Jadunandan Dash’s research while affiliated with University of Southampton and other places

Estimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi‐sensor optical satellite data sets (e.g., Landsat‐8, 9; Sentinel‐2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor‐specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m² of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near‐infrared and shortwave infrared‐1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non‐Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation‐associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m²/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes.

The fraction of photosynthetically active radiation (FAPAR) plays a crucial role in vegetation carbon capture and dynamic vegetation models, requiring accurate and long-term observations for understanding carbon dynamics. Despite advancements in satellite-derived FAPAR products, validating their accuracy remains critical. Current in-situ validation methods, relying on handheld instruments AccuPAR, DHP, and LAI 2200, are labor-intensive and fraught with uncertainties. Conversely, distributed PAR measurements offer continuous monitoring opportunities, especially with wireless connectivity enabling remote, real-time data access and reduced logistical burdens. However, efforts to develop a standardized framework meeting satellite data validation standards have been limited. Addressing this gap, our study developed a FAPAR framework utilizing data from two wireless PAR networks: one in a vineyard site in Valencia Anchor and the other in a forest site at Hainich, as part of the Copernicus Ground-Based Observation for Validation project. Key aspects explored included developing data quality indicators, determining optimal node configurations to represent Elementary Sampling Units (ESUs), and comparing FAPAR estimation methods. At the Valencia Anchor site, 12 nodes equipped with four sensors capturing radiation at canopy top and bottom were deployed in rows. Results showed that a six-node configuration exhibited a stronger correlation (r = 0.81) with observed data compared to other combinations. Additionally, ESU-level 2-flux and 4-flux FAPAR displayed similar patterns with a strong correlation (r = 0.99), with 2-flux FAPAR performing better across different node combinations. These findings contribute to the development of a robust framework and protocol for in-situ FAPAR measurements, essential for validating global satellite-derived FAPAR products.

This article presents validation and conformity testing of the Sentinel-3 Ocean Land Colour Instrument (OLCI) green instantaneous fraction of absorbed photosynthetically active radiation (FAPAR) and OLCI terrestrial chlorophyll index (OTCI) canopy chlorophyll content (CCC) products with fiducial reference measurements (FRM) collected in 2018 and 2021 over two sites (Las Tiesas—Barrax, Spain, and Wytham Woods, UK) in the context of the European Space Agency (ESA) Fiducial Reference Measurement for Vegetation (FRM4Veg) initiative. Following metrological principles, an end-to-end uncertainty evaluation framework developed in the project is used to account for the uncertainty of reference data based on a two-stage validation approach. The process involves quantifying uncertainties at the elementary sampling unit (ESU) level and incorporating these uncertainties in the upscaling procedures using orthogonal distance regression (ODR) between FRM and vegetation indices derived from Sentinel-2 data. Uncertainties in the Sentinel-2 data are also accounted for. FRM-based high spatial resolution reference maps and their uncertainties were aggregated to OLCI’s native spatial resolution using its apparent point spread function (PSF). The Sentinel-3 mission requirements, which give an uncertainty of 5% (goal) and 10% (threshold), were considered for conformity testing. GIFAPAR validation results revealed correlations > 0.95, RMSD ~0.1, and a slight negative bias (~−0.06) for both sites. This bias could be partly explained by the differences in the FAPAR definitions between the satellite product and the FRM-based reference. For the OTCI-based CCC, leave-one-out cross-validation demonstrated correlations > 0.8 and RMSDcv ~0.28 g·m⁻². Despite the encouraging validation results, conclusive conformity with the strict mission requirements was low, with most cases providing inconclusive results (driven by large uncertainties in the satellite products as well as by the uncertainties in the upscaling approach). It is recommended that mission requirements for bio-geophysical products are reviewed, at least at the threshold level. It is also suggested that the large uncertainties associated with the two-stage validation approach may be avoided by directly comparing with spatially representative FRM.

Mangroves play pivotal roles in ecosystem services, but anthropogenic pressures contribute to their alarming degradation. Precise quantification of vital vegetation characteristics, particularly leaf area index (LAI), is crucial for effective monitoring. LAI serves as a key biophysical parameter in assessing vegetation structure, ecophysiological processes, and overall health. This study pioneers the exploration of a hybrid model (combining radiative transfer with machine learning) for LAI estimation in mangroves. Employing (INvertible FOrest Reflectance Model) INFORM and support vector regression in Bhitarkanika Wildlife Sanctuary, India, we utilized digital hemispherical photographs and Sentinel-2 optical data. Results reveal INFORM's superior retrieval capacity (RMSE = 2.56 m2.m−2) over the PROSAIL model (RMSE = 0.73 m2.m−2). The study underscores the efficacy of physicalbased models, particularly INFORM, in accurate LAI estimation, particularly in challenging environments like mangrove ecosystems where in-situ data collection is constrained.

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

We used a mixed-method approach to characterise households' maize yield gap and its drivers in Malawi. • We surveyed characteristics of 70 smallholder households and observed farmers' maize yield between 0.8 and 10.9 t/ha. • We obtained a water-limited maize yield of 9.5 t/ha for the season 2019-2020 in the trial site. • Higher income and increased fertiliser application have the potential to close the yield gap. • Our approach is valuable in identifying high-productive areas and differentiated policy interventions to close the yield gap. 2 Keywords: Crop modelling Drylands Sub-Saharan Africa Mixed-method approach Yield gap drivers Crop trial experiments majority of the population. Still, crop yields show a huge variability in smallholder farming systems whose productivity is poorly measured and understood. OBJECTIVE: In this work, we estimate maize (Zea Mays) yield gap in Southern Malawi (Phalombe district) and assess drivers of productivity gap under different socioeconomic and biophysical contexts. METHODS: We use a mixed-method approach which integrates multi-source datasets (including primary ground-truth data we collected in the maize growing season 2019-2020 and secondary remote-sensing data), empirical and process-based crop-growth models (AquaCrop) to calculate the water-limited yield gap. In addition, we analyse the relationship between the relative yield (defined as the actual yield observed at the farmers' plots normalised by the AquaCrop simulated water-limited potential yield) and possible socioeconomic drivers which we collected through surveys administered to households iin the same season 2019-2020. RESULTS AND CONCLUSIONS: We obtained a water-limited potential yield for the maize hybrid SC649 of 9.5 t/ ha during the season 2019-2020 in the Malawian trial site. The observed actual yield at the households in the season 2019-2020 varied from 0.8 to 10.9 t/ha. The estimate of the yield gap ranged between 15% and 85% thus showing a large variability due to the high resolution, but low accuracy of the empirical model. Results suggest that with higher income and increased fertiliser application there is potential to increase the relative yield and that the marginal increase is spatially differentiated. SIGNIFICANCE: Our spatially-explicit approach to yield-gap analysis is valuable in identifying high-productive areas and differentiated policy interventions aimed at closing the yield and income gaps for smallholder farmers.

Understanding the drivers of ecosystem dynamics, and how responses vary spatially and temporally, is a critical challenge in the face of global change. Here we used structural equation models and remote sensing datasets to understand the direct and indirect effects of climatic, environmental, and anthropogenic variables on woody vegetation dynamics across four grasslands regions (i.e., Sahel grasslands, Greater Karoo and Kalahari drylands, Southeast African subtropical grasslands, and Madagascar) of sub-Saharan Africa. We focus on African grasslands given their importance for biodiversity and ecosystem services, the lack of clarity on how they are likely to respond to changes in disturbances, and how such responses vary geographically. This is particularly the case of grass-dominated ecosystems – the focus of our study – rather than more mixed grass-tree regions (e.g., savannas). Rainfall (β = 0.148 [-0.111, 0.398]) and temperature (β = -0.109 [-0.387, 0.133]) showed consistently opposing effects on woody vegetation (average standardised regression coefficients and 95% confidence interval range during 1997–2016) across the four bioregions. Other variables showed overall negligible effects including, for instance, dry season rainfall, soil moisture and, notably, fire. Other relationships were more context-dependent. Only Greater Karoo and Kalahari drylands showed a negative relationship between woody vegetation and fire (β = -0.031 [-0.069, 0.021]). Similarly, in Madagascar we observed strong negative effects of temperature (β = -0.429 [-1.215, -0.259]) and population density (β = -0.354 [-0.651, -0.015]) on burned area, yet these did not result in any significant indirect effects on woody vegetation. Our results clarify the contribution of environmental and anthropogenic variables in controlling woody dynamics at broad spatiotemporal scales and reveal that the widely documented negative feedback between fire and woody vegetation does not necessarily apply across all African grasslands.