A timely irrigation schedule for small-scale farms is imperative for ensuring optimum crop production in the wake of drought and climate change. Owing to the large number of irrigated small-scale farms that grow different crops across all seasons in the Mutale River catchment, this study sought to develop irrigation scheduling for these crops for sustainable water utilization without compromising crop yields. Unmanned aerial vehicle (UAV) images were utilized as the base from which crop water content patterns were derived. A total of four (4) spectral vegetation indices, viz, the Greenness Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Optimized Soil-Adjusted Vegetation Index (OSAVI), were generated to characterize crop types and water content in this study. Crop water content data, in the form of the relative water content (RWC), were measured in the field for each type of crop. Crop water content was modelled based on the empirical relationships between spectral indices and field-measured RWC. The linear regression analysis revealed a significant association between the GNDVI and the water content of sweet potato, maize, sugar beans, and Florida broadleaf mustard, with r² values of 0.948, 0.995, 0.978, and 0.953, respectively. The NDVI revealed a strong association with the water content of Solanum retroflexum, pepper, and cabbage, with r² values of 0.949, 0.956, and 0.995, respectively. The NDRE, on the other hand, revealed a strong relationship with water content in peas and green beans, with r² values of 0.961 and 0.974, respectively. The crop water content patterns simulation revealed that Solanum retroflexum, sweet potato, maize, sugar beans, and Florida broadleaf mustard reached their respective wilting points on day four after irrigation, implying that irrigation of these crops should be scheduled after every four (4) days. Peas, green beans, pepper, and cabbage reached their respective wilting points on day five after irrigation, implying that irrigation of these crops should be scheduled after every five days. The results of this study highlight the significance of considering crop water content derived from spectral bands of UAV imagery in scheduling irrigation for various types of crops. This study also emphasized the on-going significance of remote sensing technology in addressing agricultural issues that impede hunger alleviation and food security goals.

Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop characterization at reasonable spatial, spectral, and temporal scales. However, the presence of shadows on croplands tends to distort radiometric properties of the crops, subsequently limiting the retrieval of crop-related information. This study proposes a simple and reliable approach for radiometrically compensating crops under total occlusion using brightness-based compensation and thresholding approaches. Unmanned aerial vehicle (UAV) imagery was used to characterize crops at the experimental site. In this study, shadow was demarcated through the computation and use of mean spectral radiance values as the threshold across spectral channels of UAV imagery. Several image classifiers, viz., k-nearest neighbor (KNN), maximum likelihood, multilayer perceptron (MLP), and image segmentation, were used to categorize land features, with a view to determine the areal coverage of crops prior to the radiometric compensation process. Radiometric compensation was then performed to restore radiometric properties of land features under occlusion by performing brightness tuning on the RGB imagery. Radiometric compensation results revealed maize and soil as land features subjected to occlusion. The relative error of the mean results for radiance comparison between lit and occluded regions revealed 26.47% deviation of the restored radiance of occluded maize from that of lit maize. On the other hand, the reasonable REM value of soil was noted to be 50.92%, implying poor radiometric compensation results. Postradiometric compensation classification results revealed increases in the areal coverage of maize cultivars and soil by 40.56% and 12.37%, respectively, after being radiometrically compensated, as predicted by the KNN classifier. The maximum likelihood, MLP, and segmentation classifiers predicted increases in area covered with maize of 18.03%, 22.42%, and 30.64%, respectively. Moreover, these classifiers also predicted increases in the area covered with soil of 1.46%, 10.05%, and 14.29%, respectively. The results of this study highlight the significance of brightness tuning and thresholding approaches in radiometrically compensating occluded crops.

Wildfires are considered a natural factor which leaves detrimental effects on the environment. In this study, the occurrence of wildfire smoke coincided with the occurrence of clouds, and this underscored the need to separate the wildfire smoke from the clouds. The sigmoid activation function, coupled with momentum gradient optimizer (MGD) optimizer, was applied to spectrally reconfigure selected Sentinel-2 bands to smoke plumes. Bartlett’s k-comparison of equal variance statistical was applied to determine spectral radiance properties of smoke plumes and clouds across selected Sentinel-2 bands. The Relative Operation Characteristics (ROC) was used to evaluate the performance of the performance of the sigmoid activation function with MGD in characterizing smoke plumes. Bartlett’s test revealed variations in the radiance properties of smoke and clouds across the selected spectral bands of Sentinel-2 sensor, with the p-value of < 0.001 for both smoke and clouds. The mean radiance values for smoke plume were noted to be lower than that of the clouds across all the selected spectral channels besides the shortwave infrared (SWIR) cirrus channel for both original and calibrated image, where smoke and clouds had similar radiance properties. The relative operation characteristics (ROC) results confirmed the calibrated blue and green spectral bands to be effective in detecting smoke plume, with area under curve (AUC) value of 0.81 and 0.73 respectively. This research emphasized the significance of integrating machine learning and multispectral remote sensing in mitigating wildfire disaster. Because wildfire is an unpredictable incident, the findings of this study were not validated with ground-based data.

This study evaluated and minimized bias in estuarine total dissolved solids (TDS) patterns predicted using geostatistical approaches. The acquired TDS data was divided into three parts: 29 points (60 %) for predicting TDS patterns with spatial interpolation techniques, 12 points (25 %) for validation and bias correction, and 7 points (37 %) for testing the corrected bias. Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Universal Kriging (UK) and Regression Kriging (RK) were applied to map TDS patterns. R-square and Relative Error of Mean showed significant discrepancies between observed and predicted TDS levels. The Mean field bias (MFB) correction technique was applied to minimize bias in TDS patterns. After correcting bias, the TDS values predicted by IDW, OK, UK and RK at random locations deviated from measured values by -2.85 %, 0.71 %, 4.66 %, and - 6.03 % respectively. At the test locations, these values deviated by -1.45 %, 1.41 %, 2.11 %, and - 2.65 % respectively.

This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Entropy thresholding segmentation was employed to classify potholes. The sigmoid calibration function was used to reconfigure spectral radiance properties of the UAV spectral bands to pothole features. The descriptive statistics was computed to determine radiance threshold values to be used in demarcating potholes from the reconfigured or calibrated spectral bands. The performance of the sigmoid calibration function was evaluated by analyzing the area under curve (AUC) results generated using the Relative Operating Characteristic (ROC) technique. Spectral radiance pattern analysis of the pothole surface revealed high radiance values in the red channel and low radiance values in the near-infrared (NIR) channels of the spectrum. The sigmoid calibration function radiometrically reconfigured UAV spectral bands based on a total of 500 sampled pixels of pothole surface obtained from all the spectral channels. Upon successful calibration of UAV radiometric properties to pothole surface, the reconfigured mean radiance values for pothole surface were noted to be 0.868, 0.886, 0.944, 0.211 and 0.863 for blue, green, red, NIR and red edge, respectively. The area under curve (AUC) results revealed the r² values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. Overestimation of pothole 1 by both original and calibrated spectral channels was noted and can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. The results of this study emphasize the significance of reconfiguring radiometric properties of the UAV imagery for improved recognition of potholes.