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Posts tagged ‘remote sensing’

Feb 20

How to Get More From Your NDVI Sensor (Part 3)

In the conclusion of our three-part series on improving NDVI sensor data (see part 2), we discuss how to correct for limitations which occur in high leaf area index (LAI) conditions.

NDVI Sensor

Where there’s a large amount of vegetation, NDVI tends to saturate.

NDVI Limitations – High LAI

NDVI is useful in the midrange of LAI’s as long as you don’t have strong soil effects, but as you approach an LAI above 4, you lose sensitivity. In figure 6, loss of sensitivity is primarily due to a saturation in the red band. Measurements were taken in a wheat canopy and a maize canopy. The near-infrared reflectance is sensitive across the entire spectrum of the wheat and maize canopies, but the red saturates relatively quickly. Where the red starts to saturate is where the NDVI starts to saturate.

NDVI Sensor

Figure 6: Gitelson (2004) J. Plant Phys

Note: NDVI saturates at high LAI’s, however, if your purpose is to get at the fractional interception of light, NDVI tends not to have the saturation issue. In Figure 7, Fpar or the fractional interception of light of photosynthetically radiation is nearly complete far before NDVI saturates. This is because canopies are efficient at intercepting light, and once we get to an LAI of about 4, most of the light has been intercepted or absorbed by the canopy.  Thus, incremental increases in LAI don’t significantly affect the FPar variable.

NDVI Sensor

Figure 7: Fractional interception of light is near complete at an LAI around 4. (Gamon et al. (1995) Eco. Apps)

Solution 3- WDRVI

One solution for the NDVI saturation issue is called the Wide Dynamic Range Vegetation Index (WDRVI). Its formulation is similar to NDVI, except for a weighting coefficient that can be used to reduce the disparity between the contribution of the near infrared and red reflectance.  

NDVI Sensor

In the WDRVI, a is multiplied by the near-infrared reflectance to reduce its value and bring it closer to the red reflectance value. In doing so, it balances out the red and the near-infrared contribution to the vegetation index.

NDVI Sensor

Figure 8: (Gitelson (2004) J. Plant Phys)

a can range anywhere from 0 to 1. Figure 8 shows that as we use a smaller value of a, we get an increasing linear response of the wide dynamic vegetation index to LAI.

The only drawback of the WDRVI is that the selection of a is subjective. It’s something that you experiment on your own until you find a value for a that is optimal for your solution.  People tend to err on the side of a very low value simply because they’ll get closer and closer to a linear response to LAI as a decreases.

Solution 4 – Enhanced Vegetation Index

The enhanced vegetation index (EVI) was designed to enhance sensitivity in high biomass ecosystems, but it also attempts to reduce atmospheric influences.  This was a vegetation index created for the purposes of a satellite-based platform. There’s a lot of atmosphere to look through from a satellite to the ground, and sometimes the aerosols in the atmosphere affect the reflectances in the red and the near infrared regions causing spurious observations.  The EVI also tries to reduce sensitivity of the index to soil. Thus the EVI is a kind of solution to both extremes.

NDVI Sensor

In the EVI equation, the two major inputs are near infrared and red reflectances.  C1 , C2, and L are all parameters that can be estimated, but the blue band is something that has to be measured. Most NDVI sensors are two band sensors, so you don’t have that information in the blue.  Plus, with satellites, the blue band is relatively noisy and doesn’t always have the best quality data, thus EVI has limited value.

Solution 6: EVI2 (Enhanced Vegetation Index 2)

Those problems led a scientist named Jiang to come up with a solution.  Jiang observed quite a bit of autocorrelation between the red band and the blue band, so he decided to try and formulate EVI without the blue band in what he called the EVI2 (Enhanced Vegetation Index 2).  if you’re interested in the mathematics, we encourage you to read his paper, but here we give you the equation in case you’re interested in using it.

NDVI Sensor

Figure 9

When Jiang calculated his EVI2 and compared it to the traditional EVI (Figure 9), it was nearly a one to one relationship. For all intents and purposes EVI2 was equivalent to EVI.  Since this avoids blue band, it offers some exciting possibilities as it reduces to just using the two inputs of NIR and red bands to calculate NDVI.

NDVI Sensor Summary

NDVI measurements have considerable value, and though there are extremes where NDVI performs poorly, even in these cases there are several solutions.  These solutions all use the near infrared and the red bands, so you can take an NDVI sensor, obtain the raw values of NIR and red reflectances and reformulate them in one of these indices (there are several other indices available that we haven’t covered). So if you’re in a system with extremely high or low LAI, try to determine how near infrared and red bands can be used in some type of vegetation index to allow you to research your specific application.

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Feb 13

Get More From Your NDVI Sensor (Part 2)

Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1).  This week, learn about NDVI applications, limitations, and how to correct for those limitations.

Field with crop seedlings starting to sprout

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.

Green crops in a field

NDVI Applications

People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy.  Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.

In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.

Diagram depicting NDVI Sensor data

Figure 4

At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. In fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.

NDVI Limitations

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements.  On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at a leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high.  However, several solutions exist.

Solution 1-Soil Adjusted Vegetation Index

Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil.  Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation.  We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.

Diagram of Maricopa Aircraft Data

Figure 5: Qi et al. (1994) Rem. Sens. Env.

The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI—the near infrared and the red.

Image depicts two equations one is NDVI and the other is SAVI

Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)

The only thing that’s different, is the L parameter.  L is a soil adjustment factor with values that range anywhere from 0 to 1.  When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5. You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.

Solution 2- Modified SAVI

The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate—not an accurate way of handling things.  So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.  

Image depicts two equations SAVI is the top equation while the bottom equation is modified SAVI or MSAVI

Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)

This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.

Depicts a compairson of MSAVI and SAVI in terms of dynamic range and noise level

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week:  Learn about solutions for high LAI.

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to leaf area index (LAI)”—>

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Oct 17

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Unraveling the Effects of Dams in Costa Rica

Thirty years ago, in Costa Rica’s Palo Verde National Park, the wetlands flooded regularly and eco-tourists could view thousands of waterfowl. Today, invasive cattail plants cover portions of the wetland which has subsequently dried up and become colonized by hardwoods. Consequently, the number of birds has fallen dramatically.

Flocks of birds flying against a sunset

The number of birds on Palo Verde National Park has fallen dramatically. (Image: anywherecostarica.com)

Some people blame the dams built in the 1970s which introduced hydrological power and created a large irrigation district in the remote region. Dr. Rafael Muñoz-Carpena, Professor and University of Florida Water Institute Faculty Fellow and his research team are performing environmental studies on the wetlands, trying to unravel the effects of the dams and how to revert some of the damage. Rafael explains, “We have a situation where modern engineering brought about social improvements, helpful renewable resources, and irrigation for abundant food production. But the resulting environmental degradation threatens a natural region in a country that depends on eco-tourism.”

Birds in a river at Palo Verde National Park

“A vast network of mangrove-rich swamp, lagoons, marshes, grassland, limestone outcrops, and forests comprise the 32,266 acre Palo Verde National Park.” (Image and text: anywherecostarica.com)

Are The Dams Responsible?

Dr. Muñoz-Carpena says because of lack of historical data it’s difficult to untangle and separate all the factors that have caused the environmental degradation. He adds, “Thirty years ago Palo Verde National Park was part of a large wetland system which was important to all of Central America because it contained many endangered species and was a wintering ground for migratory birds from North America. The Palo Verde field station on the edge of the wetland, operated by the Organization of Tropical Studies (OTS), attracted birdwatchers and wetland scientists from all over the world.”

In the 1970’s, with international funding, a dam was built in the mountains to collect water from the humid side of Costa Rica in order to generate hydroelectric power. It was clean, abundant, and strategically important.  With the water transferred to the dry side of the country, a large irrigation district was created to not only produce important crops to the region like rice and beans, but to distribute the land among small parcel settlers.

Flock of birds in the grasslands at Palo Verde National Park

“Birding is the principal draw of visitors to the park.” (Image and text: anywherecostarica.com)

Over the years, however, the wetland area slowly degraded to the point where its Ramsar Convention wetland classification is under question. Rafael says that understanding the causes of the degradation, the impacts of the human system, and how the natural and human systems are linked, is the big question of his research, and there are many factors to consider. “The release of the water, ground and surface water (over)use, agriculture, human development, and a larger population are all factors that could contribute to this degradation. Everything compounds in the downstream coastal wetlands. In collaboration with OTS and other partner organizations and universities, we are trying to disentangle these different drivers.”

Grasslands and swamps with mountains in the background

Understanding the causes of the degradation, the impacts of the human system, and how the natural and human systems are linked, is the big question of this research. (Image: anywherecostarica.com)

A Lack of Historical Data

One of the challenges the researchers face is to gather a sufficient amount of temporal and spatial information about what happened in the past forty years.  There are no public repositories of data to tap, and the information is spotty and hard to access. Rafael says, “Thanks to the collaboration of many local partners, we have been able to gather enough information to stitch together a large database out of a collection of non-systematic studies. The biggest challenge is to harmonize data that has been collected by different people in non-consistent ways.” This large database now contains the best long-term record possible for key hydrologic variables: river flow, groundwater stage, precipitation, and evapotranspiration.

The team is also using remote sensing sources to try to obtain time-series data for land-use and vegetation change, and will have those data ground-truthed through instruments that are collecting similar time-series data. Rafael says, “The idea is to build a network that will allow us to overlap some of the previous data sources with our own, validate and upscale the ground data with remote sensing sources, enabling us to put together a detailed picture of what happened.”

Next Week:  Find out how the researchers established connectivity in such a remote area,  some of the problems associated with the research, and how the team has addressed those issues.

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Apr 18

Climate Change, Genetics, and the Future World

Climate change scientists face a particular challenge— how to simulate climate change without contributing to it. Paul Heinrich, a Research Informatics Officer associated with the Southwest Experimental Garden Array (SEGA) remembers looking at the numbers for a DOE project that would have used fossil fuel to measure forests’ response to temperature change. “It would have been very, very expensive in fossils fuels to heat a hectare of forest,” he says.

The alternative is, “to use elevation change as a surrogate for climate change so we could do climate change manipulations without the large energy costs.”

SEGA Vegetation Zones diagrams

An overview of the SEGA sites using elevation change as a surrogate for climate change. For more information on these sites, visit http://www.sega.nau.edu/. Photo credit Paul Heinrich

By monitoring organisms across a temperature gradient it is possible to identify genetic variation and traits within a species that could contribute to a species survival under projected future climates.

Control and Monitoring Infrastructure

SEGA is an infrastructure project started in 2012 after researchers at Northern Arizona University’s Merriam-Powell Center for Environmental Research were awarded a $2.8 million dollar NSF grant with a $1 million match from NAU. Consisting of ten fenced garden sites for genetics-based climate change research, SEGA is set on an elevation gradient from 4000 to 9000 feet in the Southwestern United States. Each SEGA site has an elaborate data collection and control system with meteorological stations and site-specific weather information. Custom-engineered Wireless Sensing Actuating and Relay Nodes (WiSARDs) send data packets to a hub which then send the data back to a centralized server.

Because there is inherent moisture content variability from site to site, volumetric water content and soil water potential sensors have been installed to monitor and maintain moisture levels. If there is a change in soil moisture at one site, soil sensors will detect the difference. Software on the server notes the difference and sends a signal to the other sites, turning on irrigation until the soil moisture matches across sites.

SEGA Cyberinfrastructure Major Components diagram

An illustration of SEGA’s cyberinfrastructure and data management system. Photo credit Paul Heinrich.

Having such an elaborate infrastructure creates an opportunity for researchers looking to conduct climate change research. By offering access to the pre-permitted SEGA sites, the hope is that research will generate much-needed data for climate projections and land management decisions.

When asked if the data stream was overwhelming to manage Heinrich said, “Well, not yet. We are just getting started. The system is designed for what SEGA is expected to look like in ten years, where we expect to have 50 billion data points.”

Research Considerations

Climate change projections show temperatures increasing rapidly over the next 50 to 100 years, bringing drought with it. The impact of these changes will be dramatic. Temperature and drought tolerant species will survive, those that are not will die, drastically changing the landscape in areas that are currently water stressed. Pests like the pine beetle and invasive species like cheatgrass will do well in a drier environment where water-stressed natural species will not be able to compete.

Red canyon called Soap Creek AZ from an Aerial view

Soap Creek, AZ from above. With climate change projections it is likely that more land will become marginal. Photo credit Paul Heinrich.

“Foundational species,” or species that have a disproportionate impact on the ecosystem, are the primary focus of the research efforts at SEGA sites. These are the species that drive productivity, herbivore habitat, and carbon fixation in the ecosystem. Unlike forests in other parts of the United States, forests in the Southwest can be dominated by one or two species, which makes potential research subjects easier to identify.

Genetic Variance

Amy Whipple, an Assistant Professor in Biology and the Director of the Merriam-Powell Research Station who oversees the day-to-day activities at SEGA, has been conducting some of her own research at the garden sites. Whipple has studied Piñon Pine, Southwestern White Pine, and has a proposal to study Cottonwood in process.

Whipple says that models currently suggest that Piñon Pine will be gone from Arizona within the next 50 years, adding that the models do not take into account possibilities for evolution or genetic variance that might help the Piñon survive. Her research is largely asking, will trees from hotter, drier locations have a better chance of surviving climate change? “We’re trying to do that with a number of different species to look for ways to mitigate the effects of climate change in the Southwest.”

Researchers documenting a Piñon Pine

Researchers documenting a Piñon Pine. Photo credit Paul Heinrich.

In some of her research on Piñon Pine, it was discovered that four different species were grouped morphologically and geographically from southern Arizona to Central Mexico. While this suggests that the divergence of species has occurred, it also suggests a low migration rate for these tree species. Migration rates of drought and temperature tolerant species is an important consideration when modeling for a future climate. If the migration of genetically adapted species cannot keep up with climate, the land could become marginal as a foundational species dies off.

Climate Change Predictions and Considerations

In the Southwest, there are entire forests that could become grassland in 50 years because the genetic characteristics of the foundational species currently in those regions will not adapt to higher temperatures and drought stress. But what does this mean from a land management perspective?

Ponderosa pine tree hanging off the side of a rocky cliff in the desert

Ponderosa pine trees, a foundational species in some area of the Southwestern United States.

Environmental conservationists maintain that we should protect the unique species that are in a place and that introducing other organisms or genetic material would be an ethical violation. Environmental interventionists make the argument that climate change has been caused by humans, so we have lost the option of remaining bystanders.

Research, Land Management and Policy

Paul Heinrich says that the route we take to manage the land will depend on our end goals. “Places that have trees now, if you want them to have trees 50 years from now, you are going to have to do something about it. The trees that are on the landscape right now are locally adapted to the past climate. They are not necessarily adapted to the future climate. They are probably maladapted to the future climate.”

To be clear, SEGA’s goal is not to promote or implement assisted migration. Instead, Amy Whipple says, SEGA can test what the effects of assisted migration might be. “In a smaller experimental context, we’re asking: how will these plants do if we move them around? What will happen to them if we don’t move them around?’” The goal is to provide decision makers with the data they need to make informed decisions about how to manage the land.

Image of a Meadow with trees in the distance and a set of mountains

The Arboretum Meadow in Flagstaff, AZ. Home of one of the SEGA research sites. Photo credit Paul Heinrich.

Whipple’s own view is that we may no longer have the option of doing nothing. “Unless major changes are made for the carbon balance of the planet, keeping things the same is not a viable option. Managing for a static past condition is not viable anymore.”

Remaining Questions

Both Heinrich and Whipple acknowledge that these are inherently difficult questions. Ultimately the public and land managers must make these decisions. In the meantime, data from SEGA research may help ensure better predictions, better decisions, and better outcomes.

To find out more about conducting your own climate change research using SEGA go to: http://www.sega.nau.edu/use-sega. 

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Mar 23

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What Does SMAP Mean for In Situ Soil Water Content Measurement?

With the recent news coverage of the SMAP (Soil Moisture Active Passive) satellite launch, researchers may wonder:  what does remote sensing mean for the future of in situ measurements?  We asked two scientists, Drs. Colin Campbell and Chris Lund, for answers to this complex question.  Here’s what they had to say:

Satilight Sending Pictures to Earth

Image: www.jpl.nasa.gov

What is SMAP?

SMAP is an orbiting earth observatory that estimates soil moisture content in the top 5 cm of soil over the entire earth.  The mission is three years long with measurements taken every 2-3 days. This will allow seasonal changes around the world to be observed over time, improving our ability to manage water resources and better parameterize land surface models.  SMAP determines the amount of water found between the minerals, rocky material, and organic particles found in soil by measuring the ability of radar to penetrate the soil.  The wetter the soil is, the less the radar will penetrate.  SMAP has two different sensors on the platform: an L band aperture radar with a resolution of about a kilometer when it’s looking straight down (the pixel size is about 1 km by 1 km), combined with a passive radiometer with about 40 km of resolution.  This combination creates a synthetic product that takes advantage of the sensitivity of the radiometer.

What does SMAP mean for in situ soil water content measurement?

It’s all about scale: In some ways, comparing in situ to SMAP measurements is like comparing apples to…well…mountain-sized apples.  The two forms of measurement use vastly different scales.  In situ soil moisture sensors measure water content at the volume of several liters of soil, maximum. Even the sensor with the largest field of sensitivity, the neutron probe, can only integrate a volleyball-sized volume.  On the other hand, SMAP measures at a resolution of 1 km2, which is larger than the size of a quarter section, a large field for many farmers. Global soil moisture maps will allow scientists using SMAP to look at big picture applications like weather, climate and hydrological forecasting, drought, and flooding, while more detailed in situ measurements will tell a farmer when it’s time to water, or help researchers discover exactly why plants are growing in one location versus another.  The difference in spatial scale makes the two forms of measurement useful for very different research purposes and applications. However, there are applications where the two measurements can be complementary. Most notably, in situ measurements are often temporally rich while being spatially poor. But, SMAP can be used to scale in situ measurements to areas where in situ measurements are absent. In situ measurements can also be used as a source of validation data for SMAP-derived values for any location where both in situ and SMAP measurements overlap. Thus, there is opportunity for synergy when pairing SMAP and in situ measurements.

A Map

Satellite image in Winter.

What can SMAP do that in situ measurement can’t?

Scientists say they’ve seen a relationship between the top 5 cm of soil moisture and some factors related to climate change and weather. Because in situ soil sensors sample across a spatial footprint of a few meters, it can be very difficult to use their data to say anything about processes occurring across broad spatial scales; two liters of soil is not going to tell you anything about weather or flooding.  SMAP can help us better understand the interaction between the land surface and atmosphere, improving our understanding of the global water cycle as well as regional and global climate. This will help with forecasting crop yield, pest pressure, and disease…that’s big picture research.

 The productivity of a forest also may depend on the general soil moisture measured by SMAP.  For instance, if we got an idea of the soil moisture and greenness of a forest, we could tie together the approximate water availability and the resulting biomass accumulation with incoming solar radiation.  Better biomass accumulation models could lead to better validation of global carbon cycle models.

SMAP will also be able to detect dry areas across the U.S. and challenges they might present. Surface runoff that leads to flooding could also be predicted as scientists will be able to see where soils reach saturated conditions.

In other applications, people working on global water or energy budgets have to parameterize the land surface in terms of how wet or dry it is. That’s the big advantage of SMAP’s relatively new data sets.  Any time you’re running a regional climate model you have to parameterize what the soil moisture is in order to partition surface heat flux into sensible and latent heat flux. If there’s a lot of available water, it’s weighted more toward evaporation and less toward sensible heat flux.  In areas where there’s little available water and low evaporation, you get high surface temperatures and sensible heat flux.  So SMAP will be important for model parameterization as we haven’t had a good global data set for soil moisture until now.

Dirt with a Root Sticking Out of it

In situ sensors show how much water is lost from the root zone and what is still left.

What can in situ sensors do that SMAP can’t?

In irrigated agriculture, farmers need to know when and how much to irrigate.  In situ sensors give them this information by showing how much water was lost from the root zone and what is still left.  SMAP is unable to tell you what’s down in the root zone; it only reaches to 5 cm.    Additionally, 1 km resolution is larger than most irrigation blocks. These factors mean that it will be difficult to make irrigation decisions from SMAP alone.

Scientists using in situ sensors are concerned with the soil moisture available in a local area because their time resolution is excellent and they have the ability to resolve what’s happening in particular conditions related to crops or natural systems.  Natural systems are often heterogeneous, meaning there may be adjacent areas with different types of vegetation including trees, shrubs, and grass.  Tree roots may grow deep while grass roots are shallow.  Being able to look over all these different areas without averaging them together, as SMAP does, is critical in some applications.

 What about geotechnical applications?  Literature suggests SMAP output can help predict landslides. It is more likely that it can only see when the soil is generally saturated and generate a warning. But in slopes that are at risk of landslides, in situ monitoring with sensors such as tensiometers to measure positive pore water pressure may be more useful for determining when a slide is imminent.

SMAP, like in situ water content measuring systems, is also limited by the fact that it measures the amount, not the availability, of water. If it measures 23% water content in a certain area, that measurement may not tell us what we want to know. A clay soil at 23% VWC will be close to wilting point while a sand would be above the plant optimal range. SMAP doesn’t measure the energy status of water (water potential), so even if SMAP tells us a field has water content, that water might not be readily available.  Water availability must be determined through a pedo-transfer function or moisture release curve appropriate for a specific soil type (It is possible to overlay SMAP data on soil type data to estimate energy state, but this might not be fine enough resolution to be useful).

Complementary Technology

How do SMAP and in situ instruments work together?  The key is ground truthing in situ soil moisture measurements with SMAP type satellites and vice versa.  Ground-based measurements at specific locations can be matched with satellite information to extrapolate over a field and gain confidence in the small continuous scale alongside the larger infrequent scale.  It’s analogous of a video camera recording one plant continuously while a single shot camera snaps whole-field pictures every day.  With the SMAP “single-shot” we can say, something changed from time A to time B, but we don’t know what happened in the middle (rain event, etc.). In situ measurements will tell us the details of what happened in between each snapshot.  Putting both data sets together and matching trends, we can show correlation and complete the soil moisture picture.  Basically, In situ measurements provide temporally rich information about soil moisture from a postage stamp-sized area of earth’s surface (driven by highly localized conditions), whereas SMAP gives us the ability to monitor broad scale spatiotemporal patterns across all of earth’s surface (driven by synoptic conditions).

Download the “Researcher’s complete guide to soil moisture”—>

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Jan 26

Sneak Peek: Remote Sensing in Commercial Agriculture: Perspective on Innovations

Dr. Christopher Lund is a research scientist and product manager for METER’s new irrigation management instrumentation group. He has more than a decade of experience working with land surface flux measurements, terrestrial water budgets, and soil-vegetation-atmosphere transfer scheme modeling. Prior to joining METER, he served as a research scientist on the NASA-CSUMB SIMS (Satellite Irrigation Management Support) Project, a multi-year collaboration between the California Department of Water Resources, NASA, and CSU Monterey Bay providing California growers with novel irrigation decision support tools. Dr. Lund’s current research focuses on developing cost-effective irrigation management instrumentation for commercial markets. Dr. Lund will be giving a talk on innovations in agricultural remote sensing at the Third Professional Workshop on Technology For Irrigation Scheduling.  He will talk about his work with the SIMS team and what growers can do with remote sensing data to estimate things like evapotranspiration.  He’ll also address how to improve those estimates by combining them with field measurements from ground based instrumentation such as soil moisture sensors.

remote sensing in commercial agriculture

Image: USGS Landsat Project Website

“The advantage of satellite remote sensing is that it allows you to look at many fields at once and also integrate across spatial variability.  The down side is it doesn’t give you access to everything you might want for irrigation management, so there are certain things you have to measure on the ground.  When it comes to remote sensing data and ground measurements, I don’t think it’s an either/or situation.  I think the future is hybrid products utilizing both remote sensing and ground based measurements,” he says.

He will also speak on how satellite derived NDVI data can benefit from new inexpensive ground based-sensors like the SRS.  This enables scientists to make sure that their satellite NDVI data accurately reflect what’s happening on the ground.

The seminar will be held at the Third Professional Workshop On Technology For Irrigation Scheduling on February 11, 2015 at the CREA auditorium, Calle Jose Galan Merino Sevilla, Spain.

Download the “Researcher’s complete guide to soil moisture”—>

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