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• Sensor technology developments drove advances in biophysical estimation techniques. • CubeSat constellations will increase VHR data accessibility to various users. • Sentinel-2 red-edge bands present prospects for operational crop stress detection. • RF is prominently used in the literature despite new developments in algorithms. • Retrieval models developed in Mediterranean areas do not apply to semi-arid areas. This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground-breaking events can be identified, i.e., the availability of Sentinel-2 and the SuperDove constellation. The two provide high temporal-high spatial resolution data relevant for site-specific management and super-spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel-2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high-resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red-edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress-related crop parameters to aid time-sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi-arid areas and China's temperate continental semi-humid monsoon climates. Therefore, locally-calibrated RTM models incorporating crop-type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data-scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under-studied areas. [ABSTRACT FROM AUTHOR]

The COVID-19 spread very quickly around the world following its discovery in China, in December 2019. Lockdowns implemented in China and the Global North to control the propagation of the virus and to save human lives have resulted in a global recession. The transmission of the recessionary effects from the Global North to the Global South is reflected in the decline in sub-Saharan Africa's (SSA) GDP and the associated increase in poverty. The purpose of this paper is to illustrate how the recession induced in China and the Global North by COVID-19 lockdowns may have had indirect effects on SSA mortality that are higher than those directly attributed to the pandemic itself. Our methodology relies on a three-step relationship: (i) the impact of lockdowns on the recession in the North, (ii) the impact of the recession in the North on income in SSA countries, and (iii) the impact of a decline in income on mortality in SSA. We show that COVID-19-induced lockdowns in the Global North, through the severe recessions they induced in the Global South, resulted in the transfer of between 538,000 and 679,000 deaths in one year to SSA, including the deaths of 140,000 to 177,000 children aged 0–5 years. This corresponds to a 6–7% increase in the crude death rate and a 5–6% increase in under-5 mortality. These figures are much higher than the number of deaths directly attributable to COVID-19 in SSA. Thus, policymakers must not lose sight of the indirect excess mortality caused by global economic recession triggered by the pandemic. Our results reveal the need to increase the resilience of SSA countries to exogenous shocks, including COVID-19, which, in addition to increasing poverty, may induce excessive mortality due to the high sensitivity of mortality in SSA countries to economic recession. • Theoretical model of COVID-19 deaths transfer from the Global North to the Global South. • Lockdowns in the Global North generated recession in the Global North. • Spillover effects of the recession from the Global North to sub-Saharan Africa (SSA). • Focus on the impact of income declines on mortality in SSA. • The indirect mortality through recessions in SSA would be higher than the direct mortality. [ABSTRACT FROM AUTHOR]