Soil moisture is a fundamental climate variable sustaining the terrestrial biosphere. Whereas flood-recharged soil moisture (FRSM) is an important input flux in semi-arid floodplain ecosystems, its spatio-temporal dynamics is not fully understood due to lack of adequate field data. While existing active remotely sensing data are valuable to understand soil moisture, a trade-off between high temporal against coarse spatial resolutions limit their utility at local scales. In this study, we extracted linear backscatter coefficient sigma nought (σ 0) and time series data from 91 pre-processed 10 m multi-temporal dual Sentinel-1 images. The data was collected from both inside and outside the flooded zone in a semi-arid area in northern Zimbabwe. To characterize FRSM anomaly, lag and memory, we built a hybrid deep learning long short-term memory autoencoder (LSTMAE) model based on a recent flood event which was subsequently evaluated using mean absolute error (MAE) and root mean squared error (RMSE) loss metrics using an independent validation dataset. Validation results showed that both VV and VH-polarized data effectively detected FRSM positive anomaly with very small MAE (0.0799σ 0; 0.0191σ 0) and small RMSE (0.0967σ 0, 0.0250σ 0) respectively. In the flood zone, the LSTMAE model detected three positive anomalies for both polarizations. Also, our study established that the VV LSTMAE model was effective in detecting subtle positive anomalies while VH depicted the longest lag and memory at a local scale. The study concludes that the extraction of σ 0 on Sentinel-1 time series data offers a good understanding of localised FRSM characteristics within semi-arid floodplains.

Following the invasion of Africa by the oriental fruit fly, Bactrocera dorsalis, Classical biological control (CBC) have been exploited as a safer alternative for its suppression by the introduction and release of the koinobiont endoparasitoid, Fopius arisanus. Although, the parasitoids have been released in several African countries, its extent of dispersal resulting in numbers of beneficiaries fruit growers has not yet been elucidated. This paper proposes an innovative multi-level CBC impact analysis combining cellular automata (CA) and ecological niche models to estimate parasitoid dispersal ranges and household beneficiary populations. Firstly, we provide a generic systematic methodological approach using CA rules incorporated into species distribution. Secondly, the model was used to estimate the dispersal range of the parasitoid based on the life history and bioecology of the host insect (fruit fly) and the parasitoid. Finally, the parasitoid dispersal coverage was mapped across fruit crops attacked by the target fruit fly, and the number of households that have benefitted from the parasitoids release programme was extracted from the area of the dispersal (first in Kenya), and the data was projected across all countries where the parasitoid have been released and validated. In Kenya, the model showed that F. arisanus had covered a total area of 50.34 km2 from the initial point of open field release; and at the continental scale, the model predicted that the parasitoid had covered a total area of 229.97 km2. The model estimated that 351,855 and 3,731,330 households have directly benefited from the release of F. arisanus between 2013 to 2018 in Kenya and at the continental level, respectively. The study’s outcome is appropriate for providing feedback information on the impact of CBC to government and development partners to make informed decisions on technological interventions.