A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

Enenkel, Markus; Steiner, Caroline; Mistelbauer, Thomas; Dorigo, Wouter; Wagner, Wolfgang; See, Linda; Atzberger, Clement; Schneider, Stefan; Rogenhofer, Edith

doi:10.3390/rs8040340

Cited by 55 publications

(27 citation statements)

References 65 publications

(63 reference statements)

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“…Soil moisture governs the partitioning of the mass and energy fluxes between the land and the atmosphere, thus playing a key role in the assessment of the different components of the water and energy balance. Soil moisture is an important variable for flood and landslide modelling and prediction [9][10][11][12], for drought assessment and forecasting [13][14][15], and for numerical weather prediction [16][17][18][19], to cite a few. Soil moisture is also the water source for plants and, hence, its knowledge is required for irrigation management and agricultural studies [20].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Brocca

Ciabatta

Massari

et al. 2017

Water

259

174

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Soil moisture is widely recognized as a key parameter in the mass and energy balance between the land surface and the atmosphere and, hence, the potential societal benefits of an accurate estimation of soil moisture are immense. Recently, scientific community is making great effort for addressing the estimation of soil moisture over large areas through in situ sensors, remote sensing and modelling approaches. The different techniques used for addressing the monitoring of soil moisture for hydrological applications are briefly reviewed here. Moreover, some examples in which in situ and satellite soil moisture data are successfully employed for improving hydrological monitoring and predictions (e.g., floods, landslides, precipitation and irrigation) are presented. Finally, the emerging applications, the open issues and the future opportunities given by the increased availability of soil moisture measurements are outlined.

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Section: Introductionmentioning

confidence: 99%

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Brocca

Ciabatta

Massari

et al. 2017

Water

259

174

View full text Add to dashboard Cite

show abstract

“…Drought is caused by various and 8-day, monthly, and yearly frequencies; this new index integrates satellite ET and NDVI. Recently, Enenkel et al [38] develop the Enhanced Combined Drought Index (ECDI) which link rainfall, soil moisture, land surface temperature and vegetation status.…”

Section: Introductionmentioning

confidence: 99%

Sixteen Years of Agricultural Drought Assessment of the BioBío Region in Chile Using a 250 m Resolution Vegetation Condition Index (VCI)

et al. 2016

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Drought is one of the most complex natural hazards because of its slow onset and long-term impact; it has the potential to negatively affect many people. There are several advantages to using remote sensing to monitor drought, especially in developing countries with limited historical meteorological records and a low weather station density. In the present study, we assessed agricultural drought in the croplands of the BioBío Region in Chile. The vegetation condition index (VCI) allows identifying the temporal and spatial variations of vegetation conditions associated with stress because of rainfall deficit. The VCI was derived at a 250 m spatial resolution for the 2000-2015 period with the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product. We evaluated VCI for cropland areas using the land cover MCD12Q1 version 5.1 product and compared it to the in situ Standardized Precipitation Index (SPI) for six-time scales (1-6 months) from 26 weather stations. Results showed that the 3-month SPI (SPI-3), calculated for the modified growing season (November-April) instead of the regular growing season (September-April), has the best Pearson correlation with VCI values with an overall correlation of 0.63 and between 0.40 and 0.78 for the administrative units. These results show a very short-term vegetation response to rainfall deficit in September, which is reflected in the vegetation in November, and also explains to a large degree the variation in vegetation stress. It is shown that for the last 16 years in the BioBío Region we could identify the 2007/2008, 2008/2009, and 2014/2015 seasons as the three most important drought events; this is reflected in both the overall regional and administrative unit analyses. These results concur with drought emergencies declared by the regional government. Future studies are needed to associate the remote sensing values observed at high resolution (250 m) with the measured crop yield to identify more detailed individual crop responses.

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“…3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 An alternative approach based on predetermined phenology dates (YF model) was used to predict crop yield. Estimated dates of emergence and maturity require phenology data in a region to determine the τ and ρ values for Equation (5). However, it would be difficult to use Equation (5) in a region where few phenology data are available.…”

Section: Corn Yield Predictionsmentioning

confidence: 99%

“…The ability to predict crop yield would help government agencies develop food security and trade policies [2][3][4]. Non-governmental organizations would also benefit from reliable crop yield predictions by allowing decisions to be made regarding food aid [5].…”

Section: Introductionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

Ban

Kim

Park

et al. 2016

Preprint

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

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A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

Cited by 55 publications

References 65 publications

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Sixteen Years of Agricultural Drought Assessment of the BioBío Region in Chile Using a 250 m Resolution Vegetation Condition Index (VCI)

Using MODIS Data to Predict Regional Corn Yields

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