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

2021 ◽  
Vol 13 (2) ◽  
pp. 414-423
Author(s):  
Kumaraperumal. R. ◽  
Pazhanivelan. S. ◽  
Ragunath. K.P. ◽  
Balaji Kannan ◽  
Prajesh. P.J. ◽  
...  

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.  

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.


2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona

&lt;p&gt;Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.&lt;/p&gt;&lt;p&gt;Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. &amp;#8232;&lt;/p&gt;&lt;p&gt;A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.&lt;/p&gt;&lt;p&gt;Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA&amp;#8217;s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.&lt;/p&gt;&lt;p&gt;A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Mu&amp;#241;oz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.&lt;/p&gt;&lt;p&gt;The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).&lt;/p&gt;&lt;p&gt;In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.&lt;/p&gt;


2020 ◽  
Author(s):  
Joost Buitink ◽  
Anne M. Swank ◽  
Martine van der Ploeg ◽  
Naomi E. Smith ◽  
Harm-Jan F. Benninga ◽  
...  

Abstract. The soil moisture status near the land surface is a key determinant of vegetation productivity. The critical soil moisture content determines the transition from an energy-limited to a water-limited evapotranspiration regime. This study quantifies the critical soil moisture content by comparison of in situ soil moisture profile measurements of the Raam and Twenthe networks in the Netherlands, with two satellite derived vegetation indices (NIRv and VOD) during the 2018 summer drought. The critical soil moisture content is obtained through a piece-wise linear correlation of the NIRv and VOD anomalies with soil moisture on different depths of the profile. This nonlinear relation reflects the observation that negative soil moisture anomalies develop weeks before the first reduction in vegetation indices. Furthermore, the inferred critical soil moisture content was found to increase with observation depth and this relationship is shown to be linear and distinctive per area, reflecting the tendency of roots to take up water from deeper layers when drought progresses. The relations of non-stressed towards water-stressed vegetation conditions on distinct depths are derived using Remote Sensing, enabling the parameterization of reduced evapotranspiration and its effect on GPP in models to study the impact of a drought on the carbon cycle.


2020 ◽  
Vol 24 (12) ◽  
pp. 6021-6031
Author(s):  
Joost Buitink ◽  
Anne M. Swank ◽  
Martine van der Ploeg ◽  
Naomi E. Smith ◽  
Harm-Jan F. Benninga ◽  
...  

Abstract. The soil moisture status near the land surface is a key determinant of vegetation productivity. The critical soil moisture content determines the transition from an energy-limited to a water-limited evapotranspiration regime. This study quantifies the critical soil moisture content by comparison of in situ soil moisture profile measurements of the Raam and Twente networks in the Netherlands, with two satellite-derived vegetation indices (near-infrared reflectance of terrestrial vegetation, NIRv, and vegetation optical depth, VOD) during the 2018 summer drought. The critical soil moisture content is obtained through a piece-wise linear correlation of the NIRv and VOD anomalies with soil moisture on different depths of the profile. This non-linear relation reflects the observation that negative soil moisture anomalies develop weeks before the first reduction in vegetation indices: 2–3 weeks in this case. Furthermore, the inferred critical soil moisture content was found to increase with observation depth, and this relationship is shown to be linear and distinctive per area, reflecting the tendency of roots to take up water from deeper layers when drought progresses. The relations of non-stressed towards water-stressed vegetation conditions on distinct depths are derived using remote sensing, enabling the parameterization of reduced evapotranspiration and its effect on gross primary productivity in models to study the impact of a drought on the carbon cycle.


2021 ◽  
Author(s):  
Sebastian Wieneke ◽  
Ana Bastos ◽  
Manuela Balzarolo ◽  
José Miguel Barrios ◽  
Ivan Janssens

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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&amp;#8217;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.&lt;/p&gt;&lt;p&gt;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 &amp;#8220;stress&amp;#8221; versus &amp;#8220;non-stress&amp;#8221; days.&lt;/p&gt;&lt;p&gt;Duveiller, G., Filipponi, F., Walther, S., K&amp;#246;hler, 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&amp;#8211;1116, https://doi.org/10.5194/essd-12-1101-2020, 2020.&lt;/p&gt;


Author(s):  
P. Thilagaraj ◽  
P. Masilamani ◽  
R. Venkatesh ◽  
J. Killivalavan

Abstract. 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.


2012 ◽  
Vol 9 (4) ◽  
pp. 5167-5193 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely-sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when averaged in bulk across the annual cycle, little or no added skill (<5% in relative terms) is associated with applying modern LSMs to off-line agricultural drought monitoring relative to simple accounting procedures based solely on observed precipitation accumulations. However, slightly larger amounts of added skill (5–15% in relative terms) are identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.


Author(s):  
P.R. Swann ◽  
A.E. Lloyd

Figure 1 shows the design of a specimen stage used for the in situ observation of phase transformations in the temperature range between ambient and −160°C. The design has the following features a high degree of specimen stability during tilting linear tilt actuation about two orthogonal axes for accurate control of tilt angle read-out high angle tilt range for stereo work and habit plane determination simple, robust construction temperature control of better than ±0.5°C minimum thermal drift and transmission of vibration from the cooling system.


1964 ◽  
Vol 45 (4) ◽  
pp. 535-559 ◽  
Author(s):  
E. Bolté ◽  
S. Mancuso ◽  
G. Eriksson ◽  
N. Wiqvist ◽  
E. Diczfalusy

ABSTRACT In 15 cases of therapeutic abortion by laparotomy the placenta was disconnected from the foetus and perfused in situ with tracer amounts of radioactive dehydroepiandrosterone (DHA), dehydroepiandrosterone sulphate (DHAS), androst-4-ene-3,17-dione (A), testosterone (T) and 17β-oestradiol (OE2). Analysis of the placentas, perfusates and urine samples revealed an extensive aromatisation of DHA, A and T; more than 70% of the radioactive material recovered was phenolic, and at least 80 % of this phenolic material was identified as oestrone (OE1), 17β-oestradiol (OE2) and oestriol (OE3), the latter being detected only in the urine. Comparative studies indicated that A and T were aromatised somewhat better than DHA and that all three unconjugated steroids were aromatised to a much greater extent than DHAS. Radioactive OE1 and OE2 were isolated and identified in the placentas and perfusates, but no OE3, epimeric oestriols, or ring D ketols could be detected in these sources, not even when human chorionic gonadotrophin (HCG) was added to the blood prior to perfusion. Lack of placental 16-hydroxylation was also apparent when OE2 was perfused. Regardless of the precursor perfused, there was three times more OE2 than OE1 in the placenta and three times more OE1 than OE2 in the perfusate. This was also the case following perfusion with OE2. The results are interpreted as suggesting the existence in the pregnant human of a placental »barrier« limiting the passage of circulating androgen. The barrier consists of a) limited ability to transfer directly DHAS and b) an enzymic mechanism resulting in the rapid and extensive aromatisation of the important androgens DHA, A and T.


2017 ◽  
Author(s):  
Amanda H. Schmidt ◽  
◽  
Paul R. Bierman ◽  
Veronica Sosa-Gonzalez ◽  
Thomas B. Neilson ◽  
...  
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