Browsing by Author "Omar, M. M."

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Prediction of soil texture using remote sensing data. a systematic review
(Department of Soil and Geological Sciences, 2024-09) Mgohele, R. N.; Massawe, B. H. J.; Shitindi, M. J.; Sanga, H. G.; Omar, M. M.
Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water inﬁltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be time- consuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the ﬁve most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artiﬁcial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction.
Salt-affected soils in Tanzanian agricultural lands: type of soils and extent of the problem
(Taylor & Francis Group., 2023) Omar, M. M.; Shitindi, M. J.; Massawe, B. H. J.; Fue, K. G.; Meliyo, J. L.; Pedersen, O.
Salt-affected soils are a global challenge, affecting 1 billion ha of land, with 200 million ha found in Africa. The challenge brings adverse impacts on agricultural productivity, food security, environ mental sustainability, and food security. In Tanzania, more than 2 million ha of land are salt- affected, of which 1.7 million ha are saline soil and 0.3 million ha are sodic soil. To cope with this threat, it is necessary to have a thorough understanding of its extent (coverage), existing types, and available management strategies. This review presents a comprehensive account of the challenges and opportunities of salt-affected soils in Tanzania and examines management options that have been observed to increase agricultural productivity in rice-growing areas. A systematic review of relevant articles published in databases was carried out using PRISMA guidelines and flowcharts. This review highlights the origin, extent, types, and various techniques for alleviating salt-affected soil problems. It also emphasize on the use of inorganic and organic amendments, salt-tolerant varieties, irrigation water quality, and drainage infrastructure. We revealed that farmers, use burned and unburned rice husks, sawdust, gypsum, and farm yard manure (FYM) as copping mechanisms. Furthermore, there have been continuing efforts to develop salt-tolerant rice vari eties, coupled with maintenance of irrigation infrastructure and site-specific soil management options, as appropriate solutions to tackle salt issues. Given the light of existing data, the review recommends using RS and GIS for updating information on salt-affected soils, particularly in irrigated areas, as an essential component of sustainable management and preventing further loss of agricultural land.