A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data

Bester Tawona Mudereri; Elfatih Mohamed Abdel-Rahman; Timothy Dube; Saliou Niassy; Zeyaur Khan; Henri E Z Tonnang; Tobias Landmann

doi:10.1016/j.scitotenv.2020.143151

A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data

Sci Total Environ. 2021 Mar 25:762:143151. doi: 10.1016/j.scitotenv.2020.143151. Epub 2020 Oct 21.

Authors

Bester Tawona Mudereri¹, Elfatih Mohamed Abdel-Rahman², Timothy Dube³, Saliou Niassy⁴, Zeyaur Khan⁴, Henri E Z Tonnang⁴, Tobias Landmann⁵

Affiliations

¹ Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; International Centre of Insect Physiology and Ecology (icipe), P. O. Box 30772, 00100, Nairobi, Kenya. Electronic address: bmudereri@icipe.org.
² International Centre of Insect Physiology and Ecology (icipe), P. O. Box 30772, 00100, Nairobi, Kenya; Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan.
³ Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.
⁴ International Centre of Insect Physiology and Ecology (icipe), P. O. Box 30772, 00100, Nairobi, Kenya.
⁵ International Centre of Insect Physiology and Ecology (icipe), P. O. Box 30772, 00100, Nairobi, Kenya; Remote Sensing Solutions Gmbh, Dingolfinger Str. 9, 81673 Munich, Germany.

PMID: 33143922
DOI: 10.1016/j.scitotenv.2020.143151

Abstract

Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13^th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data. The gathered in-situ hyperspectral data were resampled to the spectral waveband configurations of S2 and three representative EMs were inferred, namely: (1) Striga, (2) crop and other weeds, and (3) soil. Overall classification accuracies of 88% and 78% for the pixel-based cropland mapping and subpixel Striga detection were achieved, respectively. Furthermore, an F-score (0.84) and a root mean square error (0.0075) showed that the MESMA subpixel algorithm provides plausible results for predicting the relative abundance of Striga within each S2 pixel at a landscape scale. The capability of MESMA together with a cropland classification hierarchical approach was thus proven to be suited for Striga detection in a heterogenous agroecological system. These results can be used to guide in the adaptation, mitigation, and remediation of already infested areas, thereby avoiding further Striga infestation of new croplands.

Keywords: Africa, croplands; Endmember selection; Google Earth Engine; Invasive weeds; Spectral mixture modeling.

MeSH terms

Kenya
Plant Weeds
Striga*