Landsat-Based Drought Phenomenon Evaluation of Cassava and Sugar cane in Northeast Thailand

Northeast Thailand is a predominantly agriculture-based region yet it suffers from drought which threatens the people’s livelihood. The region is the largest producer of sugar cane and cassava in the country which are classified as high-value, food and energy crops. Thailand ranks first and third in the world in terms of exporting cassava and sugar cane respectively. Unfortunately, when compared to other regions, the northeast receives the least attention in terms of agricultural research yet it is the most vulnerable part of the country. As such, the goal of this study was to assess agricultural drought in the region pertaining to sugar cane and cassava farming over twelve years (2004 to 2015) relative to the climatic conditions. Normalized Difference Vegetation Index (NDVI)-based Vegetation Condition Index (VCI), derived from Landsat 5 and 8 satellites (30 meters’ resolution) was usedas a determinant of agricultural drought. Precipitation and temperature (0.25-degree resolution) data were sourced from GLDAS-2 Noah model products. Temporal-VCI indicated that the major agricultural drought periods for both crops were 2004, 2005, 2007, 2008, 2009 and 2011. A significant improvement in the crops’ condition was noted in 2014 and 2015. Similarly, spatial-VCI showed an increase in VCI for 2014 and 2015 despite these being major meteorological drought years. This supports the premise that the region has made efforts to curb the effects of drought on agriculture. However, continuous monitoring of drought using different physical indicators is necessary for further development of effective solutions and sharpening of the currently existing measures

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Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

Advances in Meteorology ◽

10.1155/2017/8434020 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Erika Andujar ◽

Nir Y. Krakauer ◽

Chuixiang Yi ◽

Felix Kogan

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Emission ◽

Plant Stress ◽

Meteorological Drought ◽

Condition Index ◽

Vegetation Health ◽

Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

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Spatial-temporal variation and impacts of drought in Xinjiang (Northwest China) during 1961–2015

PeerJ ◽

10.7717/peerj.4926 ◽

2018 ◽

Vol 6 ◽

pp. e4926 ◽

Cited By ~ 8

Author(s):

Junqiang Yao ◽

Yong Zhao ◽

Xiaojing Yu

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Climatic Conditions ◽

Significant Rise ◽

Northwestern China ◽

Moisture Loss ◽

Drought Severity ◽

Evaporative Demand

Observations indicate that temperature and precipitation patterns changed dramatically in Xinjiang, northwestern China, between 1961 and 2015. Dramatic changes in climatic conditions can bring about adverse effects. Specifically, meteorological drought severity based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) showed a decreasing trend in Xinjiang prior to 1997, after which the trend reversed. SPEI-based drought severity shows a much stronger change during 1997–2015 than the SPI, which is independent of the effect of evaporative demand. Meteorological drought severity has been aggravated by a significant rise in temperature (1.1 °C) over the last two decades that has not been accompanied by a corresponding increase in precipitation. As a result, the evaporative demand in Xinjiang has risen. An examination of a large spatio-temporal extent has made the aggravated drought conditions more evident. Our results indicate that increased meteorological drought severity has had a direct effect on the normalized difference vegetation index (NDVI) and river discharge. The NDVI exhibited a significant decrease during the period 1998–2013 compared to 1982–1997, a decrease that was found to be caused by increased soil moisture loss. A positive relationship was recorded between evaporative demand and the runoff coefficients of the 68 inland river catchments in northwestern China. In the future, meteorological drought severity will likely increase in arid and semiarid regions as global warming continues.

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Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017)

Revista ECIPeru ◽

10.33017/reveciperu2018.0013/ ◽

2018 ◽

pp. 81-89

Keyword(s):

Spatial Patterns ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Condition Index ◽

Agricultural Drought ◽

Sensor Data ◽

Severe Drought ◽

Dry Land ◽

Growth State ◽

Agricultural Areas

Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017) Gisell Carbajal1, Bram Willems1,2 y Waldo Lavado3 1 Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Ap. Postal 14-0149, Lima, Perú 2 Centro de Competencias del Agua, Jr. Bolognesi 150 A, 303, San Miguel, Lima, Perú 3 Servicio Nacional de Meteorología e Hidrología del Perú, Jr. Cahuide 785 Jesús María, Lima 11 – Perú Recibido el 19 de noviembre del 2018. Revisado el 9 de diciembre del 2018. Aceptado el 10 de diciembre del 2018 DOI: https://doi.org/10.33017/RevECIPeru2018.0013/ Resumen En el presente trabajo se analiza la evolución espacial y temporal del Índice de Condición de la Vegetación (ICV), con el propósito de identificar patrones relevantes a la ocurrencia de eventos de sequía agrícola en Piura. El ICV provee información acerca del estado de crecimiento de la vegetación durante situaciones extremas, y se deriva del producto: valores del Índice de Vegetación de Diferencia Normalizada (NDVI) - datos del sensor MODIS (Espectrorradiómetro de Imagen de Resolución Moderada) a una resolución espacial de 1 km en el periodo 2000-2017 a bordo del satélite Terra (MOD13A3, versión 6) obtenida en su paso diurno entre las 10:30 horas y las 12:00 horas (hora local). Los patrones espaciales del ICV revelan que, para el caso de las áreas agrícolas de secano, en el 2004, el 21 % presentaron condiciones de sequía extrema y severa, mientras que en el 2007 fue el 19,5 %, el 2011 el 15,5 % y el 2014 llegó al 21 %. Por otro lado, para el caso de las áreas agrícolas por regadío, en el 2004 se vieron afectadas el 44,2 %, el 2005 fue el 55,4 %, el 2007 fue el 38,8 %, el 2011 fue el 17,1 % y el 2014 fue el 37,1 %. Descriptores: Sequía, patrones espaciales, áreas agrícolas, secano, regadío, ICV Abstract The present work, the spatial and temporal evolution of the Vegetation Condition Index (VCI) is analyzed, with the purpose of identifying patterns relevant to the occurrence of agricultural drought events in Piura. The VCI provides information about the growth state of the vegetation during extreme situations, and it is derived from the product: Normalized Difference Vegetation Index (NDVI) values - MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor data at a spatial resolution of 1 km in the period 2000-2017 on board the Terra satellite (MOD13A3, version 6) Obtained in its passage between 10:30 am and 12:00 pm (local time). The spatial patterns of the VCI reveal that, in the case of rainfed agricultural areas, in 2004, 21 % presented extreme and severe drought conditions, while in 2007 it was 19.5 %, in 2011 the 15.5 % and 2014 reached 21 %. On the other hand, in the case of irrigated agricultural areas, 44.2 % were affected in 2004, 55.4 % in 2005, 38.8 % in 2007, 17.1 % in 2011 and 37.1 % in 2014. Keywords: Drought, spatial patterns, agricultural areas, dry land, irrigated land, ICV

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Impact of meteorological drought on vegetation in non-irrigated lands

Időjárás ◽

10.28974/idojaras.2021.3.6 ◽

2021 ◽

Vol 125 (3) ◽

pp. 463-476

Author(s):

Tahereh Sadat Mirmohammadhosseini ◽

Seyed Abbas Hosseini ◽

Bagher Ghermezcheshmeh ◽

Ahmad Sharafati

Keyword(s):

Drought Index ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pearson Correlation ◽

Confusion Matrix ◽

Correlation Method ◽

Meteorological Drought ◽

Condition Index ◽

Natural Phenomenon ◽

Moderate Drought

Drought is a natural phenomenon that causes a lot of damages annually in various sectors, including agriculture and natural resources. The aim of this study is to evaluate the influence of meteorological drought index on vegetation index. For this purpose, the standard precipitation index (SPI) as a meteorological drought index is calculated using the precipitation data of 28 meteorological stations located on the area of Lorestane province, Iran, during the years 1987–2017. Then, the vegetation condition index (VCI) is computed using normalized difference vegetation index (NDVI) images that obtained from MODIS images of Terra satellite during 2000–2017. Dry, normal, and wet years were obtained based on the SPI results for 2008, 2013, and 2016, respectively. SPI and VCI were correlated using Pearson's correlation method. The results of the relationship between VCI and SPI showed that the highest Pearson correlation coefficient related to 9-month SPI in November was equal to 0.64. Multivariate linear regression was also performed between SPI and VCI, and the results showed that SPI was significantly correlated with VCI at 5% level over a period of 9 and 12 months. Finally, a confusion matrix was used to evaluate the compliance of the SPI and VCI drought classes. Results showed that the VCI had the highest compliance in the moderate drought class with SPI.

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Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

Remote Sensing ◽

10.3390/rs12213524 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3524

Author(s):

Feng Gao ◽

Martha C. Anderson ◽

W. Dean Hively

Keyword(s):

Remote Sensing ◽

Time Series ◽

Cover Crops ◽

Cover Crop ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Research ◽

Agricultural Landscapes ◽

Absolute Difference ◽

Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

Remote Sensing ◽

10.3390/rs12152433 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2433 ◽

Cited By ~ 3

Author(s):

Iman Rousta ◽

Haraldur Olafsson ◽

Md Moniruzzaman ◽

Hao Zhang ◽

Yuei-An Liou ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Condition Index ◽

Vegetation Coverage ◽

P Value ◽

Human Society ◽

Time Series Regression ◽

Drought Conditions

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

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Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

Remote Sensing ◽

10.3390/rs11212534 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2534 ◽

Cited By ~ 7

Author(s):

Willibroad Gabila Buma ◽

Sang-Il Lee

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Meteorological Data ◽

Condition Index ◽

Drought Monitoring ◽

World Population ◽

Lake Chad ◽

In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

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Study on the correlation between meteorological and agricultural drought, based on remotely sensed indices

10.5194/egusphere-egu2020-13925 ◽

2020 ◽

Author(s):

Ruja Mansorian ◽

Mohammad Zare ◽

Guy Schumann

Keyword(s):

Time Scales ◽

Vegetation Index ◽

Meteorological Drought ◽

Lag Time ◽

Irrigation Scheduling ◽

Agricultural Drought ◽

Standard Normal Distribution ◽

Drought Indices ◽

Landsat 8 ◽

Drought Condition

<p>In this study, long-term time series of precipitation data were used for determining the drought condition using the standard precipitation index (SPI) for 3, 6 and 12 month time scales. The indicators were calculated with two methods: a) using a gamma distribution and transforming the probability of occurrence to standard normal distribution, b) using the non-parametric plotting position method. Then, the SPI values for two consequent years 2013-14 and 2014-15 were extracted from data to study on meteorological drought. The SPI index calculations showed that the first year had near normal, whereas the second year had extreme drought condition. In parallel, 34 Landsat 8 satellite images were downloaded during the indicated time period to determine normalized difference vegetation index (NDVI) and vegetation condition index (VCI) as agricultural drought indices. The mean values of VCI for each month were considered as representative value for drought condition of the area. When the agricultural and meteorological drought indices were determined, the correlation coefficient (r) were calculated for finding the relation between these types of droughts. the results show that the highest correlation between SPI-3,6 and 12-month time scales and VCI occurred in 4, 2 and 4 months lag time respectively, with corresponding r value of 0.67, 0.65 and 0.69. The best agreement between these indices with calculated lag time proves the hypothesis that agricultural drought occurs after meteorological drought. Therefore, the results could be applied by farmers to plan an appropriate irrigation scheduling for upcoming droughts, specially, in arid and semi-arid areas. It could be concluded that for having suitable planning in water scarcity condition, understanding the situation helps water planners have better insight about management polices to minimize the effects of this natural hazard on human. To sum up, finding a relation between different types of droughts is helpful for monitoring, predicting and detecting droughts to better prepare for drought phenomena and to minimize losses</p>

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Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-7963-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 7963-7997 ◽

Cited By ~ 4

Author(s):

A. McNally ◽

C. Funk ◽

G. J. Husak ◽

J. Michaelsen ◽

B. Cappelaere ◽

...

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Drought ◽

Sub Saharan Africa ◽

Sub Saharan ◽

The Relationship ◽

African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

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Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss1.2019.217 ◽

2019 ◽

Vol 10 (1) ◽

pp. 48-56 ◽

Cited By ~ 4

Author(s):

Muhammad Khubaib Abuzar ◽

Muhammad Shafiq ◽

Syed Amer Mahmood ◽

Muhammad Irfan ◽

Tayyaba Khalil ◽

...

Keyword(s):

Remote Sensing ◽

Social Life ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Temporal Analysis ◽

Agricultural Drought ◽

Natural Phenomenon ◽

Drought Risk ◽

Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

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