on top of the soil has a much lower impact on the signal than for other bands.

  • L-band observations are more sensitive to soil-moisture changes than the other bands: per unit change in soil water content, the microwave soil emission changes are stronger for L-band than for higher frequencies like C-band. This higher sensitivity leads to more accurate and precise SWC observations.

There are however some situations where the other bands are a better choice:

  • AMSR-2 has a higher temporal resolution: especially in the tropics, X-band and C-band observations are available every other day, while for L-band observations there might be a few days in between observations.

  • Sometimes, radio signals emitted from the Earth interfere with the soil moisture signals. We call this effect “radio frequency intereference” (RFI). We filter for RFI, but sometimes the filter does not filter out everything, leading to strange SWC values. When this happens in the L-band for a specific location, use X-band or C-band.

  • AMSR-2 provides the observations for X-band and C-band SWC. AMSR-2 data is available 2012 onwards, and can be combined with data from its predecessor (AMSR-E) to compute long SWC time series, all the way back to 2002. When a customer wants to have a long time series, C-band and X-band data can go back to 2002. Our L-band product can provide SWC data from 2015 to present.

Why does the 1km product sometimes show a higher correlation with in-situ measurements than the enhanced-resolution product?

Sometimes, customers compare our SWC product with in-situ measurements and find that the 1km products perform better than the enhanced-resolution products. This can be explained by the method used to enhance the spatial resolution. The 1km products use passive microwave observations, and have a temporal resolution of about 1-2 days, depending on the microwave band used and the latitude.

As a result, the 1km products see a lot of the temporal variations in SWC which are also caught by the in-situ sensor. For the enhanced-resolution products, we combine the passive microwave observations with infrared observations from the Sentinel-2 satellite. The infrared observations have a repeat cycle of about 5 days, depending on the latitude, but they cannot be made on cloudy days. The infrared data tell us how the soil moisture is spatially divided within the coarser 1km product and thus can accurately tell us which fields are relatively wet or dry, compared to the environment. However, the lower temporal resolution of the infrared data can cause the correlation coefficient to drop compared to the 1km product. The enhanced-resolution product does get updated every time a new passive microwave observation comes in, but the temporal changes that result from the infrequent infrared observations can cause a degradation in the correlation.

Therefore, when a customer is interested mostly in the temporal changes, often the 1km products are the best choice. When they are interested in the spatial variations, the enhanced-resolution product can tell which areas are relatively dry or wet, compared to the environment.

What type of satellites are used in your algorithms?

We use both passive microwave satellites and satellites that observe in the optical domain. The passive microwave satellites measure, which measure microwave signals that are naturally radiating from the Earth’s surface. This enables observations to be acquired during cloudy conditions, because of the physical properties of waves transmitted in this spectrum’s range. Examples of the satellites/sensors we use are:

When is the data observed?

Currently, Planet uses the nighttime observations during the descending orbits. Each satellite has its own time of observing an area, which is roughly the same time every day. For the satellites we use, these overpasses are either around 01:30 solar time or 06:00 solar time. The data acquired at that time can be regarded as a snapshot of the soil conditions at that time. Therefore, the measurements we provide are representative for the time of overpass.

What are the depths of the measurements?

The sensors we use measure the signal originating from the top layer of the soil. Typically, this is up to 10 cm deep, though the strongest contribution is received from the most upper layer. Commonly assumed is a depth of roughly 5 centimeter, but in reality the depth of this measurement varies slightly with water content. If the soil is drier, the sensing depth is deeper into the soil.

What does the unit \(m^3 m^{-3}\) represent?

The unit \(m^3 m^{-3}\) of the Planet data products is volumetric soil water content, which indicates the volume fraction of water in a volume of soil. A value of 0.4 is equivalent to 400 liters of water in one \(m^3\) of soil.

Where can I find how your soil water content retrieval works?

Planet’s retrieval algorithm is based on the Land Parameter Retrieval Model (LPRM), which has been extensively described in the scientific literature. The baseline algorithm is described in Owe et al., 2008. De Jeu et al., 2014 provides a review of LPRM and Van der Schalie et al, 2018 and 2021 describe the latest updates Planet has taken this well-tested method and uses its patented algorithm to go to a much higher spatial resolution. See also Technology.

What is your accuracy and precision?

The precision of the Planet soil water content data is about 0.001 \(m^3/m^3\). Accuracy is lower at approximately 0.04 \(m^3/m^3\). This is similar to properly installed in-situ sensors (see e.g. this paper ). This said, there are no independent measurements at the scale we are observing (e.g. no benchmark measurements at 100x100m) so the absolute accuracy remains largely unknown.

What is the difference between the 100 m and the 1000 m resolution product?

Our 1000 m resolution product is based on our patented disaggregation method where we make optimum use of the overlapping satellite footprints to refine the resolution from 36 km tot 1 km. The 100 m product also uses SWIR imagery of Sentinel 2. This imagery is used to add more spatial constraints to our disaggregation method.

Is SWC a modeled product or an observation?

The brightness temperatures are direct observations by the satellite. These observations are used in a physical based radiative transfer model to retrieve soil moisture. However, considering the strong physical description of the radiative transfer model, scientists often refer passive microwave soil moisture as satellite observed.

What do you measure in cities?

The soil water content values that we measure in urban areas are simply the measured dielectic constant values of the city’s surface converted into soil water content. Although we do show soil water content values over urban areas, these values don’t actually represent soil water content. You can use the built-up area data flag to mask out cities and other urban areas if required. See How to retrieve the data flags?.

Is vegetation on top of the soil an issue for your Soil Water Content retrievals?

Soil water content refers to the amount of water in the soil and not in the vegetation above the surface. We use the land parameter retrieval model (LPRM, Owe et al., 2008, De Jeu et al., 2014) to estimate SWC from satellite microwave observations. LPRM explicitly separates soil water content and attenuation due to surface vegetation. Therefore, vegetation on top of the soil is not an issue.

However, when the vegatation is very dense, it may attenuate almost all microwave radiation emitted by the soil. In this case soil water content cannot be accurately determined. This generally only happens in very densely-vegetated areas such as the Amazon rainforest. We detect and flag pixels where dense vegetation leads to an unreliable SWC estimate, see How to retrieve the data flags?.

A metric to determine whether dense vegetation hinders an accurate SWC retrieval is the vegetation optical depth (VOD). The VOD, also estimated by LPRM, is indicative of the fraction of radiation that is attenuated by vegetation. When the VOD is high (i.e. close to 1) for a specific pixel and time step, the vegetation attenuates a large fraction of the emitted microwave radiation and thus the estimated SWC is less reliable.

Low-frequency microwaves are less attenuated by vegetation than high-frequency microwaves. Therefore, L-band observations are better suited for estimating soil water content under dense vegetation than X-band and C-band observations.

If you suspect that dense vegetation affects your area of interest, don’t hesitate to contact your customer succes manager (CSM) to further investigate the issue.

How come your pixel size is so much smaller compared to the other Soil Water Content products?

This is due to the fact that Planet is using its patented disaggregation method ( that uses the individual overlapping footprints to retrieve the soil water content from the L1B data of the microwave satellites. Moreover, we use optical data to provide more constraints to our downscaling method.

Why do you have more than one Soil Water Content product?

Indeed we offer several products. The reason for this is that we want to give our clients the best possible product for their specific application. Based on your use of our data we will advise you which product to use, you are not alone.