Mali: Nanposela, Kani Sukumba

Overview

  • Crop identification and acreage estimation: Improve satellite recognition of dominant crop species (cotton, maize, millet, sorghum, peanut) in smallholder fields, using VHR optical satellite images as well as C-Band and X-Band SAR data as input.
  •  Residue and Tillage mapping: n/a
  •  Soil moisture: n/a
  •  Crop biophysical variables (LAI): i/ Monitor canopy development states for dominant crops including fraction vegetation cover, planting density, LAI, canopy height and density. ii/ Improve seasonal yield and biomass prediction for crops and forage. The focus here will also be on microwave data as input.
  •  Other: n/a

The mapping resolution is sub-meter to 5-m. The timeliness (with regards to growing season) is 01 April- 30 November. Research activities on crop identification and acreage estimation will be linked to those on crop biophysical variables characterization.

Project Objectives

Estimating Crop Area

Operational Implementation Plan

Project Overview Implementation Plans Site Description Specific Project Objectives & Deliverables In Situ Observations EO Data Requirements Project Reports Study Team View/Print All JECAM – Sidebar Image Operational Implementation Plans 1. Crop identification and acreage estimation: 2014: Algorithms development and calibration in Kani-Sukumba (Mali) Land use / land cover survey for 150 smallholder fields (30 fields for each of the 5 dominant crops, each field at least 1ha in size) Compilation of historical (2002-2013) land use land cover data into consolidated database (depending on the year of survey, data covers between 150-500 fields) Acquisition of archive TerraSAR-X and Radarsat2 data (if available) and tasking of new data acquisitions, with a target of at least 5 acquisitions for the season (field preparation/pre-sowing stage, vegetative stage, flowering stage, grain-filling stage, post-harvest). 10-day frequency preferred. Algorithm development in partnership with competent ARIs (to be determined) 2015: Algorithms validation and crop area mapping in Sukumba, Kani, Nanposela (Mali) Same as 2014, plus: Forward application of algorithms developed in 2014 cropping season in Sukumba (same geography, different climate) including algorithm re-calibration Forward application of algorithms developed in 2014 cropping season in Kani, Nanposela (different geography, different climate) Peer-reviewed publications on libraries and methods

Field size measurement

Estimation of Biophysical Variables

Operational Implementation Plan

2014: Development of spectral, temporal and contextual (textural) libraries in Sukumba (Mali)

  • Detailed ground characterization of field-scale agronomic practices, environmental conditions (climate, soil), monitoring of canopy growth and collection of end-of-season yield and biomass data for 50 smallholder fields (10 fields for each of the 5 dominant crops, each field at least 1ha in size)
  • Tasking of TerraSAR-X and Radarsat2 data, with a target of at least 5 acquisitions for the season (field preparation/pre-sowing stage, vegetative stage, flowering stage, grain-filling stage, post-harvest). 10-day frequency preferred.
  • Concurrent collection of decadal (10-day) multispectral (VIR/NIR) data from WorldView2/3 and UAV (both NIR camera on-board of fix wings UAV and TetraCaM on-board of octocopter vehicle) under BMGF Remote Sensing Learning Package grant.

2015: field-to-landscape scale yield and biomass prediction for Sukumba (Mali)

  • Same as 2014, plus:
  • Calibration of appropriate yield and biomass prediction model for local conditions
  • Testing of multi-source satellite data assimilation for improved end-of-season yield and biomass predictions, in partnership with Univ. Catholique de Louvain

 5. Other: n/aThe current project phase is research. The proposed project builds on 2002-2013 research investments in the locality of Sukumba under projects ‘Carbon From Communities’ (NASA, 2002-2004), ‘Soil Management CRSP’ (USAID, 2002-2007), ‘Seeing Is Believing – West Africa’ (AgCommons/BMGF, 2009-2010) and Dryland Systems CRP (CGIAR, 2013-present). It also leverages the upcoming ‘Imagery for Smallholders – Activating Business Entry points and Leveraging Agriculture’ (ISABELA) project (BMGF, 2014-2015).

Biophysical Variables
  • LAI (Leaf Area Index)
  • Biomass

Forecasting Agricultural Variables

Operational Implementation Plan

Agricultural Variables (large scale)
  • Yield

Site Description

Landscape TopographyVariable
Typical Field Size1.4±1.2 ha
Climatic ZoneTropics, warm
Crop Details

Cotton (Normal):
Calendar: April - November
Typical Rotation: cotton – millet/sorghum cotton – maize – millet/sorghum cotton continuous

Maize (Normal):
Calendar: April - October
Typical Rotation: cotton – maize – millet/sorghum

Millets (Normal):
Calendar: April - December
Typical Rotation: cotton – millet/sorghum cotton – maize – millet/sorghum

Sorghum (Normal):
Calendar: April - December
Typical Rotation: cotton – millet/sorghum cotton – maize – millet/sorghum

peanut (Normal):
Calendar: April - October

Soil Type & Texture

Inorganic:

  • Sandy Loam
Soil Drainage Class
  • Well drained
Irrigation Infrastructure
  • No irrigation (precipitation)
Other Site Specifications

In Situ Observations

EO Data

Optical Data Requirements

SAR Data Requirements

Passive Microwave Data Requirements

Thermal Data Requirements

Results

Documents and Files

Links to paper

Project Reports

Study Team

Team Leader

  • Name: Pierre Sibiry Traore
  • Affiliation: CGIAR
  • Affiliation Webpage: https://www.cgiar.org/
  • Position: Director
  • Email: p.s.traore@cgiar.org
  • Personal Webpage:
  • Phone number:
  • Postal Address:
  • Other Team Members