A COMPARATIVE ANALYSIS OF PLANETSCOPE AND SENTINEL SENTINEL-2 SPACE-BORNE SENSORS IN MAPPING STRIGA WEED USING GUIDED REGULARISED RANDOM FOREST CLASSIFICATION ENSEMBLE
- 1International Center of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
- 2Department of Earth Sciences, University of Western Cape, Private Bag X17 Bellville 7535 South Africa
- 3Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
- 4RSS-Remote Sensing Solutions Gmbh, Dingolfinger Str. 9, 81673 Munich, Germany
Keywords: Feature selection, Food security, High spatial-temporal resolution, Nanosatellites, New generation sensors, Precision agriculture, Weed detection, Sub-Saharan Africa
Abstract. Weeds are one of the major restrictions to sustaining crop productivity. Weeds often outcompete crops for nutrients, soil moisture, solar radiation, space and provide platforms for breeding of pests and diseases. The ever-growing global food insecurity triggers the need for spatially explicit innovative geospatial technologies that can deliver timely detection of weeds within agro-ecological systems. This will help pinpoint maize fields to be prioritized for weed control. Satellite remote sensing offers incomparable opportunities for precision agriculture, ecological applications and vegetation characterisation, with vast socioeconomic benefits. This work compares and evaluates the strength of Sentinel-2 (S2) satellite with the constellation of Dove nanosatellites i.e. PlanetScope (PS) data in detecting and mapping Striga (Striga hermonthica) weed within intercropped maize fields in Rongo sub-county in western Kenya. We applied the S2 and PS derived spectral data and vegetation indices in mapping the Striga occurrence. Data analysis was implemented, using the Guided Regularised Random Forest (GRRF) classifier. Comparatively, Sentinel-2 demonstrated slightly lower Striga detection capacity than PlanetScope, with an overall accuracy of 88% and 92%, respectively. The results further showed that the VNIR (Blue, Green Red and NIR) and the Atmospheric resistance Vegetation Index (ARVI) were the most fundamental variables in detecting and mapping Striga presence in maize fields. Findings from this work demonstrate that Sentinel-2 data has the capability to provide spatial explicit near real-time field level Striga detection – a previously daunting task with broadband multispectral sensors.