The Usangu wetland, located in Mbarali District, Mbeya Region, Tanzania, lies between latitudes 7˚45'S to 9˚00'S and longitudes 33˚40'E to 35˚00'E and consist of five subsystems ([8]). The Usangu wetland is the study area within Mbarali District, though its hydrological influence extends slightly beyond, including the riparian reach through the Ruaha National Park and the Mtera/Kidatu hydroelectric system. The Usangu sub-catchment drainage area spans approximately 20,800 km², with 4840 km² (23% of the plains) situated below 1100 m above mean sea level (msl), while the remaining 77%, representing the higher catchment, ranges from 1100 m to 2000 m above msl ([13]).

The Usangu Plains as shown in Figure 1 and Figure 2 consist of western and eastern wetlands connected by the Great Ruaha River, with other major perennial rivers draining the plains, including the Mbarali, Kimani, Chimala, and Ndembera ([16]). Smaller rivers, such as the Umrobo, Mkoji, Lunwa, Mromboji, Ipatagwa, Mambi, Kioga, Mjenje, Kimbi, Itumbo, and Mswiswi, also contribute to the drainage network. The plains feature extensive alluvial deposits that support irrigation, livestock keeping, and dryland farming, as illustrated in Figure 3. In the Usangu wetland, mean rainfall ranges from 500mm to 700mm, and mean annual temperature ranges from 18˚C to 28˚C ([31]). The wetland’s outflow sustains the Ruaha National Park, providing essential ecosystem services for wildlife conservation, serves as a water source for the Kidatu Dam, and contributes approximately 15% to the 4 million capacity Stigler Gorge Hydro Power Dam ([9]).

The precipitation dataset sourced from the Climate Data Record (CDR) of NOAA PERSIANN, which is accessible through the cloud-based Google Earth Engine (GEE) platform using JavaScript API code. This dataset has a spatial resolution of 0.25 degrees. The dataset was clipped and filtered to match the wetland area. The Standardized Precipitation Index (SPI) was calculated to assess temporal and spatial patterns of drought and wet conditions, with SPI values ranging from -3 (extreme drought) to 2 (extreme wet). For visualization, a timeseries plot was generated to examine the temporal consistency of SPI across the study period (as shown in ). Spatial consistency was validated by comparing SPI with precipitation clustering, derived using the K-Means algorithm, and presented in .

Figure 1. Usangu catchment map.

Figure 2. The wetland area.

Figure 3. Cropland in the wetland area.

Land Surface Temperature (LST) modelling for the Usangu wetland was conducted using thermal infrared bands ST_B10 from Landsat 8 and 9. The Landsat derived LST was then statistically compared to Sentinel-2 spectral bands (B2-B12) and topographic features elevation, slope and NASA SRTM Digital Elevation Model (DEM) data to predict high-resolution (10m) LST across the wetland, processed within Google Earth Engine (GEE). To ensure data quality, cloud filtering techniques were applied to mask clouds and generate cloud-free images, minimizing noise in the dataset for training and testing. The images were then processed using a median function to create composite images and cropped to fit the study area.

A Random Forest model was employed for LST prediction, trained using machine learning techniques with input features including spectral bands, vegetation indices, and elevation data derived from the SRTM DEM. The model was configured to use 70% of the dataset for training and 30% for testing. Model performance was evaluated by comparing the predicted LST to reference LST data, with errors quantified using the Mean Absolute Error (MAE). The relationship between reference and predicted LST was assessed through a linear correlation, visualized with a trend line and quantified using the Pearson correlation coefficient.

Precipitation data for the study area were sourced from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset ([36]), accessed as a spatial dataset via the cloud based Google Earth Engine (GEE) platform. The CHIRPS dataset provides a high spatial resolution of 0.05 degrees and consists of daily precipitation records, covering the analysis period since 1981 ([2]). To examine trends, seasonal patterns, and residual components in the precipitation data, statistical time series decomposition was performed using the Seasonal-Trend Decomposition using LOESS (STL) method ([11]; [26]). The STL approach was selected for its ability to elucidate natural variability in precipitation data, aiding in the interpretation of trends, seasonal cycles, and time series characteristics, as well as identifying anomalies ([11]).

Coupled Model Intercomparison Project Phase 6 (CMIP6) historical precipitation data was bias corrected using CHIRPS observations via linear scaling ([19]; [30]). The resulting time series Figure 4 reveals severe dry spells (≤200 mm/month) in the Usangu wetland region during 2010-2015.

Figure 4. Bias corrected CMIP6 precipitation.

K-Means clustering of precipitation data was conducted using machine learning techniques within the GEE Python environment, as illustrated in Figure 10 ([27]) and equation 1. This process utilized geographical data from 299 satellite images obtained from the Global Precipitation Measurement (GPM) dataset, specifically NASA/GPM_L3/IMERG_MONTHLY_V07, which has a spatial resolution of 0.1 degrees. The GPM data, available monthly, spanned the period from 2000 to 2024. The K-Means algorithm was applied to categorize the precipitation data into four classes: Heavy, Moderate, High, and Low. A timeseries plot was generated to compare the classified precipitation data with the Standardized Precipitation Index (SPI) for validation.

C_i: Data points in cluster, μ_i: Mean (centroid) of cluster, x: Precipitation value.

The Normalized Difference Vegetation Index (NDVI) data were sourced from the MODIS MOD13A1.061 product (Terra Vegetation Indices 16-Day Global 500 m), covering the period from 2019 to 2023 using equation 2. The NDVI data were clipped to the wetland area. To improve data quality, the NDVI infrared bands were filtered to remove cloud cover and then multiplied by the appropriate scale factor provided in the MODIS dataset to enhance visualization. Monthly averages of NDVI were calculated for each year from 2019 to 2023 to capture temporal variations in vegetation health across the study period Figure 13.

Visualization of water conditions in the Usangu wetland was conducted using spectral indices, including the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MDWI), Normalized Difference Water Index (NDWI), Water in Wetland (WIW), and Automated Normalized Difference Water Index (ANDWI). The data were sourced from Landsat 8 Level 2 (surface reflectance) images, covering the period from 2020 to 2024. These images were filtered to include only those with less than 10% cloud cover to ensure data quality for the wetland area. A composite median image was derived from the surface reflectance bands of the filtered satellite images to reduce temporal variability and enhance consistency.

The NDVI map was generated in Figure 15(a) using the near-infrared (NIR) and red bands to assess vegetation health, calculated as NDVI = (NIR − Red)/ (NIR + Red). To detect water amidst the wetland complex vegetation cover, multiple water indices were employed, leveraging various spectral bands. NDWI, as described in prior studies ([3]; [20]; [33]), was calculated using the NIR and green bands to identify water bodies. The MDWI ([20]) utilized the short-wave infrared (SWIR) band alongside the green band to enhance water detection in vegetated areas. The WIW index method ([21]) was applied using surface reflectance bands in the NIR and SWIR ranges, incorporating a logical thresholding approach to improve water detection reliability, particularly when other indices were less effective. The ANDWI ([25]) was computed using red, green, blue (RGB), NIR, and both SWIR1 and SWIR2 bands to improve water demarcation and visibility in vegetated wetlands.

Table 1. Description of dataset.

Studies show that rivers drain approximately 75% of the surface water on land, underscoring the critical role of river systems in wetland hydrology ([4]). In agricultural regions, land surface emissivity has been identified as a crucial factor for energy balance, influencing thermal dynamics in such areas [7] Also, research has demonstrated that Land Surface Temperature (LST) significantly affects surface water temperature and river flow, with higher LST values often leading to increased water temperatures and altered flow patterns ([21]). These findings from other regions provide a comparative framework for interpreting the hydrological and thermal dynamics observed in the Usangu wetland Figure 5.

Figure 5. LST spatial variation in the study area.

The linear regression r value of 0.789 in the correlation of LST reference against prediction in Figure 6 suggests that the temperature values are significantly captured. The wetland ecosystem is positively affected by the LST due to an imbalance of the energy ([34]). The regression line data points exhibit significant clustering between 20˚C and 30˚C. The alteration in Land Surface Temperature (LST) trends has affected land cover changes, desertification, and water management. Figure 7 illustrates the area influenced by the increase in LST within the basin.

The r correlation in Figure 7 illustrates the MDWI (Modified Normalized Difference Water Index) spectral index, which indicates that water detection is lower than that of MNDWI, which indicates the availability of water in the wetland but is obscured by vegetation. According to the Histogram, the correlation results in the wetland exhibit identical greenness and dryness values. The negative correlation between the LST and elevation in Figure 8 indicates that there is opposite relationship between the two variables. Consequently, the wetland is characterized by a higher temperature than the rest of the areas as shown in Figure 5.

Figure 6. LST baseline (Reference) against prediction.

Figure 7. Correlation coefficient with LST.

Figure 8. LST against elevation.

4.2.1. STL

The precipitation data was analysed using the ML of the stats model in the statistics package. The analysis of the trend indicates a long-term increase in precipitation from the 1990s to the 2020s, characterized by below-average levels in the 1990s and subsequent growth in the 2000s. The timeseries decomposition Figure 9 indicates years below the average from 2000 to 2003 and from 2010 to 2012. This timeframe aligns with the SPI Figure 11 depicting the dry spell in the wetland area.

Figure 9. Timeseries decomposition of precipitation.

4.2.2. Standard Precipitation Index (SPI) in the Wetland Area

In K Means, the precipitation data categorised into four classifications: heavy (red), high (orange), low (blue), and moderate (green) as depicted in Figure 10. The years characterised by moderate precipitation, correlated with the SPI, are predominantly represented by green bars (moderate) and exhibit fewer red, orange, and blue bars: 2003, 2008, 2013, 2017, and 2023. Mean precipitation during the years with moderate rainfall in the study area is 731 mm. We selected 4 clusters to capture the meteorological significance of precipitation intensity distribution, achieving a silhouette score of 0.695 despite it being a lower score for the two clusters and three clusters.

The index indicates a long-term deficiency in rainfall. Figure 11 illustrates extended droughts occurring in the years 2003, 2008, 2013, 2017, and 2023. The SPI drought trend of the data is compared with Figure 9, which represents the precipitation dataset using K-Means.

Figure 10. K-means precipitation temporal clustering.

Figure 11. Standardized precipitation index (SPI) with drought years.

The Usangu wetland, an agricultural area characterized by extensive irrigation development, experiences a drop in NDVI from April to May, coinciding with the paddy harvesting season. This seasonal decline explains the negligible correlation between drought anomaly years in the SPI index (shown in Figure 11) and NDVI values in the wetland in Figure 13. For instance, the drought year 2023 had a mean NDVI of 0.50, reflecting minimal impact. NDVI exhibits a decreasing annual trend from April to October, coinciding with rising LST in the study area shown in Figure 14, which peaks during this period. The NDVI map Figure 12 further illustrates the spatial distribution of vegetation across the region.

NDVI and NDWI of the Wetland

The NDVI Figure 15(a) indicate that the wetland is covered with healthy vegetation, with the maximum positive NDVI reaching 0.5. Normalised Difference Water Index (NDWI) ([33]) Figure 15(b) indicates a maximum negative value of −0.25, suggesting that the wetland is vegetated but deprived of water.

Figure 12. NDVI map showing spatial variation in the wetland area.

Figure 13. NDVI values show seasonal changes in vegetation health across the wetland throughout the year.

Figure 14. LST show seasonal changes in temperature across the wetland area throughout the year NDWI.

Figure 15. (a) Usangu Wetland NDVI; (b) Usangu Wetland NDWI.

The Modified Normalized Difference Water Index (MDWI) ([1]), a variant of the Normalized Difference Water Index (NDWI) that incorporates the SWIR2 band, was applied to assess water and vegetation dynamics in the Usangu Wetland. Analysis of Figure 17(a) (ANDWI) and Figure 17(b) (NDWI) indicates water index values between −0.6 and −0.3, suggesting mostly non-water features within the wetland, as shown in Figure 16. This range of values, particularly the prevalence of negative NDWI values, indicates that the wetland areas are characterized by healthy vegetation, as negative values typically reflect higher vegetation presence due to the influence of the SWIR2 band in reducing water detection sensitivity in vegetated regions.

The Water in Wetland (WIW) approach ([21]), using single spectral bands from Landsat and Sentinel data alongside logical thresholds, was used to identify water pixels in the wetland area. Figure 18(a) and Figure 18(b) indicate that this methodology identified water pixels, hence demonstrating the presence of water

Figure 16. Histogram of wetland NDWI.

Figure 17. (a) ANDWI values in wetland indicate water coverage in the study area; (b) NDWI values in the wetland.

Figure 18. (a) WIW values using a relaxed classification logic, show the pixel-based water extent in the wetland study areas; (b) WIW values using a restricted classification logic, show the pixel-based water extent in the wetland study areas.

bodies within the wetland. Subsequent analysis in Figure 18(a) reveals an increased pixel density, signifying that the WIW method with moderate logical criteria detected a greater concentration of water pixels in contrast to a more restrictive approach in Figure 18(b).

The Water in Wetland (WIW) method employs spectral thresholds applied to Near-Infrared (NIR) and Short-Wave Infrared 2 (SWIR2). Two distinct logical conditions govern water detection. The stricter condition equation 6 requires both thresholds to be satisfied simultaneously and is best suited for mapping permanent water bodies where precision is critical. The relaxed condition equation 6a requires at least one threshold to be satisfied and is best suited for wetland delineation where omission errors must be minimised.