QGIS and Applications in Agriculture and Forest

Nicolas Baghdadi, Clément Mallet, Mehrez Zribi, Nicolas Baghdadi, Clément Mallet, Mehrez Zribi

• Publisher: Wiley-ISTE
• Year: 2018
• Print ISBN:
  9781786301888
• Edition: 1
• eBook ISBN:
  9781119490890
• Topic: Biological Sciences
• Subtopic: Environmental Science
• Index: Biological Sciences

These four volumes present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The volumes detail applications of remote sensing over continental surfaces, with a first one discussing applications for agriculture. A second one presents applications for forest, a third presents applications for the continental hydrology, and finally the last volume details applications for environment and risk issues.

1
Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas

1.1. Context

The spatio-temporal monitoring of soil moisture in agricultural areas is of great importance for numerous applications, particularly those related to the continental water cycle. The use of in situ sensors ensures this monitoring but the technique is very costly and can only be carried out on a very small agricultural area, hence the importance of spatial remote sensing that now enables large-scale operational mapping of soil moisture with high spatio-temporal resolution.

Radar data have long been used to estimate and map the surface soil moisture of bare soils [BAG 16b]. In fact, physical, empirical and semi-empirical models were developed to invert the radar signal to monitor the soil moisture at different spatial scales (intra-plot scale, plot scale, on grids of a few hundred m² to a few km²). Over vegetated cover surfaces, the coupling of radar and optical data is often necessary to estimate the surface soil moisture. Optical data are complementary to radar data, and their interest lies in their potential to estimate the physical parameters of vegetation, for example Leaf Area Index (LAI) from satellite indices such as the Normalized Difference Vegetation Index (NDVI). These parameters make it possible to evaluate the contribution of the vegetation in the backscattered radar signal, to extract the soil contribution and to finally invert it in order to estimate the surface soil moisture.

To map the soil moisture in the case of vegetation cover, most studies use the semi-empirical Water Cloud Model (WCM) developed by Attema and Ulaby [ATT 78]. Generally, in this model, the total backscattered radar signal is modeled as the sum of (1) the backscattered signal from the soil multiplied by the two-way attenuation and (2) the direct reflected signal from the vegetation. In most studies, the contribution of vegetation has been expressed in terms of one physical parameter of vegetation (biomass, LAI, water content, vegetation height). The soil contribution is generally modeled as a function of soil moisture and surface roughness (for given instrumental parameters: incidence angle, wavelength and polarization). It can be simulated using a physical radar backscattering model (in particular the Integral Equation Model (IEM) [FUN 94]), or a semi-empirical backscattering model (e.g. Dubois [DUB 95] or Baghdadi [BAG 16a] models).

The objective of this chapter is to show how to map the surface soil moisture over agricultural plots (summer and winter crops) and grasslands using the free and open-source software QGIS (Quantum Geographic Information System), by coupling radar (Synthetic Aperture Radar (SAR)) and optical images acquired at high spatial resolution (~10 m × 10 m).

1.2. Study site and satellite data

The study site located near Montpellier in the South of France (Figure 1.1) is an agricultural area (15 km × 15 km). Figure 1.1 shows the layout, made using QGIS, of a satellite image acquired over the study site by Sentinel-2A (S2A).

QGIS functionality for layout:

- Project > New Print Composer >…

1.2.1. Radar images

Two Sentinel-1A (S1A) radar images in...

Table of contents

• Cover
• Table of Contents
• Title
• Copyright
• Introduction
• 1 Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas
• 2 Disaggregation of Thermal Images
• 3 Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB
• 4 Land Cover Mapping Using Sentinel-2 Images and the Semi-Automatic Classification Plugin: A Northern Burkina Faso Case Study
• 5 Detection and Mapping of Clear-Cuts with Optical Satellite Images
• 6 Vegetation Cartography from Sentinel-1 Radar Images
• 7 Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park
• 8 Physiognomic Map of Natural Vegetation
• 9 Object-Based Classification for Mountainous Vegetation Physiognomy Mapping
• List of Authors
• Index
• Scientific Committee
• End User License Agreement