Biome specific biomass retrieval Thuy Le Toan

Stephane Mermoz, Alexandre Bouvet, Ludovic Villard, Milena Planelld, Thierry Koleck

The CCI Biomass team &

‘CCI Biomass’ objectives and constraints

● Constraint 1: sub optimal EO

data of the epoch 90’s-2019, prior to the missions more adapted to AGB retrieval : GEDI, NISAR, BIOMASS

● Constraint 2: consistency of data and products for change estimates

● Constraint 3: use of the heritage from Globbiomass

Radar Mid 1990s JERS SAR

ERS SAR

2002-2011 ALOS PALSAR (25 m mosaics)

ICESAT GLAS

2015-2017 ALOS-2 PALSAR-2

(25 m mosaics)

GEDI

2018 onwards

Sentinel-1

SAOCOM

NISAR

Optical

MO

DIS

Sen

tinel

-2

Land

sat

BIOMASS

NOVASAR

MOLI

ICESAT-2

TANDEM-X

Lidar Epoch

ENVISAT ASAR

To improve Globbiomass 2010 map and to produce global biomass map for late 90’s to 2019

Tasks overview ● To assess Globbiomass 2010 map at data rich regions, based on

high quality in situ data including lidar-derived AGB maps ● To Investigate alternative approaches that are optimised to the physical

and ecological conditions of the different biomes/forest types - Approaches should also be applicable to data provided by future missions - Benefit from new datasets (CCIs, new scientific results, new in situ data..), from improved understanding of the EO data wrt forest ecophysiology. ● To develop and test such approaches at test regions, using high

quality in situ data, EO data and CCI datasets

Biomes / Forest Types to be considered � Differences in requirements, linked to scientific or ‘thematic’ use, in terms of: spatial and temporal resolution, accuracy, uncertainties, coverage (local, national, regional global.. � Differences in characteristics of the forests that lead to specific interaction with the

incoming EO signams 1. Wet Tropics 2. Dry tropics 3. Boreal Temperate 4. Mangrove forests 5. Inundated forest 6. Plantations

Where global optimisations are expected ?

1. Residual topographic effects : from the use of ALOS mosaics To be investigated: - More advanced topographic correction approach on ALOS SLC dual pol data (similar to Villard & Le Toan, 2014). Need a collaboration with JAXA, to have data in priority for the tropical belt (Investment for NISAR, BIOMASS) - To add eco-climate information in the retrieval approach (e.g. AGB decreases with land elevation..). 2. Effect of environment, in particular soil moisture - Use of CCI SM, or SM from SMOS data 3. Improved segmentations in ‘ecoregions’

(Edinburgh)

Biomes/Forest Types specific improvements

Wet Tropics: The most important biome in terms of Carbon, and AGB estimates are needed for carbon cycle, climate, MRV of REDD+… Currently the biggest uncertainties in AGB esimates using EO data are in wet Tropics Use of Lidar-based AGB datasets to investigate where improvements are needed 1. From TropiSAR, AfriSAR campaigns (Labrière et al., 2018) 2. From RDC map (Xu and Saatchi, 2017)

Lidar-based AGB map Globbiomass AGB map

AfriSAR Lopé, Gabon (Labrière et al., 2018)

● Nouragues, French Guiana

AGB map of RDC

Liang Xu, Saatchi et al., 2017

Airborne lidar data in RDC used to produce AGB map Liang Xu, Saatchi et al., 2017

Large difference at high biomass

AGB at RDC: Comparison Globbiomass and Lidar based map Xu&Saatchi (2017)

Biomes/Forest Types specific improvements

Wet Tropics: Approaches to be considered : ● For dense forest with high AGB, the radar backscatter exhibits a decreasing

trend with AGB, due to increasing attenuation (Mermoz et al., 2016)

Approach for high AGB in dense forests at CESBIO

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2. Experimental data in Central African Republic Example of a simulation of the effect of wood density ρ (linked to tree species)

L-band ρ = 0.575, 0.6, 0.625 ht/h = 0.75

1..Electromagnetic modelling

Confirmation of the decreasing trend of the radar backscatter after ~200 t/ha

Using Lidar-based AGB map (Xu and Saatchi 2017)

Mapping of high AGB forests at low resolution (500 m) using the HV decreasing trend. A test to be pursued

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0

AGB map at 500m resolution-Congo basin 500 t/ha

AGB

Biomes/Forest Types specific improvements

Wet Tropics: Approaches to be considered : ● For dense forest with high AGB, the radar backscatter exhibits a decreasing

trend with AGB, due to increasing attenuation (Mermoz et al., 2016) Æ resolution to be degraded to 500m Æ ALOS-PALSAR data issue for 2015, 2018.. ● The ‘saturation’ level of AGB with L-band SAR backscatter (ALOS) could be

extended with L-band passive microwave (SMOS), due to one way versus two-way attenuation

Æresolution of 25 km, possibly to 10 km

SMOS L-band Vegetation Optical Depth sensitive to AGB up to 200-300 t/ha

SMOS L-band Vegetation Optical Depth sensitive to AGB for different LC

SMOS-based 25km AGB – ITT ESA SMOS VEGETATION

Cf. Jean-Pierre Vigneron

Comparison SMOS VOD with Lidar-based AGB in RDC (Xu and Saatchi)

Issues on tropical wet forest

• The available EO data to date are sub-optimal for the retrieval of biomass in tropical forests

• The BIOMASS mission aims at providing AGB of tropical forests after 2022

• GEDI and NISAR expected to improve the retrievals, after 2019 (and 2022)

• For AGB maps dated back to 10-15 years, consistent data source could be passive microwave data (SMOS)

Use for global C cycle and climate models ? Methods to increase spatial resolution using LULC, eco- climate datasets? Are such datasets useful for other objectives?

Using ALOS-PALSAR , the AGB map of CESBIO is limited at 80 t/ha