GOOGLE EARTH ENGINE BASED AGRICULTURAL DROUGHT MONITORING IN KODAVANAR WATERSHED, PART OF AMARAVATHI BASIN, TAMIL NADU, INDIA

The agricultural drought assessment and monitoring has become a prime concern in recent times as it impedes land capability and causes food scarcity. Therefore, the present study constructed a methodological framework through the Google Earth Engine (GEE) platform, which offers advanced and effective monitoring in a timely concern of the drought occurrences. The study has been carried out in the Kodavanar watershed, a part of the Amaravathi basin is noted with signs of drought such as insufficient rainfall and vegetation stress in the current situation. The remote sensing indices are utilised for the agriculture drought assessment including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Vegetation Health Index (VHI). In particular, the VHI results show that the area of healthy vegetation and no drought category is rapidly decreased from 934.29 to 107.83 sq.km across the years and have been reached threatening condition as extreme drought category with extremely low vegetation cover has been increasing in a exponential proportion of over 5% in the year 2019 and 2020. However, the agriculture drought results compared through the meteorological drought indicator of Standardized Precipitation Index (SPI) reflects that the SPI and VHI are reflecting similar signs and indicating the dry condition of precipitation with moderate vegetation over the highlighted regions of northern tip and central eastern portions. This present work illustrates the effective use of the GEE platform in monitoring the agriculture drought and the highlighted portions of the study should be implemented with proper water resource management by the researchers, planners and policymakers in the Kodavanar watershed for reducing the vegetation stress.

Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices

Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.

Evaluating the relationship between SIF and GPP under climate extremes

<p>Sun Induced Chlorophyll Fluorescence (SIF) is considered as a good proxy for photosynthesis given its closer link to the photosynthetic light reactions compared to remote sensing vegetation indices typically used for ecosystem productivity modelling (eg. NDVI). Satellite-based SIF shows significant linear relationships with gross primary production (GPP) from in-situ measurements across sites, biomes and seasons. While SIF can be considered a good proxy for large scale spatial and seasonal variability in GPP, much of the SIF-GPP co-variance can be explained by their common dependence on the absorbed photosynthetically active radiation. Whether SIF can be an equally good proxy for interannual variability in GPP especially during periods of vegetation stress (drought/heat) is, so far, not clear.</p><p>In this study, we evaluate the relationship between satellite-based SIF and in-situ GPP measurements during vegetation stress periods (drought/heat), compared to non-stress periods. GPP is obtained from eddy-covariance measurements from a set of forest sites pre-filtered to ensure homonegeous footprints. SIF is obtained from GOME-2 covering the period 2007-2018. Because of scale mismatch between each site’s footprint (in the order of hundred meters) and the spatial resolution of GOME-2 (ca. 50km), we additionally use SIF from the downscale product from Duveiller et al. 2020 (ca. 5km) and the more recent dataset from TROPOMI (ca. 7 x 3.5 km), covering only the last year of the study period.</p><p>We develop a classification of stress periods that is based on both the occurrence of drought/heat extreme events and the presence of photosynthetic downregulation. We then evaluate the relationship between SIF and GPP and their yields, for different plant functional types and at site-level. We evaluate how these relationships vary depending on environmental conditions and in particular for “stress” versus “non-stress” days.</p><p>Duveiller, G., Filipponi, F., Walther, S., Köhler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data, 12, 1101–1116, https://doi.org/10.5194/essd-12-1101-2020, 2020.</p>

Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde

<p>Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short-lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote-sensing data are applied here to satellite-derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.</p>

Drivers of vegetation activity during European summers: A causal inference approach applied to solar-induced fluorescence observations

<p> Summer weather in Europe has become more extreme in recent years. Several studies have focused on unraveling the influence that this extreme weather may have on ecosystem dynamics. However, traditional optical indices characterise the state of vegetation in terms of greenness or structure, but fail to capture short term impacts on vegetation activity caused by water or heat stress. Being a byproduct of photosynthesis, solar induced fluorescence (SIF) represents an exception, since its dynamics may reflect an integral of the environmental stressors that have immediate influence on ecosystem water, energy and carbon exchanges during droughts or heatwaves. Spaceborne datasets of SIF have not only been used to monitor crop photosynthetic activity and GPP at global scales, but also as a proxy of transpiration dynamics, or even biogenic volatile organic compound emissions. Additionally, numerous case studies have indicated the potential of using SIF for early drought detection and monitoring of ecosystem impacts. </p><p>However, as with most earth science applications, the majority of previous studies rely on correlations or linear regressions to establish these cause–effect relationships, which implies that the actual drivers of drought and periods of vegetation stress remain largely unresolved.</p><p>Here we examine the underlying causality and interactions between vegetation activity (represented by changes in SIF) and potential environmental drivers of vegetation stress over Europe during the summer months. Using satellite observations of  photosynthetically active radiation (PAR) and the fraction of absorbed PAR (fPAR), the SIF signal is decomposed into the component that relates to fPAR and the component that relates to the fluorescence yield, which represent different physical and biochemical responses to vegetation stress. Using recently developed methods for causal inference applications in Earth science (https://causeme.uv.es/), the dynamics of SIF and its deconstructed components are evaluated against satellite observations of soil moisture, vapor pressure deficit and temperatures. Common causal relationships and dynamics are observed when grouping regions by aridity index and fractions of vegetation cover. Results help establish direct and indirect links of potential drivers of vegetation activity during periods of heat and water stress.</p>