Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov’s NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov’s method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover.

Sensible heat flux (H) has a large impact on energy exchange between the surface and the atmosphere and, thus, affects climate change and climatic and hydrological modelling. In the past, remote sensing of H has been a major area of interest and, as a result, various methods have been established for its retrieval. However, large discrepancies between measured and simulated values of H have been observed over land surfaces because of various assumptions and simplifications. This article presents a generalized algorithm for the estimation of sensible heat flux that is suitable for a wide range of atmospheric and terrestrial conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Standard built-in atmospheric profiles in Fast Atmospheric Signature Code (FASCODE) together with atmospheric conditions obtained by periodic radio sounding, once a week, performed at the Broglio Space Centre in Malindi, Kenya, were used in simulating MODIS data at 11.03 and 12.02 μm wavelengths using PcLnWin software. This new approach improves the form of the Mito algorithm, developed to determine surface temperature, by removing some of the assumptions underlying the algorithm – for example, the assumption that air temperature T _a is approximately equal to surface temperature T _s. The resulting bulk aerodynamic resistance equation allows the formulation of a general algorithm for the determination of H, which takes into account the surface emittance effect, water vapour column (WVC), canopy properties, air temperature and different atmospheric stabilities. Unlike other conventional methods developed earlier for the determination of H, a prior knowledge of surface temperature as an auxiliary input is not necessary in this new algorithm. The estimates of sensible heat flux derived from MODIS using the proposed algorithm compared well with in situ measurements, giving a good correlation coefficient of r = 0.9.

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases.

Coastal zones are the epicenter of economic activities in most developing countries in the world hence are bound to change. With advancement in technology, monitoring degradation of coastal zones using remote sensing (RS) and geographic information system (GIS) has become a critical subject for scientific research. RS refers to the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft) while GIS is a system that creates, manages, analyzes, and maps all types of data. This paper reports an evolving Kenyan coastline over the past three decades using RS and GIS. Literature on chronological evolution and its impacts along this coastline is scanty and it is therefore envisaged that this work will instigate more enquiry into factors that were involved in landscape formation along the Kenyan coast. Prior reports indicate that research work done along this coastline were by field survey. We report a prograding beach on Mto Tamamba delta as displayed by multispectral Landsat imagery. The marine ecosystems, riparian communities and tourism industry are facing a major threat. The finding establishes hydrodynamic effects and human influence as key contributors of this menace. This study is aimed at detecting the coastline change using multispectral Landsat images from the years 1990 to 2021. For this purpose, coastlines belonging to these years were drawn numerically with the aid of GIS and RS on ENVI 5.3 software. We utilized maximum likelihood algorithm to categorize the images, and employed a time series methodology on classified endmembers to infer the presence of erosion and accretion. Using the image subset technique, areas prone to erosion and accretion within the coastal datum were identified and cut. The patterns of spatiotemporal trend of these endmembers were effectively employed to corroborate these findings. Change in distribution of endmembers over the 31 years’ period was assessed using thematic change technique; an approach not previously applied in this study area. From the classified Mto Tamamba image, the pixel coverage area for each endmember was extracted. Validation using field campaign data gave an overall accuracy of 84% and kappa coefficient of 0.799. Taking into account that, the traditional methods of monitoring land degradation over a large area are time consuming and expensive; remote sensing data used in this study has offered alternatively cheap, consistent, reliable and retrievable data.

Rice ( Oryza sativa L.) crop monitoring in Africa requires efficient retrieval of biophysical parameters such as leaf area index (LAI) and leaf chlorophyll content (LCC), which are essential for crop stress detection and nutrient management. This study aimed at developing a parsimonious modeling approach that overcomes the limitations of existing single‐ and multi‐target models. We developed a novel multi‐output framework that integrates Pareto optimization with adaptive stopping rules to simultaneously retrieve LAI and LCC from Sentinel‐2 imagery. Model performance was evaluated using in situ data ( n = 177) collected from rice farms in Kenya's Ahero irrigation scheme using coefficient of determination ( R ² ) and root mean square error (RMSE) metrics. The parsimonious XGBoost (eXtreme gradient boosting) model trained on 11 vegetation indices achieved high accuracies ( R ² = 0.76 and RMSE = 0.766 m ² m ^{− 2} for LAI; R ² = 0.74 and RMSE = 3.590 µg cm ^{− 2} for LCC). These results represent gains of up to 32% for LAI and 47% for LCC compared to prior Sentinel‐2‐based multi‐target retrieval approaches. Notably, the model achieved these improvements with a 54% reduction in vegetation indices and a 28% smaller model size relative to a 24‐vegetation indices baseline model. Therefore, our innovative design results in improved stability, generalization, and computational efficiency. By reducing complexity while improving accuracy, the framework offers a practical pathway for scalable crop monitoring and productivity improvement using freely available satellite data.

Accurate retrieval of land surface temperature (LST) from broadband longwave radiometric measurements is fundamentally limited by the nonlinear coupling between surface emissivity and temperature, which can render the inverse problem weakly observable under low irradiance contrast. We present a conditioning-controlled retrieval methodology that estimates broadband surface emissivity and LST directly from paired ground-based upwelling and downwelling longwave irradiance measurements acquired at high temporal resolution. The approach combines adaptive temporal pairing constrained by a quasi-steady apparent surface temperature criterion with a fixed-iteration Newton inversion, and explicitly diagnoses inversion stability through Jacobian strength, residual magnitude, and observed convergence order. A formal uncertainty propagation framework is developed for both independent and correlated irradiance error structures, enabling decomposition of irradiance-driven and emissivity-driven temperature uncertainty. The method is evaluated using 39 datasets from four Surface Radiation Budget (SURFRAD) Network sites spanning diverse atmospheric conditions. The Newton inversion exhibited stable and well-conditioned behaviour across all cases, and retrieved surface temperatures agreed with independent in-situ measurements with a root mean square error of 0.54 K and a mean absolute error of 0.47 K, consistent with propagated uncertainty estimates. Results demonstrate that reliable broadband LST retrieval can be achieved without externally prescribed emissivity products when inversion conditioning and measurement uncertainty are explicitly incorporated into the retrieval design.