Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R²) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009–2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).

The trend and magnitude of actual evaporation across the phenophases of miombo woodlands are unknown. This is because estimating evaporation in African woodland ecosystems continues to be a challenge, as flux observation towers are scant if not completely lacking in most ecosystems. Furthermore, significant phenophase-based discrepancies in both trend and magnitude exist among the satellite-based evaporation estimates (i.e. Global Land Evaporation Amsterdam Model (GLEAM), moderate resolution imaging spectroradiometer (MODIS), operational simplified surface energy balance (SSEBop), and water productivity through open-access remotely sensed derived data (WaPOR)), making it difficult to ascertain which of the estimates are close to field conditions. Despite the many limitations with estimation of evaporation in woodlands, the development and application of the distributed temperature system (DTS) is providing deepened insights and improved accuracy in woodland energy partitioning for evaporation assessment. In this study, the Bowen ratio distributed temperature sensing (BR-DTS) approach is used to partition available energy and estimate actual evaporation across three canopy phenophases of the miombo woodland, covering the entire 2021 dry season (May–October) and early rain season (November–December) at a representative site in Mpika in Zambia, southern Africa. To complement the field experiment, four satellite-based evaporation estimates are compared to the field observations. Our results show that actual evaporation of the miombo woodland appears to follow the trend of the net radiation, with the lowest values observed during the phenophase with the lowest net radiation in the cool dry season and the highest values during the phenophase with peak net radiation in the early rainy season. It appears the continued transpiration during the driest period in the dormant phenophase (with lowest canopy cover and photosynthetic activities) may be influenced by the species-dependent adapted physiological attributes such as access to moisture in deep soils (i.e. due to deep rooting), plant water storage, and the simultaneous leaf fall and leaf flush among miombo plants. Of the four satellite-based evaporation estimates, only the WaPOR has a similar trend to the field observations across the three phenophases. However, all four satellite-based estimates underestimate the actual evaporation during the dormant and green-up phenophases. Large coefficients of variation in actual evaporation estimates among the satellite-based estimates exist in the dormant and green-up phenophases and are indicative of the difficulty in estimating actual evaporation in these phenophases. The differences between field observations and satellite-based evaporation estimates can be attributed to the model structure, processes, and inputs.

Complex African ecosystems such as the Miombo Woodland, with unique plant phenology, have evaporation dynamics that have not been investigated due to very few, if at all existant, flux tower observations. Furthermore, significant differences have been observed in satellite-based evaporation estimates in the Miombo Woodland especially in the dry season. Therefore, deciding which sattelite evaporation product to use in this ecosystem is difficult, as these products vary in many respects. In this study, the actual evaporation estimates for six satellite-based evaporation estimates are compared across Miombo Woodland phenophases in the Luangwa Basin, in southern Africa. In the absence of basin scale field observations, the actual evaporation estimated using the general water balance is used as reference, to which the six satellite-based evaporation estimates have been compared. Our results show significant variation in actual evaporation estimates in the water limited, high temperature and lower forest canopy cover and leaf chlorophyll conditions in the dormant phenophase. Lowest variation is observed in water abundant, high temperature, high leaf chlorophyll content and high forest canopy cover in the maturity/peak phenophase(s). Compared to the basin scale water balance actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. The results of underestimation at basin scale agrees with local field observations in a dense Miombo Woodland in the Luangwa Basin, which indicates that satellite-based evaporation estimates generally underestimate dry season (dormant phenophase) and early rain season (green-up phenophase) actual evaporation. The discrepancies in dry season satellite-based evaporation estimates may be cuased by the Miombo Woodland species’ phenological adaptation attributes such as: leaf fall, leaf flush, access to deep soil moisture and the within vegetation water storage, coupled with heterogenous plant species response to phenological stimuli. Therefore, it appears that satellite-based evaporation estimates using model structure, processes and inputs that are capable of capturing Miombo species dry season phenological interaction with climate are likely to have actual evaporation estimates closer to field conditions.

Attaining a “land degradation neutral world” by 2030, as envisaged by the United Nations (UN) 2030 agenda for sustainable development, sustainable development goal (SDG) number 15, requires accurate information for the implementation of targeted interventions. The Trends.Earth historical data set to support monitoring and reporting, and to track the impact of sustainable land management is only available up to the year 2021. However, combining the predictive ability of Cellular-Automata-Markov (CA-Markov) model(s) in Idris Selva and the Trends.Earth model could provide an insight into potential future land cover degradation. Therefore, this study assesses the status of the land cover degradation in the Upper Zambezi River Basin (UZB) in southern Africa using the CA-Markov model and the Trends.Earth model. The UZB includes the headwaters of the Zambezi River and is susceptible to land cover degradation with potential negative effects on water resources. High resolution multispectral Landsat data are used in the Land Change Modeler (LCM) and the CA-Markov chain model in Idris Selva 17.0 to assess historical changes and predict future changes in land use and land cover (LULC) for the period 1993-2033. The LULC change maps produced with the LCM and CA-Markov models in Idris Selva are used to assess the land cover degradation status for the period 1993-2033 in the UZB using the Trends.Earth model in QGIS 3.34. Results show that land cover degradation maps produced, at local level, from high spatial resolution multispectral data provides more detail of land cover degradation compared to the Trends.Earth global data set. In terms of land cover degradation, the UZB is largely stable. However, of concern are areas, including wetlands and the headwaters of the Zambezi River, which shows land cover degradation as a result of loss of forest cover to expansions in human settlements and cropland. On the contrary, some areas show improvements in forest cover due to conversion of grassland and cropland into forest cover. For the period 2023 – 2033 the forest cover in the UZB is predicted to have a net reduction of 236258 hectares at a net annual rate of -0.14%. The spatial extent of land cover degradation is projected to build-up on the historical spatial extent. Predicting land cover degradation, as demonstrated in this study, makes available information for instituting targeted interventions which may help in the monitoring and management of water resources, as well as contribute towards a land degradation neutral world.

Evaporation is a major constraining factor of water availability at the land surface which makes its assessment a highly significant prerequisite for application in hydrological, agricultural, climate studies and many other disciplines at various scales. However, its importance and calculation procedures have largely been crafted around and often limited to crop productivity. The overarching consequence of this is inaccurate estimates of evaporation for other land surfaces and particularly for forest systems. Due to limited field evaporation observations attention has been focused on the application of satellite-based products. However, in the case of Africa, and the Miombo ecosystem in particular, the number of flux towers is extremely limited (very few if any) which makes it extremely difficult to evaluate available satellite-based evaporation products. In this study we used the energy balance Bowen ratio approach to estimate field evaporation in a dense Miombo Woodland which we then used to evaluate four energy balance evaporation models. The models evaluated included the MOD16, SEBS, SSEBop and WaPOR. Furthermore, cluster analysis was used to assess the similarity of the models in simulating evaporation. The results show that at daily and dekadal scale the simulated evaporation by the four models significantly varied from field evaporation observations. However, less variations were observed at monthly scale.  Furthermore, all four models overestimated evaporation during the dry season (June-September) with RMSE ranges between 0.21 – 0.38 mm.day-1 and 6.64 - 9.91 mm.month-1. Based on the RMSE and biases the MOD16 (RMSE = 6.64 mm.month-1; Bias = 2.04 mm.month-1), SEBS (RMSE = 8.69 mm.month-1; Bias = 5.72 mm.month-1) and WaPOR (RMSE = 7.44 mm.month-1; Bias = 6.67 mm.month-1) ranked higher than the SSEBop (RMSE = 9.91 mm.month-1; Bias = 9.84 mm.month-1) in simulating evaporation in the Miombo Woodland. Three clusters were observed with the SEBS and WaPOR grouped together indicating their close similarity in simulating evaporation in the Miombo ecosystem while the MOD16 and SSEBop were each grouped separately. Results of this study could aid the interpretation of these evaporation models in Miombo Woodland covered basins such as the Zambezi River Basin in Southern Africa. This could help in monitoring basin water availability and ecosystem reactions and feedbacks to climate change and anthropogenic impacts.