Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R ² = 0.26, RMSE = 3.9 µg/cm²), CCC (R ² = 0.65, RMSE = 0.33 g/m²), and LAI (R ² = 0.47, RMSE = 0.73 m²/m²), comparable to the estimations based on the machine learning method Random forest regression of LCC (R ² = 0.34, RMSE = 4.06 µg/cm²), CCC (R ² = 0.65, RMSE = 0.34 g/m²), and LAI (R ² = 0.47, RMSE = 0.75 m²/m²). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R²) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R² of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R² = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

Understanding long-term drought dynamics in regions dominated by rain-fed agriculture systems is essential for climate adaptation planning. Thus, monitoring drought enables the provision of data for informed decision-making, which is critical to reducing drought risk. This study reconstructs spatiotemporal drought variability in the eastern Amhara region of Ethiopia over 120 years (1901–2020) using the self-calibrating Palmer Drought Severity Index (scPDSI). Data from the Royal Netherlands Meteorological Institute (KNMI) ( https://climexp.knmi.nl/start.cgi ) at 0.5° resolution were analyzed across 11 representative meteorological stations. Spatial interpolation using universal Kriging was applied to identify geographical patterns of drought. Temporal trends, recurrence intervals, and spatial patterns were assessed, and results were triangulated with key informant interviews. The region experienced drought conditions of varying severity approximately every 2.4 years. Mild and moderate droughts occurred more frequently, while severe and extreme droughts recurred roughly once per decade. A marked clustering of severe and extreme drought episodes was observed after 1970, indicating intensification in the late 20th and early 21st centuries. In peak years such as 1922 and 1984, over 80–90% of stations recorded severe to extreme drought conditions. Within the Eastern Amhara Region, particularly the Northern, northeastern, eastern, south-central, and southwestern areas, have frequently experienced severe and extremely severe drought, reflecting sensitivity to large-scale climate drivers. Interview participants confirmed that drought incidents have become increasingly frequent which has impeded the livelihoods of subsistence farmers. We recommend establishing comprehensive drought-monitoring and early-warning systems to inform the design of proactive measures.., Shifting to drought-tolerant crop varieties, promoting small-scale irrigation, diversifying crops to reduce reliance on high-risk crops, and developing alternative livelihoods for communities heavily dependent on agriculture are crucial. By bridging climate science with agricultural practice, this research contributes evidence for designing policies that strengthen resilience farming systems in one of Africa’s most drought-prone regions.

La sequía es un fenómeno recurrente en los pastizales de Borena, Etiopía, con impactos severos sobre la producción ganadera y las comunidades agro-pastoriles. Los datos de Observación de la Tierra (EO) ofrecen un gran potencial para mejorar la detección de sequías mediante información espacio-temporal oportuna; sin embargo, muchos indicadores tradicionales se centran únicamente en la sequía meteorológica o no son adecuados para ecosistemas semiáridos. La evapotranspiración real (ET) es un indicador clave, ya que integra condiciones atmosféricas (i.e. sequía meteorológica), disponibilidad de agua (i.e. sequía hidrológica) y el estado fisiológico de la vegetación (i.e. sequía agrícola/ecológica). Este estudio evalúa estimaciones de ET obtenidas con el modelo de balance de energía de tres fuentes (3SEB), forzado con datos globales de Copernicus (Sentinel-3 y ERA5), para caracterizar la respuesta de la vegetación a la sequía en ecosistemas semiáridos. El modelo se validó en tres sitios de eddy-covariance (EC), dos dehesas en España y una sábana en Sudáfrica que presentan condiciones similares (i.e., clima, estructura, fenología) a las que encontramos en Borena. Además, se generaron mapas de ET para Borena (2019–2022) y se compararon con el índice de vegetación de diferencia normalizada (NDVI), comúnmente usado en indicadores de sequía para caracterizar la respuesta de la vegetación. Los resultados de ET preliminares muestran buena concordancia con las mediciones EC (RMSE ~1 mm/día) y una respuesta más rápida frente al NDVI, lo que resalta su potencial para la detección temprana del estrés hídrico. El trabajo futuro se centrará en comparar indicadores basados en ET con índices tradicionales de sequía mediante un análisis espacio-temporal, así como con mediciones de servicios ecosistémicos.

Ali Yasin Ahmed, Abebe Mohammed Ali, Nurhussen Ahmed and Birhane Gebrehiwot. J Ecol Environ 2024;48:. https://doi.org/10.5141/jee.24.066