Ehsan Jalilvand

For more than a year, I dedicated myself to developing and validating a satellite-based soil moisture product specifically for Northern latitudes. This May 2023, I embarked on a remarkable journey, traveling over 1200 miles across Alaska's breathtaking tundra landscape. This firsthand experience enriched my understanding profoundly, providing a grounded perspective and a tangible connection to the terrain I typically observe only through satellite imagery.

Here we measured surface roughness and I am putting back all the pins in their previous position.

Last vegetation measurement with the Laura team, here we measured tree trunk perimeter, count the number of trees, identify the tree type based on the leaves and ridges on the trunk, measured tree height and the distance between the representative trees. These measurements were later taken into account to see how they affect SMAP soil moisture measurements.

Measuring roughness using pinboard. We first put the pinboard on the ground and suddenly open the pin's lid and all the pins fall on the ground then I sit in front of the pinboard and took a picture from the top of the pin which now shows the deviation from a smooth surface. This photo was later analyzed and the roughness is estimated accordingly.

Mahya is measuring the tree heights using the Laser rangefinder and hypsometer. All you need to do is just point at the bottom and then the top of the tree and the device will measure the tree's height.

Measuring soil moisture with 3 different TDR sensors: a HydroProbe (6 cm), a HydroSense (12 cm), and our own low-cost citizen science soil moisture sensor (photo credit Gaetan Pique)

Here, after doing measurements me, and Modi (my groupmate from Georgia tech in this NASA field campaign) came across Alex and Victoria (USDA team) that were measuring surface roughness, and decided to stay and help them. They chose an L shape 3x3 m area on the ground and clear all the leaves and then measure surface roughness using three different methods: 1) PinBoard, 2) iPad LiDAR and, 3) Laser ranger mounted on a device that moves on a rail and scan the below surface (Alex invented this device!)

Measuring bulk density:

To do that we needed to dig a hole, remove the soil, put it in the oven and weigh the dried soil and then divide it by the volume of the soil.

to dig a hole we first put a plexiglass ring on the ground and fix it using three long bolts, then the soil inside the hole is removed to the depth of 6 cm (and 12 cm) and the removed soil was then put in a ziplock to dry and weigh later. In the next step we needed to measure the volume of the soil removed (volume of the hole) to do that we used two methods:

Putting a plastic bag in the hole and adding water from a graduated cylinder until the hole is completely filled (volume of water = volume of soil)
Using iPad pro lidar scanner to scan the hole and measure its volume (see the video)

(photo and video credit Gaetan Pique)

The deep deep dark eyes owl of Harvard Forrest! it was kinda scary and beautiful at the same time! (photo credit Gaetan Pique)

Crop growth stage monitoring (field measurement)

In the middle of Kellogg's corn field, we usually put on a Poncho because corn leaves can be really wet in the morning and you will be soaked after doing measurements. I am not sure if it is due to irrigation or plant transpiration in the morning. The leaves are sharp too and can easily cut your skin, so I wore safety glasses here!

Soybean field, 6:30 AM, we saw a herd of deer jumping and running very fast in the corn and soybean fields, while the sun was slowly rising, so extraordinary! 🦌

Mahya is measuring the surface roughness using iPad pro LiDAR sensor

Me counting the number of soybean leaves. in each round of measurement we usually measure the height, count the number of leaves and cut multiple soybeans to measure the vegetation water content (VWC).

On The way back from last corn field, look how happy I am! 😁

Me measuring Leaf Area Index (LAI) using LAI Meter; you need to do one measurement above the canopy and one below. It is also important that the sun's radiation remain the same in both measurements, this is especially important in partially cloudy weather!

Soybean at the end of the growing season, almost all the leaves turn yellow and in two weeks only seeds and pods remained.

I am measuring the corn height using a measuring tape. the corn can get as high as 3 m!

AGU 2022 Fall Meeting

I used an analogy in my poster to explain what is happening in permafrost and why it is important by adopting the above scene from GOT season 7 when the ice wall was broken by the ice dragon and an army of nightwalkers released to the living world!

Longer than the short story!

The frozen layer of soil near arctic regions (permafrost) has stored remains of plants and animals (Ancient carbon) for thousands of years intact (just like the ice wall that stopped the night walkers from invading the living world). This ancient carbon is estimated to be twice as much as the carbon that is already in the atmosphere, but now due to global warming, at a rate 3-4 times faster than the rest of the world, this frozen layer is rapidly thawing (similar to the destruction of the ice wall by the dragon fire). The thawing permafrost is a perfect recipe for the micro oraganisms that decompose the plant and animal remains and release greenhouse gases (co2 and ch4). Therefore, a huge stock of ancient carbon (like an army of the dead) can be released into the atmosphere which can be a game changer in climate change! However, this is not accurately reflected in climate modeling as the affected permafrost regions are difficult to be identified and the microbial activities involved in the production of the greenhouse gases are not fully understood! long story short, this can result in a significant miss-calculation in climate modeling!

How my research can help?

The microbial community and greenhouse gas production process (e.g., methanogenesis) changes based on the permafrost condition (whether it is fully or partially thawed) and I am developing an algorithm to estimate the surface soil moisture using high-resolution radar data (SMAP- Sentinel 1). The fact is that this product already exists globally but it is not designed for the northern latitude. so I basically update the retrieval algorithm to account for these specific features such as organic-rich soil and transient water bodies.

The community and the other researchers' feedback was extraordinary!

I was a stranger in this specialized permafrost session and among these permafrost scientists! I was a bit worried that the community might say, this is not an accurate analogy! However, to my surprise, most of the experts who stopped by my poster really liked this short story and were encouraging! the interesting thing for me was more than 50% of people who stopped by my poster were from completely different fields that wanted to know what is the story behind that dragon fire!