# Presentation: 034864

## A Potential MODIS Real-Time Product: The Vegetation Canopy Water Content

- Authors: Susan L. Ustin1, David Riaño1,2, Marco Trombetti1,2
- Speaker: David Riaño

- 1CSTARS, University of California, Davis, USA
- 2 CSIC,  Madrid, Spain

- NASA NNG04GQ42G: Global Estimation of Canopy Water Content
- NASA bridge funding: A Implementation Plan for the Direct Downlink MODIS Canopy Water Content Product (CWC)

- EOS/NPP International Direct Readout Meeting 2008
- March 31 - April 4, 2008, Bangkok, Thailand

## Vegetation Canopy Water Content (CWC)

- Definition
- Algorithm
- Validation
- Implementation plan
- Study cases
- Future research

## CWC=Equivalent Water Thickness x Leaf Area Index

Dry Matter Content

Fuel Moisture Content

- Dry Matter Content
- Fuel Moisture Content

- Wf= Fresh weigh; Wd=Dry weigh; LA=Leaf Area; SA=Spatial Area

- Definition: Vegetation Canopy Water Content (CWC)

- Jacquemoud, 1995

## Definition: Vegetation Canopy Water Content (CWC)

- Applications
- Wildfire behavior/risk
- Monitor/assess natural vegetation
- Precision farming
- Linkage with biogeochemical cycles
- Indicator of soil moisture

**Notes:**
Clarifying the linkages between biogeochemical cycles, their feedback on greenhouse gases concentrations and estimating primary productivity of the biosphere. Predict linkages between Biogeochemical cycles to understand feedbacks to atmospheric concentrations of greenhouse gases, and to estimate primary productivity of the biosphere
Monitor/assess natural vegetation health related to drought, pollution or diseases
Precision farming: Improving irrigation scheduling by monitoring its demand Improve irrigation scheduling by reducing over-watering and under-watering
Identify wildfire behavior/risk: drives ignition probability and burning efficiency
Indicator of soil moisture and Leaf Area Index
Energy partitioning and fluxes

## Definition: Vegetation Canopy Water Content (CWC)

- Optical remote sensing more related to CWC = (EWT*LAI)

**Notes:**
Remote sensing more related to CWC because that is the total amount of water in the pixel
DMC is less band specific 
Dot lines are the MODIS bands

## Definition: Vegetation Canopy Water Content (CWC)

- FMC more difficult to obtain than CWC
- Estimate DMC in the dry season
- Field calibration
- No network of field sites

**Notes:**
FMC more difficult to obtain than CWC
FMC depends on DMC
CWC accounts for total water in the pixel
Estimate DMC in the dry season // field calibration
DMC is dependent more on species type than seasonal variation (Shipley & Vu, 2002)
DMC could be assumed to be constant. A decrease is expected during the drought period because of decreased productivity (Garnier et al, 2001)
No network of sites with common sampling methodology of CWC measurements, routinely performed.

Two reasons: 1) CWC does not depend on the DMC 2) CWC for the water in a pixel 
Even if there was a network difficult to obtain significant information.
One option would be to estimate DMC from remote sensing in the drought season when is more accurate.
Another could be to calibrate with field data 

## Algorithm:  CWC

- Empirical approach
- +	Known accuracy
- +	Simple to compute
- Radiative transfer models (RTM)
- +  Based on physical relationships: how sunlight interacts with plant leaf and canopy structure  and soil materials
- +  More independent of sensor
- +  More independent of site conditions

**Notes:**
they should be more universal than empirical fittings. 
Empirical approach 
+	Known accuracy 
+	Simple to compute
Site-dependent
Difficult to extrapolate to regional or global scale studies 
Differences in leaf and canopy characteristics 
Sensor calibration and observation conditions
Radiative transfer models (RTM) 
+  Based on physical relationships: how sunlight interacts with plant leaf and canopy structure  and soil materials 
+  More independent of sensor 
+  More independent of site conditions
Parameterization is far more complex
Expensive computation

## Algorithm:  CWC

**PROSPECT-SAILH**

**EWT****, ****LAI****, DMC**
**N, Cab, LIDF, Soil **

**canopy ******** **

- 1. Radiative Transfer model

**EWT*********LAI**

- 2. Training

**canopy ******

- 3. Validation

**canopy ******

**EWT*LAI**

- (Trombetti et al., 2008)

**Notes:**
PROSPECT-SAILH model. Prospect is the most widely use model to simulate leaf reflectance and transmittance. Sailh also widely used, applicable to homogeneous canopies.
Advantage of use of a physical model but with the neural network to be fast enough!!
Similar methodology applied to estimate LAI from SPOT-Vegetation (Baret, 2007)

## Algorithm:  CWC

- Back-propagation neural network
- Trained with PROSPECT-SAILH
- Noise in all input parameters
- Combinations of MODIS reflectance and vegetation indexes used to train network

**Notes:**
Trained with PROSPECT-SAILH: 600 random samples
Noise in all input parameters to make  makes ANN more tolerant of errors
Back-propagation neural network with 5 neurons in hidden layer
Combinations of MODIS reflectance and vegetation indexes used to train network

I would explain the choice of NN to invert the model
Validate the model with its own performing very well r2 >0.99
Noise in all input parameters makes ANN tolerant of errors

## Validation: CWC

- Multi-scale approach

- MODIS (500 m)

- Hyperspectral AVIRIS-high altitude (16 m)

- Field sampling plots

- Hyperspectral AVIRIS-low altitude (5 m)

## Validation: CWC

- Airborne high spatial/spectral resolution (4-20m, 224 bands)
- Simultaneous estimation of water vapor and liquid water content
- Homogeneous source + Wide areas coverage

- (Cheng et al., 2008)

**Notes:**
Airborne high spatial/spectral resolution (4-20m, 224 bands) 
MODTRAN-based model 
Simultaneous estimation of water vapor and liquid water content
Homogeneous source + Wide areas coverage
Accepted as a primary data source for retrieval of physically based water features (Gamon et al., 1993; Green et al., 1993; Roberts et al., 1997; Ustin et al., 1998; Serrano et al., 2000; Sims and Gamon, 2003, Cheng et al. 2006a and 2006b)

Make the point it works well because a wide range of values from grasslands to agricultural sites
Also mention that the large offset could be because three week difference between field data and AVIRIS adquisition
With different soil cover
Mention Jasper Ridge
Vapor absorption different pick and liquid:A band at ~1650 nm and ~2215 nm capture SWIR reflectance where changes have a high negative correlation directly related to water content (Bowman 1989; Cohen 1991; Hunt et al. 1987; Jackson and Ezra 1985; Ripple 1986; Thomas et al. 1971; Tucker 1980). These bands are located in the intermediate water absorption features, instead of the major water absorption peaks, at 1453 and 1930 nm, where absorption by atmospheric water vapour usually limits observations of the ground (Schowengerdt 1997) 
and also it works well because a wide range of climatic conditions (seasonal differences)

## Validation: CWC

- AVIRIS to MODIS

**Notes:**
Only natural vegetation
We have validated only for the USA
Explain which ecoregions.
In bewtween different ecoregion
Different time of the year, spring, summer and early fall
Yosemite and Green Ridge are mainly forest with higher CWC
Table Mointain and SMEX, Arizona are a mix.

## Validation: CWC

|  | Shrub | Forest | Grass | All |
| --- | --- | --- | --- | --- |
| m | 1.18 | 1.67 | 1.47 | 1.47 |
| b | -0.11 | -0.20 | -0.14 | -0.15 |
| N | 3614 | 5747 | 2886 | 12247 |
| r2 | 0.59 | 0.61 | 0.75 | 0.71 |
| P-value | < 0.0001 | < 0.0001 | < 0.0001 | < 0.0001 |
| RMSE | 0.028 | 0.080 | 0.035 | 0.063 |

**Notes:**
About the RMSE one.The only thing I can think of is that the NN outputs less but larger errors, and this makes the RMSE going higher. Does it make sense ? Could this be due to the fact that we use a unique RT model for heterogeneous conditions ?
Better tahn multiple linear regression 

## Implementation plan: CWC

- Runs under matlab
- Downloadable under login
- Inputs
- MODIS Surface reflectance 8 day composite
- MODIS land cover product
- Calibration for each vegetation type
- Output
- CWC in binary ENVI format

**Notes:**
Runs under matlab
2 min and 5 sec in Pentium 2.13 GHz 1 GB RAM
Executes outside matlab
Downloadable under login
Inputs
MODIS Surface reflectance 8 day composite Terra (MOD09A1) or  Aqua (MYD09A1)
MODIS land cover product MOD12Q1 reclassified
Artificial Neural Network trained with the model
Calibration for each vegetation type
Output
CWC in binary ENVI format

Gain and offset for each vegetation type
You could even put other vegetatition types.
Legal as long as people downloads the program under 
I would also mention the possibility of more accuretely calibrate the model for more refined cover classes

## Implementation plan: CWC

**Inputs**

**CWC**

**Load hdf**

**Reshape**

**Load land cover**

**Vegetation indexes**

**Subset**

**Run ANN with RTM**

**Writes CWC ENVI**

**Notes:**
Do you think to semplify it ? it is not very readable. I would try to make the descriptions more schematic

## Study cases: CWC

**Notes:**
ultitemporal change in CWC for 2005 for natural vegetated areas in the USA. Each month is depicted by a single image composite after masking areas covered by snow, clouds, ice and land cover types not considered here.

## Study cases: CWC

**Notes:**
Marco: About the forest affair; yes, I merged all of the classes in a big one. Yes, this could give problems in wintertime, but on the other side I could also expect that areas without green vegetation will give CWC values out of the considered range.What do you think ?
So it means areas with snow and no leaves will not be in the range of good CWC values.

## Study cases: CWC

## Study cases: CWC

**2000**

**2001**

**2002**

**2003**

**2004**

**2005**

**2006**

**2007**

**Deviation from mean CWC (%)**

- <-80
- -80 to -60
- -60 to -40
- -40 to -20
- -20 to 0
- 0 to 20
- 20 to 40
- 40 to 60
- 60 to 80
- >80

- State of California boundaries

## Future research: CWC

- Testing Terra versus Aqua CWC estimates
- Gathering CWC datasets for calibration
- Generation of CWC also for agriculture
- Differentiation in the model by forest and region
- Test models developed for discontinuous canopies

**Notes:**
Testing Terra versus Aqua CWC estimates in relation to different viewing angles, CWC morning/afternoon changes and existing problems in Aqua ́s band 6 band and Terra ́s band 5
Gathering CWC datasets for calibration in Australia, Asia, Africa and Europe
Generation of CWC also for agricultural land cover type.
Differentiation in the model of evergreen and broadleaves forest and ecorigions
Testing models develop for discontinuous canopies, including additional information on for example tree height

And for climatic-ecoregional areas  (like tropical, boreal and mediterranean for example) ?

## A fuel type map product?

**Notes:**
Example for Mediterranean Ecosystems

## A fuel type map product?

- Local scale
- Fire behavior modeling
- Fire prevention
- Fire assessment
- Regional-continental scale
- Biogeochemical cycling modeling

**Notes:**
Example for Mediterranean Ecosystems
One solution is use of Lidar. 3D models BUT is really expensive and only possible at local scales. Although USGS is planning on flying the entire USA with LIDAR.
Integration with Landsat is a solution. But only possible at local regional scale

## A fuel type map product?

- (Riaño et al., 2003)

- (Morsdorf et al., 2003)

- (Wulder et al., 2003)

**Notes:**
Example for Mediterranean Ecosystems
One solution is use of Lidar. 3D models BUT is really expensive and only possible at local scales. Although USGS is planning on flying the entire USA with LIDAR.
Integration with Landsat is a solution. But only possible at local regional scale

## A fuel type map product?

- Local scale
- Airborne Lidar data
- Regional scale
- Airborne Lidar fused with Landsat data
- Continental scale
- Lidar fused with MODIS data

**Notes:**
One time investment
Actualization every 3 years or so

## ICESat GLAS 
Lidar:
Footprint 60-70 m
Vertical Accuracy: 1m
DESDynI
InSAR
Lidar
Footprint 25 m
Vertical Accuracy: 1m

- ICESat GLAS
- Lidar:
- Footprint 60-70 m
- Vertical Accuracy: 1m
- DESDynI
- InSAR
- Lidar
- Footprint 25 m
- Vertical Accuracy: 1m

**Notes:**
It is at 590 km
Orientado a la medición de movimientos en los casquetes glaciares, desarrollo vertical de las nubes y altura de la vegetación.
0.0039 sec takes a pulse to come back 254.23 Hz is the maximum posible frequency	
Still acquiring data

Reclassification of modis land cover map with integration of lidar data. Problem: Large footprint

Along with ICEsat discussion, mention the plans for the DESDynI satellite (the combined radar and lidar) as another satellite with some support for this work. The DLR Terra SAR-X
is good for land cover mapping in cloudy tropics but wavelength is too short (~2cm) to penetrate forest to get structure.
If you want, I can try to bullet-ize your slides...

DESDynI: A dedicated U.S. InSAR and LIDAR mission optimized for studying hazards and global environmental change.
The sensors are: 1) an L-band Interferometric Synthetic Aperture Radar (InSAR) system with multiple polarization, and 2) a multiple beam lidar operating in the infrared (~ 1064 nm) with ~ 25 m spatial resolution and 1 m vertical accuracy.