A Remote Sensing Based Approach for Optimizing the Sampling Strategies in Crop Monitoring and Crop Yield Estimation Studies

  • Babacar NdaoEmail author
  • Louise Leroux
  • Abdoul Aziz Diouf
  • Valerie Soti
  • Bienvenu Sambou
Conference paper
Part of the Southern Space Studies book series (SOSPST)


In the context of a growing population and impacts of climate changes on agricultural lands and food security, improving crop monitoring of smallholder farming systems is crucial to reach the Sustainable Development Goal (SDG) 2 «Zero hunger». There is an increasing scientific interest in understanding the effects of the biodiversity induced by tree-based agricultural systems on agricultural production and human nutrition. To elucidate the contribution of landscape diversity to agricultural productivity, this study proposes a sampling strategy guided by landscape heterogeneity to account for tree diversity. We propose a simple and reproducible approach based on remote sensing, object-based segmentation and unsupervised classification. The study was conducted in the Senegalese Peanut Basin characterized by a tree-based agricultural system dominated by Faidherbia albida.

Assuming that agricultural landscapes with similar trees and crop cover composition will have similar phenological development, a multiresolution segmentation was performed on Sentinel-2 NDVI time series to obtain homogeneous landscape units. For each unit, landscape diversity proxies were derived from various geospatial data sources, namely vegetation productivity and its dynamic over the last 15 years, actual evapotranspiration, woody cover rates and soil type. Landscape units were classified using a hierarchical clustering based on the derived landscape diversity proxies to obtain a landscape heterogeneity gradient divided into four classes. Based on this classification, an optimized sampling strategy was produced to carry out an inventory campaign of tree biodiversity. More than 8000 trees including 41 species have been inventoried. The proposed approach will also be used to help in the choice of a crop fields network representative of the landscape diversity to improve the spatial representativeness of crop yield estimations.


Remote sensing Tree-based agricultural system Landscape heterogeneity Segmentation Landscape classification Sampling strategy 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Babacar Ndao
    • 1
    • 2
    Email author
  • Louise Leroux
    • 3
    • 4
  • Abdoul Aziz Diouf
    • 1
  • Valerie Soti
    • 3
    • 4
  • Bienvenu Sambou
    • 2
  1. 1.Centre de Suivi Ecologique (CSE)DakarSenegal
  2. 2.Institut des Sciences de l’Environnement (ISE), UCADDakarSenegal
  3. 3.CIRAD, UPR AIDADakarSenegal
  4. 4.AIDA, Univ Montpellier, CIRADMontpellierFrance

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