The deadline is fast approaching to apply for the 2019 Grant A. Harris Fellowship. The fellowship awards $10,000 in METER research instrumentation to six U.S. or Canadian graduate students studying any aspect of agricultural, environmental, or geotechnical science.
(Image source: https://vimeo.com/69136931)
Camila Tejo Haristoy, former University of Washington grad student, was a Grant A. Harris Fellowship winner. She used METER soil moisture and temperature sensors to study the water holding and temperature patterns of canopy soil in an old-growth Sitka Spruce forest in Washington state. Sitka Spruce tree crowns contain large accumulations of organic matter known as “canopy soil”. These accumulations provide substrate and habitat for a broad community of plants, insects, and other arboreal species. Using tree-climbing techniques, Camila installed soil moisture and temperature sensors in the canopy soils of spruce trees from an old-growth stand in the Olympic Peninsula, Washington.
This study characterized for the first time environmental conditions associated with soil mats within the crown of spruce trees, providing a framework for understanding the distribution and activity of epiphytic plants, nutrient dynamics, and associated canopy organisms.
Watch the documentary
Watch a fascinating 7-minute documentary of Camila’s interesting and exciting research. The documentary description: “Camila spends long rainy days climbing into treetops, taking temperature and moisture measurements, and collecting soil and plant samples. In the process, she interacts with a seldom seen, barely understood, and lushly beautiful environment.” (source https://vimeo.com/69136931)
In this brief 30-minute webinar, Dr. John Gammon, University of Alberta, teaches the basics of the Photochemical Reflectance Index (PRI).
He gives an introduction to the photochemical reflectance index and what it can tell researchers about xanthophyll cycle activity, carotenoid: chlorophyll pigment ratios, light-use efficiency, and plant stress. He also discusses remote sensing.
University of Idaho graduate student and past Grant A. Harris Fellowship recipient, Adrianne Zuckerman, is taking a different approach to stream restoration than the traditional approach, channel manipulation, which often requires heavy equipment and major disruption to the riparian area.
Zuckerman set out to understand how vegetation lining the stream bank impacts habitat quality for anadromous salmon and steelhead in Washington’s Methow River, which flows through the eastern Cascades. Zuckerman wanted to know how tree species composition affects the amount of nutrients available to the benthic insect community, since they are a critical food source for young salmonid fish.
When Zuckerman began investigating methods for measuring leaf contribution to the stream, she found that leaf litter traps were the standard equipment. Leaf litter traps are time-consuming to set and maintain, and data analysis consists of frequent visits to the field followed by extensive time in the lab processing leaf material.
Looking for an alternative method, she discovered the LP-80 ceptometer: a lightweight, field-portable instrument for measuring leaf area index. Using the LP-80, Zuckerman was able to rapidly assess the leaf area contribution of each tree species along the riparian corridor. Using this information, it was relatively straightforward for her to estimate the contribution of each tree species to the stream food web.
Zuckerman’s research will help land managers and other researchers understand the importance of riparian vegetation for maximizing the food available to salmonid fish species. Improvement and maintenance of optimal stream-side vegetation composition should ultimately help to enhance salmon populations in the Pacific Northwest.
Receive $10,000 in METER research instrumentation
The Grant A. Harris Fellowship awards $40,000 in research instrumentation (four $10,000 awards) annually to graduate students studying any aspect of agricultural, environmental, or geotechnical science. METER is now accepting applications for the 2018 award. Learn more about how to apply for the Grant A. Harris Fellowship here.
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The conversion of light energy and atmospheric carbon dioxide to plant biomass is fundamentally important to both agricultural and natural ecosystems.
The detailed biophysical and biochemical processes by which this occurs are well understood. At a less-detailed level, however, it is often useful to have a simple model that can be used to understand and analyze parts of an ecosystem. Such a model has been provided by Monteith (1977). He observed that when biomass accumulation by a plant community is plotted as a function of the accumulated solar radiation intercepted by the community, the result is a straight line. Figure 1 shows Monteith’s results.
Figure 1. Total dry matter produced by a crop as a function of total intercepted radiation (from Monteith, 1977).
In an update to our previous blog, “Soil Moisture Sensors in a Tree?”, we highlight two current research projects using soil moisture sensors to measure volumetric water content (VWC) in tree stems and share why this previously difficult-to-obtain measurement will change how we look at tree water usage.
Researchers explore the feasibility of inserting capacitance soil sensors in tree stems as a real-time measurement.
Soil Moisture Sensors in Tree Stems?
In a recent research project, Ph.D. candidate Ashley Matheny of the University of Michigan used soil sensors to measure volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple. Though both tree types are classified as deciduous, they have different strategies for how they use water. Oak is anisohydric, meaning the species doesn’t control their stomata to reduce transpiration, even in drought conditions. Isohydric maples are more conservative. If the soil starts to dry out, maple trees will maintain their leaf water potential by closing their stomata to conserve water. Ashley and her research team wanted to understand the different ways these two types of trees use stem water in various soil moisture scenarios.
Historically, tree water storage has been measured using dendrometers and sap flow data, but Ashley’s team wanted to explore the feasibility of inserting a capacitance-type soil sensor in the tree stems as a real-time measurement. They hoped for a practical way to make this measurement to provide more accurate estimations of transpiration for use in global models.
Scientists measured volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple.
Ashley and her team used meteorological, sap flux, and stem water content measurements to test the effectiveness of capacitance sensors for measuring tree water storage and water use dynamics in one red maple and one red oak tree of similar size, height, canopy position and proximity to one another (Matheny et al. 2015). They installed both long and short soil moisture probes in the top and the bottom of the maple and oak tree stems, taking continuous measurements for two months. They calibrated the sensors to the density of the maple and oak woods and then inserted the sensors into drilled pilot holes. They also measured soil moisture and temperature for reference, eventually converting soil moisture measurements to water potential values.
Results Varied According to Species
The research team found that the VWC measurements in the stems described tree storage dynamics which correlated well with average sap flux dynamics. They observed exactly what they assumed would be the anisohydric and isohydric characteristics in both trees. When soil water decreased, they saw that red oak used up everything that was stored in the stem, even though there wasn’t much available soil moisture. Whereas in maple, the water in the stem was more closely tied to the amount of soil water. After precipitation, maple trees used the water stored in their stem and replaced it with more soil water. But, when soil moisture declined, they held onto that water and used it at a slower rate.
Researchers want to figure out the appropriate level of detail for tree water-use strategy in a global model.
Trees use different strategies at the species level
The ability to make a stem water content measurement was important to these researchers because much of their work deals with global models representing forests in the broadest sense possible. They want to figure out the appropriate level of detail for tree water-use strategy in a global model. Both oak and the maple are classified as broadleaf deciduous, and in a global model, they’re lumped into the same category. But this study illustrates that if you’re interested in hydrodynamics (the way that trees use water), deciduous trees use different strategies at the species level. Thus, there is a need to treat them differently to produce accurate models.
Reference: Matheny, A. M., G. Bohrer, S. R. Garrity, T. H. Morin, C. J. Howard, and C. S. Vogel. 2015. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere 6(9):165. http://dx.doi.org/10.1890/ES15-00170.1
Next week: Learn about more research being done using soil moisture sensors to measure volumetric water content in tree stems.
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Dr. Brent Clothier, Dr. Steve Green, Roberta Gentile and their research team from Plant and Food Research in New Zealandare working in Kenya to alleviate the poverty of the many small-holder farmers who grow avocados in the Central Highlands of Kenya. These farmers have old and very large avocado trees. The fruit from these trees are purchased by the company Olivado EPZ who presses over 1300 small-holders’ avocados for oil. Dr. Clothier and his team are investigating how to increase the productivity of the farmers’ avocado trees and increase the quality of the fruit so they yield more oil.
Small-holder farmers grow avocados in the Central Highlands of Kenya.
Reducing Leaf Area to Avoid Water Stress
Because of the age and size of these trees, harvesting of the avocados is difficult and time consuming, and through dropped fruit, the quality of the avocados can be comprised. In addition, any dry season water-stress negatively impacts fruit filling. The research team performed some initial remedial pruning of these trees to develop a more manageable and productive tree form. They sought to assess whether the reduced leaf area would enable the trees to avoid water stress during the dry season of January through March between the short and long rainy seasons. They removed 30-40% of the central limbs of the avocado tree to create a more open canopy form.
The team instrumented two trees with heat-pulse sap-flow probes. One tree was left unpruned and the tree in the photo above was pruned. The tree that was pruned was using between 300-400 liters per day, as expected for a tree of that large size. The unpruned tree was smaller in size, and it was using between 150-250 liters per day during May and June. The selective limb pruning resulted in the rate of water-use dropping to 200-300 liters per day, a drop of 100 liters per day.
The more open canopy form of the pruned avocado tree.
Determining Tree Water Use During Rainy and Dry Seasons
The team also measured the water-use of four trees of different sizes during the entire season using the compensation heat-pulse method and soil water content. They found the trees’ water-use doubled with the arrival of the Short Rains and then began to decline in early January after the rains ended. The trees were under a degree of water stress prior to the arrival of the (short) Short Rains, and as the weak Short Rains ended early, the trees again went into water stress with only occasional respite due to isolated rainstorms in January and February.
This pattern of water stress presents a challenge for sustaining high levels of avocado production. The period of water stress in October is at the time of main flowering, and researchers who were there noted a carpet of aborted flowers on the orchard floor. They also noticed that the fruit were smaller at one farm than those higher up in the Central Highlands where rainfall is higher and more frequent. Thus, to improve production it is imperative to mitigate the impacts of drought, and this needs to be done without reference to any infrastructure for irrigation.
Next week: Read about an inexpensive irrigation solution for these farmers and how the researchers are developing a plan to manage nutrients.
(This article is a summary/compilation of several articles first printed in WISPAS newsletter)
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We wanted to highlight innovative ways people have modified their instrumentation to fit their research needs. Here, Georg von Unold, founder and president of UMS (now METER) illustrates ingenuity in a story that inspired the invention of the first UMS tensiometer and what could be one of the greatest scientific instrument hacks of all time.
The Bavarian Alps
An Early Penchant for Ingenuity
In 1986, graduating German students were required to join the military or perform civil service. Von Unold chose to do a civil service project investigating tree mortality in the alpine region of the Bavarian Mountains. He explains, “We were trying to understand pine tree water stress in a forest decline study related to storms in certain altitudes where trees were inexplicably falling over. The hypothesis was that changing precipitation patterns had induced water stress.”
To investigate the problem, von Unold’s research team needed to find tensiometers that could measure the water stress of plants in the soil, which was not easy. The tensiometers von Unold found were not able to reach the required water potential without cavitating, so he decided to design a new type of tensiometer. He says, “I showed my former boss the critical points. It must be glued perfectly, the ceramic needed defined porosity, a reliable air reference access, and water protection of the pressure transducer. I explained it with a transparent acrylic glass prototype to make it easier to understand. At a certain point, my boss said, “Okay, please stop. I don’t understand much about these things, but you can make those on your own.”
Two snorkels protected a data logger predecessor from relative humidity.
Snorkels Solve a Research Crisis
The research team used those tensiometers (along with other chemical and microbial monitoring) to investigate why trees only in the precise altitude of 800 to 1100 meters were dying. One challenge facing the team was that they didn’t have access to anything we might call a data logger today. Von Unold says, “We did have a big process machine from Schlumberger that could record the sensors, but it wasn’t designed to be placed in alpine regions where maximum winter temperatures reached -30℃ or below. We had to figure out how to protect this extremely expensive machine, which back then cost more than my annual salary.“
Von Unold’s advisor let him use the machine, cautioning him that the humidity it was exposed to could not exceed 80%, and the temperature must not fall below 0℃. As von Unold pondered how to do this, he had an idea. Since the forest floor often accumulated more than a meter of snow, he designed an aluminum box with two snorkels that would reach above the snow. The snorkels were guided to a height of two meters. Using these air vents, he sucked a small amount of cold, dry air into the box. Then, he took his mother’s hot iron, bought a terminal switch to replace the existing one (so it turned on in the range of 0-30℃), and mounted a large aluminum plate on the iron’s metal plate to better distribute the heat.
Von Unold says, “Pulling in the outside air and heating it worked well. The simple technique reduced the relative humidity and controlled the temperature inside the box. Looking back, we were fortunate there wasn’t condensing water and that we’d selected a proper fan and hot iron. We didn’t succeed entirely, as on hot summer days it was a bit moist inside the box, but luckily, the circuit boards took no damage.”
Tree mortality factors were only found at the precise altitude where fog accumulated.
Interestingly, the research team discovered there was more to the forest decline story than they thought. Fog interception in this range was extremely high, and when it condensed on the needles, the trees absorbed more than moisture. Von Unold explains, “In those days people of the Czech Republic and former East Germany burned a lot of brown coal for heat. The high load of sulfur dioxide from the coal reduced frost resistivity and damaged the strength of the trees, producing water stress. These combined factors were only found at the precise altitude where the fog accumulated, and the weakened trees were no match for the intense storms that are sometimes found in the Alps.” Von Unold says once the East German countries became more industrialized, the problem resolved itself because the people stopped burning brown coal.
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Do you have an instrument hack that might benefit other scientists? Send your idea to email@example.com.
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.
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.
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.
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.
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.
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.
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.
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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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.
Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.
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.
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.
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.
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.
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.
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.
MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)
Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) 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.
Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.
NDVI – Global
The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe. Conversely, areas of low vegetation have low NDVI values, which look brown. NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.
NDVI – Local
How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation—from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year. Within these growth cycles NDVI helps to quantify the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.
Figure 2: Seasonal growth plotted against spatiotemporal variation
Spectral Reflectance Data
So where does NDVI come from? In Figure 3, the x-axis plots wavelength of light within the electromagnetic spectrum; 450 to 950 nm covers both the visible region and a portion of the near infrared. On the y-axis is percent reflectance. This is a typical reflectance spectrum from green vegetation.
The green hyperspectral line is what we would expect to get from a spectral radiometer. Reflectance is typically low in the blue region, higher in the green region, and lower in the red region. It shifts dramatically as we cross from the visible to the near infrared. The two vertical bars labeled NDVI give you an idea of where a typical NDVI sensor measures within the spectrum. One band is in the red region and the other is in the near-infrared region.
NDVI capitalizes on the large difference between the visible region and the near infrared portion of the spectrum. Healthy, growing plants reflect near-infrared strongly. The two images on the right of the figure above are of the same area. The top image is displayed in true color, or three bands–blue, green and red. The image below is a false color infrared image. The three bands displayed are blue, green, and in place of red, we used the near infrared. The bright red color indicates a lot of near infrared reflectance which is typical of green or healthy vegetation.
The reason NDVI is formulated with red and near infrared is because red keys in on chlorophyll absorption, and near infrared is sensitive to canopy structure and the internal cellular structure of leaves. As we add leaves to a canopy, there’s more chlorophyll and structural complexities, thus we can expect decreasing amounts of red reflectance and higher amounts of near-infrared reflectance.
How Do We Calculate the NDVI?
The Normalized Difference Vegetation Index takes into account the amount of near-infrared (NIR) reflected by plants.It is calculated by dividing the difference between the reflectances (Rho) in the near-infrared and red by the sum of the two. NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.
The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy. The ratio of those two give us percent reflectance in each of the bands.
Next Week:Learn about NDVI applications, limitations, and how to correct for those limitations.
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