# Advances in remote sensing of cultivated landscapes at LISAH

Denis Feurer, Cécile Gomez, Jean-Stéphane Bailly, Guillaume Coulouma,

Philippe Lagacherie, Frédéric Jacob, Laurent Prevot, Damien D. Raclot

► To cite this version:

Denis Feurer, Cécile Gomez, Jean-Stéphane Bailly, Guillaume Coulouma, Philippe Lagacherie, et al. Advances in remote sensing of cultivated landscapes at LISAH. Journées CRITEX, EQUIPEX - Investissements d'Avenir, Jan 2016, Rennes, France. 2016. [hal-01381254](https://hal.science/hal-01381254)

HAL Id: hal-01381254

https://hal.science/hal-01381254

Submitted on 14 Oct 2016

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# Advances in remote sensing of cultivated landscapes at LISAH

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**Abstract:** In order to propose new and sustainable methods for cultivated areas management, the French INRA-IRD-Supagro laboratory for the study of Soil-Agrosystem-Hydrosystem Interactions (LISAH) develops tools for modelling fluxes (water, soils, solutes) based on a fine characterisation and observation of the landscape mosaics and of its dynamic (field limits, networks, structures, vegetation, agricultural practices, topography, etc.). Such observations require to develop innovative acquisition and processing methods. This poster presents some of these methods concerning different scales and techniques.

## I. Digital Mapping of Soil properties

**Objective:**

Mapping of soil properties over the Lebna catchment (250km²), from the surface to the sub-surface

**Approach in 3 steps:**

1. **1st:** Vis-NIR hyperspectral imaging data were used to map key topsoil properties over bare soils of Lebna catchment using multivariate models (Gomez et al., 2012).

2. **2nd:** These topsoil properties estimations were used as covariates in geostatistical methods to produce entire topsoil properties maps over Lebna catchment (see illustration).

<figure><img src="image_3.png" /></figure>

3. **3rd:** Topsoil properties estimations were used in pedotransfer functions to map sub-surface soil properties (Lagacherie et al., 2013).

**Results:** Vis-NIR hyperspectral imaging data and Digital Soil Mapping methods can be used to map key soil properties, used by soil surveyors to describe soil types, related to soil erosion processes...

**References:** Gomez, C., Lagacherie, P., & Bacha, S. (2012). Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in the Cap-Bon Region, Tunisia. In B. Minasny, B. Malone, & A. B. McBratney (Eds.), Digital Soil Assessment and Beyond (pp. 387-392).

Ciampalini, R., Lagacherie, P., Monestiez, P., Walker, E., & Gomez, C. (2012). Co-kriging of soil properties with Vis-NIR hyperspectral covariates in the Cap Bon region (Tunisia). In B. Minasny, B. Malone, & A. B. McBratney (Eds.) Digital Soil Assessments and Beyond (pp. 393-398).

Lagacherie, P., Sneep, A.-R., Gomez, C., Bacha, S., Coulouma, G., Hamrouni, M. H., & Mekki, I. (2013). Combining Vis-NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia). Geoderma, 209-210(0), 168-176. doi:http://dx.doi.org/10.1016/j.geoderma.2013.06.005

## II. Landscape structures mapping

**Objective:**

Mapping landscapes structures which have an impact on hydrological fluxes

**Approach :**

<figure><img src="image_4.png" /></figure>

* LiDAR DEM analysis to detect topographic discontinuities

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* DEM-driven stochastic modelling to interpolate the detected lines and remove false detections

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a. 12 LiDAR profiles with ground truth and aerial views
b. principle of Haar wavelet decomposition and classification
c. result

<figure><img src="image_7.png" /></figure>

Stochastic modelling of ditches network

a. network correction approaches : reconstruction and cut

b. application of the reconstruction

c. five equiprobable reconstructed networks

**References:** Bailly, J.; Lagacherie, P.; Millier, C.; Puech, C. & Kosuth, P. (2008), 'Agrarian landscapes linear features detection from LiDAR: application to artificial drainage networks', International Journal of Remote Sensing 29(12), 3489-3508.

Bailly, J.; Levavasseur, F. & Lagacherie, P. (2011), 'A stochastic algorithm to reconstruct artificial drainage networks from incomplete remote sensing delineations', International Journal of Applied Earth Observation and Geoinformation 13, 853--862.

## Field observations

<figure><img src="image_8.png" /></figure>

## V. 3D modelling by photogrammetry and Structure-from-Motion (SfM)

**Objectives:**

Attaining a decimeter to centimeter scale description of landscape features at the watershed scale

**Approach :**

<figure><img src="image_9.png" /></figure>

* Lightweight image acquisition : light platforms (kite) and consumer grade cameras

<figure><img src="image_10.png" /></figure>

* High image redundancy and SfM approaches for processing

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

**Results:**

Operationnal method of image acquisition and 3D processing which allows to obtain decimetric to centimetric DEM on areas up to several km²

**Reference:** C. Corbane, D. Raclot, F. Jacob, J. Albergel, P. Andrieux, Remote sensing of soil surface characteristics from a multiscale classification approach, CATENA, Volume 75, Issue 3, 15 November 2008, Pages 308-318, ISSN 0341-8162, http://dx.doi.org/10.1016/j.catena.2008.07.009.

**Reference:** Feurer, D.; El Maaoui, M. A.; Planchon, O.; Boussema, M. R. & Pierrot-Deseilligny, M. *Kite-Borne photogrammetry for decimetric 3D mapping of several square kilometres areas* Proceedings of IEEE 2015 International Geoscience and Remote Sensing Symposium (IGARSS'15), 2015

## III. Gullies detection with THRS imagery

**Objective:**

Exhaustive mapping of gullies for hydrological modelling

**Approach :**

<figure><img src="image_13.png" /></figure>

* Contour detection from THRS satellite imagery

<figure><img src="image_14.png" /></figure>

* Filter combination and decision rules with ancillary data

<figure><img src="image_15.png" /></figure>

**Reference:** Desprats J.F., Raclot D., Rousseau M., Cerdan O., Garcin M., Le Bissonnais Y., Ben Slimane A.,Fouche J., Monfort-Climent D. (2013). Mapping linear erosion features using high and very high resolution satellite imagery. Land Degradation & Development, 24: 22-32. doi: 10.1002/ldr.1094.

<figure><img src="image_16.png" /></figure>

## Simulation and computing

## VI. Mapping evapotranspiration of vineyards

**Objective:** Mapping of vineyard daily evapotranspiration (ETR) over the Peyne catchment (65 km²)

**Approach :** 12 ETR maps produced from visible-NIR and thermal IR images (ASTER instrument, 90 m resolution) using two water stress indices : WDI (Moran et al., 1994) and S-SEBI (Roerink et al., 2000)

<figure><img src="image_17.png" /></figure>

Validation at the plot scale against eddy covariance ETR measurements (sites 6 and 7) and against ETR outputs of the HYDRUS-1D water transfer model (sites 1-7)

<figure><img src="image_18.png" /></figure>

**Results:**

Visible-NIR and thermal images allow for ETR mapping at the regional scale. ETR maps can be useful for assessing the available water content of soils

**Reference:** Galleguillos, M., Jacob, F., Prévot, L., Lagacherie, P. and French, A., *Comparing two simplified models for inferring daily evapotranspiration from ASTER over a Mediterranean vineyard watershed*. Remote Sensing of Environment, 2015, 6 (115), 1326-1340

## VII. Mistigri mission design

**Objective:** Design of a high spatial and temporal resolution thermal satellite

<figure><img src="image_19.png" /></figure>

Surface temperature and from evapotranspiration retrieved airborne hyperspectral scanner

<figure><img src="image_20.png" /></figure>

Potential accessibility at ground in the case of (a) 1 satellite at 561 km or (b) 2 at 720 km

**Approach :**

Image simulation (radiative transfer modelling, airborne campaigns)

<figure><img src="image_21.png" /></figure>

Simulation of different spectral configurations with 4 or 3 bands and different positions in the thermal infrared spectrum

**Results:** Having the spectral sampling (bands, resolutions) of the LISAH team proposed a spectral configuration allowing for a 1K accuracy in temperature estimation

**Reference:** Lagouarde, J.-P.; Bach, M.; Sobrino, J. A.; Boulet, G.; Briottet, X.; Cherchali, S.; Coudert, B.; Dadou, I.; Dedieu, G.; Gamet, P.; Hagolle, O.; Jacob, F.; Nerry, F.; Olioso, A.; Ottlé, C.; Louis Roujean, J. & Fargant, G. *The MISTIGRI thermal infrared project: scientific objectives and mission specifications*. International Journal of Remote Sensing, 2013, 34, 3437-3466