How to Use Satellites to Find Growing Plants: A Practical and Theoretical Guide

USGS Maximum consecutive dry days, July 2024

Introduction

Knowing where and when plants are green and growing would help botanists and ecologists plan field work and help managers make real-time land management decisions.  There are many potential sources of geospatial data available but it is difficult to know which sources are most useful.

Over the past year I evaluated the accuracy and interpretability of dozens of indicators that could be used to assess current growing conditions.  These websites, maps, and data layers are mainly provided by US Government agencies to help managers respond to natural resource concerns such as rangeland management and drought impacts.

In my opinion, none of the available resources is a perfect fit to find growing plants.  However, understanding how plant growth is related to biophysical constraints can help identify the best available resources.

Plant Growth Theory

Plant growth (Net Primary Productivity, NPP), is proportional to total soil moisture (SM) and Growing Degree Days (GDD) of accumulated Temperature (T) since the start of that plant's growing season.  Soil moisture depends upon accumulated (total and frequency of ) precipitation (P) minus evapotranspiration (ET).  

 NPP = Sum (SM*T)

 SM = Sum (P-ET)

[In the above equations, "equals" is used to mean proportional, or depends upon. ]

The ideal resource for plant growth would be a direct measurement of NPP.  A second option would be measurement of soil moisture and temperature, with a way of combining them to estimate NPP.  A third option would be measurement of precipitation, which could be used to estimate SM. All of these variables can be measured via satellite, but delays make them difficult to use in real time.  Also, there are important details in how they are measured and modelled that make interpretation difficult.  

Details

Plant Growth: satellites can measure NPP via NDVI (Normalized Difference Vegetation Index), but available sources are delayed (DroughtView), low-resolution (GIMMS), weighted toward trees, and contain artefacts early in the growing season.  Existing models of NPP are not well-calibrated (VegDRI), or delayed and not mapped (RAP).

Soil Moisture: satellites can measure SM, but satellite data (SMAP) is not consistently available, may be inaccurate, and only provides a snapshot, not the sum.  Models of SM are not well-calibrated (NASA LIS).

Temperature: sensor networks can measure T and calculate Growing Degree Days (NPN) but aspect and micro variation are often more important than county-level temperatures.  This is helpful early in the growing season.  

Precipitation: radar and rain gauge networks can measure P and are timely and accurate.  Maps of accumulated P are available from NOAA and number of days with/without rain in the last month from USGS.  However, because P is not a direct measure of NPP, this can only be a general guide to plant growth.  

Conclusions

P - Best. Note that both total and frequency matter.

SM - not available.

T - GDD is helpful in Spring phenology, but don't matter later in the season.

NPP - difficult to use.  Good for viewing thinning and forest fires, not very helpful for spotting wildflower growth or distinguishing between grass and tree green-up.  

NOAA precipitation accumulation as percent of normal for July, 2024

Practical How-To

I recommend using a balance-of-evidence approach that combines the two best sources of precipitation information, NOAA for total accumulation, and USGS for frequency.  If both sources show good precipitation during the recent growing season, there is a good chance that plants are growing well in response to abundant and regular moisture. 

NOAA's National Water Prediction Service provides accumulated precipitation over any time period from 2005-present with less than 24 hour delay.  Their web viewer is currently the only way I know to view this data because I don't have the programming skills to use their API.  

Scroll down to Precipitation Estimate, where it is possible to set the time period of interest and map Precipitation in terms of Observed (totals), Normal (average), Departure from Normal (inches more/less than normal), and Percent of Normal.  The map is sometimes glitchy depending on connection speed.  More details about how precipitation is calculated are available in the help guide.  Note that this map is usually up to date for the previous "water day" ending in the early morning (so for AZ, selecting "Today" shows precipitation that occurred yesterday through 7 am today).

USGS's Drought Monitor provides access to several precipitation metrics, with a 48 hour delay. To assess precipitation frequency/regularity, I use Maximum Consecutive Dry Days (Past 30 days) and Days Since Precipitation.  They also offer total accumulated precipitation over the last 7 and 30 days (again, with a 2 day delay).  Their web-viewer is clunky and hard to use, but it is easily available.  Double click on the Dataset of interest in the left pane and resize the resulting window to view the area of interest.  The layers have no opacity, so to view the underlying map they have to be checked on/off in the Layers pane.  

They also make their data available as WMS that can be added to any ArcGIS desktop or online map.  The opacity can be adjusted on the Group layer that is created after the data is mapped.  However, the time-enabled settings can be difficult to use, and I haven't figured out how to show the data time period.  The web viewer is easier to change the time period and to see the data date range.

Note that USGS Drought Monitor also provides VegDRI and QuickDRI, two models that claim to incorporate all of the biophysical variables listed above (and then some!) to model NDVI difference from average.  These models are extremely complex, but they don't seem well-calibrated to Arizona because I have not found them to be very accurate or helpful.  

The Knife Edge of Spring

"To what purpose, April, do you return again?

Beauty is not enough.

You can no longer quiet me with the redness

Of little leaves opening stickily."

-from Spring by Edna St. Vincent Millay

In AZ, the spring green up of grasses and trees makes use of a narrow window of time between declining winter moisture and increasing summer heat.  Our redbud trees blooms for three or four days (this year, from April 26-29), the lilac bushes hardly last much longer, and the spring green up of weedy lawns is over by the end of May.  June is the dry season in Arizona.  June is when spring dies.

The knife edge of spring in Arizona can be seen in the biomass production.  For example, the Rangeland Analysis Platform shows that peak spring growth in Dugas, AZ in 2020 lasted approximately 3 weeks, from 4/1 to Earth Day 4/22.  By May, the growth of the annual green up was already in freefall.  More examples can be seen in this previous blog post using RAP to investigate biomass production variability from year to year.  

Phenology Mapping

The National Phenology Network's Visualization Tool can be used to follow the spring green up via Accumulated Growing Degree Days (AGDD), which work by adding up all of the days and temperatures above some minimum threshold for growth, usually 32 F for cool season plants (winter annuals, cool season grasses, willows and cottonwoods and other early spring plants) or 50 F for warm season plants (mesquite, acacia, and other warm season trees like Chilopsis, walnut, and warm season grasses and forbs).

AGDD Details

A "growing degree day" (GDD) is calculated by subtracting the threshold temperature (T) from the average temperature for each day when the minimum temperature is above the threshold temperature of 32 or 50 degrees. 

Average T = (Maximum T - Minimum T)  / 2

GDD = Average T - Threshold T

 Accumulated growing degree days (AGDD) simply adds up all of the GDD since the beginning of the year.

AGDD = GDD (January 1) + GDD (January 2) + … + GDD (yesterday)

To use the NPN Visualization Tool for AGDD, choose Map>Base Layer>Category: Daily temperature accumulations>Layer:Current Day.

AGDD Example - 32 F

The figure below shows AGDD around Prescott, AZ from a threshold temperature of 32 as 5/2/24.  Most of the map shows AGDD of about 2,000; since there have been about 120 days since the beginning of the year, that works out to an average daily temperature of 48 degrees F (2,000/120 + 32 =  48).  Of course, some areas have been warmer than that and some have been cooler:  

Phenology AGDD from 32 F 

1,600 - cottonwoods leafed out (Prescott)

1,800 - still winter grasses not green (Kirkland junction)

2,200 -  annual grasses very green, early spring wildflowers blooming (Dugas)

2,245 - mesquite not leafed out yet, annual grasses still somewhat green (Yava)

2,500 - mesquite leafed out, annual grasses brown (Date)

2,800 mesquite flowering (Congress)

AGDD Example 50 F

Or maybe a 50 degree threshold better shows mesquite and grass green up? The average daily temperatures, of course, are the same, but the different threshold yields much lower AGDDs, mostly in the low 100's.  On this map, light green area are grasses already brown and mesquite leafed out, green areas actually are green fields of annual grasses, whereas white and blue are areas where willows, cottonwoods, and elms have leafed out, but herbaceous plants are just barely getting started.

Phenology AGDD from 50 F 

240 - oaks turning brown (Kirkland)

350 - mesquite not greened up yet, annual grasses and forbs green (Dugas)

400 - mesquite not leafed out yet, annual grasses still somewhat green (Yava)

550 - mesquite leafed out, annual grasses brown (Date)

800 - mesquite flowering (Congress)

AGDD Anomalies

The examples above of specific phenology AGDD values can be used in conjunction with NPN's Visualization Tool to predict plant growth stage.  Also, it is possible to use NPN's Visualization Tool to highlight geographic areas that may be ahead or behind the usual spring green up using the "Anomaly" visualization.  In the figure below, anomaly from 32 F AGDD, it can be seen that the Kirkland valley, Black Canyon City, and the Verde valley are behind (blue) normal phenology, whereas Prescott valley and especially the area north of Bagdad are ahead (red) normal phenology.  

"Life in itself 

Is nothing,

An empty cup, a flight of uncarpeted stairs.

It is not enough that yearly, down this hill,

April

Comes like an idiot, babbling and strewing flowers."

-from Spring by Edna St. Vincent Millay

Herbaceous Vegetation in Southwestern Pine Forests

Herbaceous forage in southwestern pine forests can be few and far between.  Especially in unnaturally-overgrown thickets, there is simply not enough light and too much acidic leaf needle litter to grow robust grasses and forbs.  The high carbon content and resistant nature of conifer bark and needles means that few nutrients are available to growing plants.  

Even when forest thinning restores natural stand densities, the recalcitrant accumulated biomass often seems to limit production.  There is simply too much carbon and it is clogging up microbial turnover of nutrients.  Sure, you'll find plenty of aboveground mushrooms and mycelial hypahe at work in the soil, but I have to wonder if there isn't a more productive alternate-stable state.

What is the limiting resource?  I've theorized that light may still be limiting when forests aren't thinned enough to create light gaps in the canopy.  But it also seems that the overabundance of carbon may be soaking up nitrogen.  In these nutrient-poor systems we typically find 'tolerator' species like sedges, that can eke out a frugal living in acidic conditions.

What about blueberries?  In some parts of the world, conifers and blueberries go together like apples and pie, but in many of the southwestern mountain ranges we have no native Vaccinium.  I'm still not sure why, except that they seem to prefer colder (moister?) climes than New Mexico can provide....

There are two species of Vaccinium in the southwest, with Vaccinium myrtillus much more common.  However, south of Santa Fe even this species only occurs at scattered locations in mixed conifer and subalpine forests.  These high-elevation forests are being rapidly lost to stand-replacing forest fires.  Even with current climate change, it is unlikely that extensive tracts of cooler forests will be able to regrow.

What about legumes?  Fabaceae are often able to supply their own nitrogen requirements and eventually supplement total ecosystem N.  But I'm surprised by how rarely I find good legumes in the forest.  Thermopsis is surprisingly rare, as are Lotus and I almost never find clovers growing in conifer needle duff.  

Robinia neomexicana seems to be one legume shrub that has found its way into a diversity of habitats, growing almost like a weedy in mountainous areas throughout the southwest.  

Seeding mixtures in forests typically use annual grasses, but I wonder if there isn't a Fabaceae that could dramatically enhance production to increase ground-cover, forage, pollinator, and wildlife habitat?  Should standard thin-and-chip treatments be supplemented with seeding efforts?

What about increasing disturbance to disrupt pine duff accumulation?  These thick, undecomposed layers and inhibit germination of many forbs...

Terrestrial Fertilization to Sequester CO2?

One of the main uncertainties in the global carbon cycle is measuring the amount of carbon bound up in ecosystems such as forests and grasslands. This Net Ecosystem Production (NEP) converts CO2 to plant material, detritus, and some animals. Most escapes back to the atmosphere as respired CO2, but some is sequestered in soil organic matter and trees.

Most ecosystems are Nitrogen limited because fertilization with nitrogen increases NEP. Interestingly, many ecosystems are already fertilized by Nitrogen deposition from drifting clouds of various nitrogen compounds emitted by urban areas, industry, and agriculture.

Nitrogen deposition increases productivity and decreases respiratory losses from decomposition (Hogberg). But how much? And how much is too much? Natural vegetation may be satiated/saturated with a low quantity of nitrogen, and any more would begin acidifying the soil, killing plants and washing away to pollute the watersheds.

Magnani et al attempt to answer some of these questions and measure the size of the "The human footprint in the carbon cycle of temperate and boreal forests" of Europe. They find that the amount of nitrogen deposited in European forests confers a huge increase in fertility; they find no sign of a decrease due to Nitrogen saturation.

However, their findings rested on a number of unpublished studies, and a flurry of correspondence questioned their main conclusions. De Shrivjer et al point out that just because NEP continues to increase with increasing Nitrogen deposition, this doesn't mean that the forest ecosystems aren't loosing nitrogen as runoff. Indeed, it makes sense that at very high applications of fertilizer an increasing fraction would be wasted. Many farmers have to contend with the problem that, beyond a certain point, a doubling of Nitrogen fertilizer may confer only an incremental increase in crop productivity, while vastly increasing the amount of Nitrogen that washes off.

de Vries present a more central problem in Magnani et al's results: according to Magnani's data correlation, for every unit of Nitrogen applied to European forests, 470 units of carbon are sequestered. Yet the only plant material with a C:N ratio that high is pure xylem wood, and it seems unlikely that all of the deposited nitrogen is being used to grow tree stems. Furthermore, de Vries et al find that Magnani et al failed to control for a range of other variables that could affect forest NEP. de Vries reanalyze that portion of Magnani's data that is publicly available and find a more plausible -- and vastly reduced -- C:N ratio of 20 to 40 (20-40 units carbon for every unit nitrogen).

It doesn't end there, though. Magnani et al respond that they agree with De Shrivjer, but refute de Vries. Magnani point out differences between wet and dry deposition, to argue that their stoichiometric ratio is really closer to 175-225. They claim that this ratio is not implausible, even though it is much higher than actual forest fertilization experiments (Nadelhoffer). They explain this difference by suggesting that up to 70% of the actual nitrogen deposited is absorbed by leaves, whereas the forest fertilization experiments applied nitrogen to the soil. (Nadelhoffer).

{[It is not clear to me what the consensus is on how much nitrogen can be absorbed by the canopy, or why wet versus dry deposition matters. }

News and Views: Hogberg P. Environmental science: Nitrogen impacts on forest carbon. Nature. 2007 June 14;447(7146):781-782.

Original Paper: Magnani F, Mencuccini M, Borghetti M, Berbigier P, Berninger F, Delzon S, Grelle A, Hari P, Jarvis PG, Kolari P, et al. The human footprint in the carbon cycle of temperate and boreal forests. Nature. 2007 June 14;447(7146):849-851.

Questions: De Schrijver A, Verheyen K, Mertens J, Staelens J, Wuyts K, Muys B. Nitrogen saturation and net ecosystem production. Nature. 2008 February 14;451(7180):E1.

More Questions: de Vries W, Solberg S, Dobbertin M, Sterba H, Laubhahn D, Reinds GJ, Nabuurs G-J, Gundersen P, Sutton MA. Ecologically implausible carbon response? Nature. 2008 February 14;451(7180):E1-E3.

Response:Magnani F, Mencuccini M, Borghetti M, Berninger F, Delzon S, Grelle A, Hari P, Jarvis PG, Kolari P, Kowalski AS, et al. Magnani et al. reply. Nature. 2008 February 14;451(7180):E3-E4.

More Information: Nadelhoffer, K. J. et al. Nitrogen deposition makes a minor contribution to carbon sequestration in temperate forests. Nature 398, 145–148 (1999)

Follow Up: SUTTON MA, SIMPSON D, LEVY PE, SMITH RI, REIS S, Van OIJEN M, De VRIES WIM. Uncertainties in the relationship between atmospheric nitrogen deposition and forest carbon sequestration. Global Change Biology. 2008 September 1;14(9):2057-2063.

Human Appropriation of NPP

http://sedac.ciesin.columbia.edu/es/hanpp.html

http://www.eoearth.org/article/Global_human_appropriation_of_net_primary_production_(HANPP)

How efficient are plants? (part II)

Insolation at Earth's surface (the total solar irradiance, in units of W/m2):

On sunny days, about 1 kilowatt of solar radiation bathes every square meter of the earth's surface every hour. Because of the seasons and weather, the annual average for much of the united states is between 1/2 and 1/3 of this ideal "clear sky" condition.

Photosynthetically Active Radiation, or PAR, is only ~40% of that total, depending on which chlorophyll molecules are present in the plant. Because of physiological requirements, plants actually use an order of magnitude less, usually about 2% of total solar insolation. Of that, orders of magnitude less are available for conversion into biomass. Contrast that with commercial solar arrays that can capture about 20% of the energy in solar radiation!

Either way, the rest of that kW is either reflected or converted to heat...and heat makes the wind blow. A 20mph wind contains enough energy to generate 1 kilowatt per square meter for every hour it blows.

references: http://www.pages.drexel.edu/~brooksdr/DRB_web_page/papers/UsingTheSun/using.htm

How efficient are plants? (part I)

Annual net primary production (g C m-2 yr-1) estimated as the average of all model estimates:

In order to answer how efficient plants are at converting solar energy into chemical energy, we can look at how much energy is in sunlight and how it is used in photosynthesis.

The surface of the Earth receives 8,000 to 10,000 kilocalories (kcal) of energy from the sun each day on each square meter of surface during the growing season.

A kcal is what most people call a calorie (you're supposed to eat 2,000 a day) and is defined as the amount of heat needed to warm 1 kg of water 1 degree Celsius (°C). Indeed, most (~95%) of this solar energy is used up heating the surroundings and evaporating water, while a paltry ~2% is used for photosynthesis (3% is reflected).

So out of 10,000 kcal only 2% or 200 kcal are available to a one square meter plant per day. If the growing season is 150 days, Gross Primary Productivity should be on the order of 30,000 kCal per year. However, at least half of this is lost by cellular respiration as the plants run their own metabolism. Also, C3 plants respire CO2 at high temperature and sunlight because the protein machinery can "run backwards" (C4 plants minimize these losses to "photorespiration"). Because of other inefficiencies, the Net Productivity is always lower:

Estimated Net Productivity of Certain Ecosystems (in kilocalories/m2/year)
Temperate deciduous forest	5,000
Tropical rain forest	15,000
Tall-grass prairie	2,000
Desert	500
Coastal marsh	12,000
Ocean close to shore	2,500
Open ocean	800
Clear (oligotrophic) lake	800
Lake in advanced state of eutrophication	2,400
Silver Springs, Florida	8,800
Field of alfalfa (lucerne)	15,000
Corn (maize) field, U.S.	4,500
Rice paddies, Japan	5,500
Lawn, Washington, D.C.	6,800
Sugar cane, Hawaii	25,000

Note that even the most efficient crops store about half of

At these efficiencies it would take 144 square meters of temperate forest to supply the 720,000 kcal an average adult needs to survive a year, but just 28 square meters of sugar cane in Hawaii, assuming we could eat all the calories produced in a forest or live entirely on sugar. These areas are about the size of a small house and a bedroom, respectively. (1 sq meter = 10.7 sq feet)

The graph at the top of this post shows that the average productivity is about 1 kg per square meter per year. This makes sense because a gram of whole plant biomass yields about 4.25 kcal, so 1kg of plant matter per square meter yields 4,250 kcals of energy.

sources: http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/N/NetProductivity.html
note: these estimated values are different, though of the same magnitude, from other sources.

A map of which input is most limiting across terrestrial biomes:  temperature, sunlight, or water.