Synergy of Satellite-Derived Drought Indices for Agricultural Drought Quantification and Yield Prediction

2020 ◽  
pp. 122-148
Author(s):  
Dipti Ladli ◽  
Kanhaiya Lal ◽  
Kiran Jalem ◽  
Avinash Kumar Ranjan
Keyword(s):  
Condition Index ◽  
District Level ◽  
Drought Indices ◽  
Yield Prediction ◽  
Drought Condition ◽  
Yield Model ◽  
Yield Data ◽  
Moderate Drought ◽  
Vegetation Health Index ◽  
Paddy Yield

The present study was conducted over Jharkhand state (India) for assessing the drought condition and corresponding yield of paddy (district-level) during Kharif 2018. Vegetation drought indices, namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and vegetation indices (VI) anomaly, were derived from different VI (i.e., NDVI, EVI) to assess the paddy health condition during drought year (2018) and non-drought year (2017). Later, the correlation between the DES-based yield data and derived drought indices (for the year 2017) were made to develop the district-level paddy yield model for the drought year 2018. The key results of the study shown that VCI derived from EVI data was found to be more reasonable to depict the drought condition, wherein ~21% area was under severe drought condition, 43% area under moderate drought condition, and 36% area under no drought condition. In addition, the yield prediction model derived from VCI (EVI-based) was found to be promising for predicting the paddy yield for Kharif 2018 with fair R2 of 0.53.

scholarly journals Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

2020 ◽  
Vol 12 (3) ◽  
pp. 530 ◽  
Author(s):  
Yang Han ◽  
Ziying Li ◽  
Chang Huang ◽  
Yuyu Zhou ◽  
Shengwei Zong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Condition Index ◽  
Temporal Variations ◽  
Changbai Mountains ◽  
Drought Indices ◽  
Drought Condition ◽  
Meteorological Observations

Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

scholarly journals Mapping the Spatial and Temporal Variation of Agricultural and Metrological Drought Using Geospatial Techniques, Ethiopia

2020 ◽  
Author(s):  
Abebe Senamaw ◽  
Solomon Addisu ◽  
K.V. Suryabhagavan
Keyword(s):  
Temporal Variation ◽  
Crop Yield ◽  
Meteorological Drought ◽  
Condition Index ◽  
Agricultural Drought ◽  
Spatial And Temporal Variation ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Condition ◽  
Gis And Remote Sensing

Abstract Background Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The objective of this study was to assess spatial and temporal variation of agricultural and metrological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI). To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as SPI and crop yield anomaly. The results revealed that the year 2009 and 2015 were drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r=0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. ResultThe result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56%, 35% and 8% of study area were vulnerable to very severe, severe and moderate drought condition respectively. Conclusion In conclusion, the study area is highly vulnerable to agricultural and meteorological drought. Thus besides mapping drought vulnerable areas, integrating socioeconomic data for better understand other vulnerable factors were recommended.

scholarly journals UP-SCALING PADDY YIELD AT SATELLITE-FOOTPRINT SCALE USING SATELLITE DATA IN CONJUNCTION WITH CCE DATA IN SAHIBGANJ DISTRICT, JHARKHAND

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 235-238 ◽  
Author(s):  
B. R. Parida ◽  
A. K. Ranjan
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Monsoon Season ◽  
Survey Method ◽  
Yield Model ◽  
Yield Data ◽  
Time Saving ◽  
Aquacrop Model ◽  
Paddy Yield ◽  
Up Scaling

<p><strong>Abstract.</strong> Agriculture plays a vital role in the economy of India as almost half of the workforce dependent on agriculture and allied activities. Rice is an important staple food and provides nutritious need for the billions of population. Mapping the spatial distribution of paddy and predicting yields at district level aggregation are crucial for food security measures. This study has utilized the time-series MODIS-based Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data in conjunction with CCE data to derive a statistical model for up-scaling paddy yield at satellite-footprint scale over Sahibganj district in Kharif (monsoon) season 2017. The CCE data were collected from ten random paddy plots. In addition, Area, Production, and Yield (APY) data were collected during harvesting period by interacting with eighty farmers belong to eight villages. The AquaCrop model was also used to simulate the paddy yield for Kharif season. The key results showed that based on the farmers-based yield data, paddy yield was observed as ~3200&amp;thinsp;kg/hectare, whereas, NDVI and EVI-based yield models based on satellite data showed about 2,960 and 3,530 kg/hectare, respectively. Moreover, multi-regression-based yield model showed the mean yield of 3,070&amp;thinsp;kg/hectare. With respect to farmers-level yield data, the relative deviation (RD) of yield based on NDVI data was &amp;minus;7.5% (underestimation), while EVI was 10.31% (overestimation). The multi-regression-based yield model and AquaCrop model were underestimated by &amp;minus;4.06 and &amp;minus;10.16%, respectively. Thus, it can be inferred that the multi-regression-based yield was close to farmers-based survey yields. It can be concluded that the satellite databased yield prediction can be reliable with &amp;plusmn;&amp;thinsp;10% of RD. Nevertheless, remote sensing technology can be beneficial over traditional survey method as the satellite-based methods are cost-effective, robust, reliable, and time-saving than the traditional methods.</p>

scholarly journals ASSESSMENT AND MONITORING OF AGRICULTURAL DROUGHTS IN MAHARASHTRA USING METEOROLOGICAL AND REMOTE SENSING BASED INDICES

2018 ◽  
Vol IV-5 ◽  
pp. 253-264 ◽  
Author(s):  
P. V. Aswathi ◽  
B. R. Nikam ◽  
A. Chouksey ◽  
S. P. Aggarwal
Keyword(s):  
Remote Sensing ◽  
Meteorological Drought ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Drought Assessment ◽  
Sparse Vegetation ◽  
Vegetation Health Index ◽  
Agricultural Yield ◽  
Remote Sensing Methods

<p><strong>Abstract.</strong> Drought is a recurring climatic event characterized by slow onset, a gradual increase in its intensity, and persistence for a long period depending upon the availability of water. Droughts, broadly classified into meteorological, hydrological and agricultural drought, which are interconnected to each other. India, being an agriculture based economy depends primarily on agriculture production for its economic development and stability. The occurrence of agriculture drought affects the agricultural yield, which affects the regional economy to a larger extent. In present study, agricultural and meteorological drought in Maharashtra state was monitored using traditional as well as remote sensing methods. The meteorological drought assessment and characterization is done using two standard meteorological drought indices viz. standard precipitation index (SPI) and effective drought index (EDI). The severity and persistency of meteorological drought were studied using SPI for the period 1901 to 2015. However, accuracy of SPI in detection of sub-monthly drought is limited. Therefore, sub-monthly drought is effectively monitored using EDI. The monthly and sub-monthly drought mapped using SPI and EDI, respectively were then compared and assessed. It was concluded that EDI serves as a better indicator to monitor sub-monthly droughts. The agricultural drought monitoring was carried out using the remote sensing based indices such as vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), shortwave angle slope index (SASI) and the index which maps the agricultural drought in a better way was identified. The area under drought as calculated by various agricultural drought indices compared with that of the EDI, it was found that the results of SASI matched with results of EDI. SASI denotes different values for the dry and wet soil and for the healthy and sparse vegetation. SASI monitors the agricultural drought better as compared to other indices used in this study.</p>

scholarly journals An Agricultural Drought Index for Assessing Droughts Using a Water Balance Method: A Case Study in Jilin Province, Northeast China

2019 ◽  
Vol 11 (9) ◽  
pp. 1066 ◽  
Author(s):  
Yijing Cao ◽  
Shengbo Chen ◽  
Lei Wang ◽  
Bingxue Zhu ◽  
Tianqi Lu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Potential Evapotranspiration ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Growing Seasons ◽  
Dryness Index ◽  
Vegetation Health Index

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed.

Satellite soil moisture for yield prediction in water limited regions 

2021 ◽  
Author(s):  
Mariette Vreugdenhil ◽  
Isabella Pfeil ◽  
Luca Brocca ◽  
Stefania Camici ◽  
Markus Enenkel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Early Warning ◽  
Growing Season ◽  
Early Warning Systems ◽  
Yield Prediction ◽  
Warning Systems ◽  
Yield Data ◽  
Disaster Risk Financing

&lt;div&gt; &lt;p&gt;Accurate and reliable&amp;#160;early warning systems can&amp;#160;support&amp;#160;anticipatory&amp;#160;disaster risk financing&amp;#160;which&amp;#160;can be more cost effective than post-disaster&amp;#160;emergency response.&amp;#160;One of the challenges in&amp;#160;anticipatory&amp;#160;disaster risk financing is basis risk, as a result of&amp;#160;data and&amp;#160;model uncertainty.&amp;#160;The increasing availability of Earth Observation&amp;#160;(EO)&amp;#160;data provides the opportunity to&amp;#160;develop shadow models or include different variables in early warning systems&amp;#160;and weather index insurance. Especially of interest is the early indication of&amp;#160;climate impacts on agricultural production.&amp;#160;Traditionally, crop and yield prediction models&amp;#160;use meteorological data such as precipitation and temperature, or&amp;#160;optical based indicators&amp;#160;such as&amp;#160;Normalized&amp;#160;Difference&amp;#160;Vegetation&amp;#160;Index (NDVI), for yield prediction.&amp;#160;&amp;#160;In recent years, soil moisture has gained popularity for yield prediction as it controls the water availability for plants.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;Here, we will present the use of different satellite-based rainfall and soil moisture products, in combination with NDVI, to develop a yield deficiency indicator over two water limited regions. An analysis for Senegal and Morocco is performed at the national level using yield data of four major crops from the Food and Agriculture Organization of the United Nations. Freely available EO datasets for rainfall, soil moisture, root zone soil moisture and NDVI were used. All datasets were spatially resampled to a 0.1&amp;#176; grid, temporally aggregated to monthly anomalies and finally detrended and standardized. First, regression analysis with yearly yield was performed per EO dataset for single months. For this, EO datasets where aggregated over areas where the specific crop was grown. Secondly, based on these results multiple linear regression was performed using the months and variables with the highest explanatory power. The multiple linear regression was used to provide spatially varying yield predictions by trading time for space. The spatial predictions were validated using sub-national yield data from Senegal.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;The analysis&amp;#160;demonstrates the added-value of&amp;#160;satellite&amp;#160;soil&amp;#160;moisture for&amp;#160;early yield prediction.&amp;#160;Both in Senegal and Morocco&amp;#160;rainfall and&amp;#160;soil moisture&amp;#160;showed&amp;#160;a high predictive&amp;#160;skill&amp;#160;early in the growing season: negative early season soil moisture anomalies often lead to low yield. NDVI&amp;#160;showed&amp;#160;more predictive power later in the growing season.&amp;#160;For example, in Morocco soil moisture at the start of the season can already explain 56% of the variability in yield. NDVI&amp;#160;can explain 80% of the yield, however this is at the&amp;#160;end of the growing season.&amp;#160;Combining&amp;#160;anomalies of the&amp;#160;optimal months&amp;#160;based on&amp;#160;the&amp;#160;different variables in multiple linear regression&amp;#160;improved yield prediction. Again,&amp;#160;including NDVI&amp;#160;led&amp;#160;to higher predictive power, at the cost of early warning.&amp;#160;&amp;#160;This analysis shows very clearly that soil moisture&amp;#160;can be&amp;#160;a valuable tool&amp;#160;for&amp;#160;anticipatory&amp;#160;drought risk financing and early warning systems.&amp;#160;&lt;/p&gt; &lt;/div&gt;

scholarly journals Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
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.

Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia

2019 ◽  
Vol 231 ◽  
pp. 111220 ◽  
Author(s):  
Hao Guo ◽  
Anming Bao ◽  
Tie Liu ◽  
Felix Ndayisaba ◽  
Liangliang Jiang ◽  
...  
Keyword(s):  
Central Asia ◽  
Condition Index ◽  
Drought Condition ◽  
Variable Weights

scholarly journals Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

2018 ◽  
Vol 10 (11) ◽  
pp. 1834 ◽  
Author(s):  
Fei Wang ◽  
Zongmin Wang ◽  
Haibo Yang ◽  
Yong Zhao ◽  
Zhenhong Li ◽  
...  
Keyword(s):  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Drought Indices ◽  
Spatio Temporal ◽  
The Yellow River Basin ◽  
Meteorological Droughts

Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio–temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky–Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero–three months compared with the meteorological drought index.

scholarly journals To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction

2020 ◽  
Vol 12 (10) ◽  
pp. 1653
Author(s):  
Yang Chen ◽  
Tim R. McVicar ◽  
Randall J. Donohue ◽  
Nikhil Garg ◽  
François Waldner ◽  
...  
Keyword(s):  
Crop Yield ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Crop Growth ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Landsat Images ◽  
Yield Model ◽  
Pixel Resolution ◽  
Country Level

The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.

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