scholarly journals Ambiguous Agricultural Drought: Characterising Soil Moisture and Vegetation Droughts in Europe from Earth Observation

2021 ◽  
Vol 13 (10) ◽  
pp. 1990
Author(s):  
Theresa C. van Hateren ◽  
Marco Chini ◽  
Patrick Matgen ◽  
Adriaan J. Teuling
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Heat Waves ◽  
Growing Season ◽  
Earth Observation ◽  
Agricultural Drought ◽  
Positive Anomaly ◽  
Surface Soil Moisture ◽  
Modis Ndvi ◽  
The Past

Long-lasting precipitation deficits or heat waves can induce agricultural droughts, which are generally defined as soil moisture deficits that are severe enough to negatively impact vegetation. However, during short soil moisture drought events, the vegetation is not always negatively affected and sometimes even thrives. Due to this duality in agricultural drought impacts, the term “agricultural drought” is ambiguous. Using the ESA’s remotely sensed CCI surface soil moisture estimates and MODIS NDVI vegetation greenness data, we show that, in major European droughts over the past two decades, asynchronies and discrepancies occurred between the surface soil moisture and vegetation droughts. A clear delay is visible between the onset of soil moisture drought and vegetation drought, with correlations generally peaking at the end of the growing season. At lower latitudes, correlations peaked earlier in the season, likely due to an earlier onset of water limited conditions. In certain cases, the vegetation showed a positive anomaly, even during soil moisture drought events. As a result, using the term agricultural drought instead of soil moisture or vegetation drought, could lead to the misclassification of drought events and false drought alarms. We argue that soil moisture and vegetation drought should be considered separately.

scholarly journals Ambiguous agricultural drought: characterising soil moisture and vegetation droughts in Europe from earth observation

2020 ◽  
Author(s):  
Theresa C. van Hateren ◽  
Marco Chini ◽  
Patrick Matgen ◽  
Adriaan J. Teuling
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Heat Waves ◽  
Growing Season ◽  
Earth Observation ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Positive Anomaly ◽  
The Past ◽  
Near Future

Abstract. Climate change will likely lead to more regular and more severe drought events in the near future, with large impacts on agriculture, especially during long-lasting precipitation deficits or heat waves. This study focuses on agricultural droughts, which are generally defined as soil moisture deficits so severe, that vegetation is negatively impacted. However, during short soil moisture drought events, vegetation is not always negatively affected, and sometimes even thrives under these conditions. Because of this duality in agricultural drought impacts, the use of the term agricultural droughts is ambiguous. Here we show that, in major European droughts over the past two decades, clear asynchronies and discrepancies occur between soil moisture and vegetation anomalies. A clear delay is visible between the onset of soil moisture drought and vegetation drought, and correlation between the two types of drought generally peaks at the end of the growing season. This behaviour seems to be different in droughts at lower latitudes, where correlations peak earlier in the season, likely due to water limited conditions occurring much earlier there. Moreover, results indicate that in some cases, vegetation can show a positive anomaly, even when soil moisture anomalies are negative. As a result, the use of the term agricultural drought could lead to misclassification of drought events and false drought alarms depending on whether vegetation or soil moisture is used to quantify the drought. We argue that it is necessary to make a distinction between soil moisture drought and anomalies in vegetation.

scholarly journals The spatiotemporal patterns of surface soil moisture in Northeast China based on remote sensing products

2016 ◽  
Vol 7 (4) ◽  
pp. 708-720 ◽  
Author(s):  
Xingming Zheng ◽  
Kai Zhao ◽  
Yanling Ding ◽  
Tao Jiang ◽  
Shiyi Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Northeast China ◽  
Surface Soil ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
The Past ◽  
Warming Rate

Northeast China (NEC) has become one of China's most obvious examples of climate change because of its rising warming rate of 0.35 °C/10 years. As the indicator of climate change, the dynamic of surface soil moisture (SSM) has not been assessed yet. We investigated the spatiotemporal dynamics of SSM in NEC using a 32-year SSM product and found the following. (1) SSM displayed the characteristics of being dry in the west and wet in the east and decreased with time. (2) The seasonal difference was found for the temporal dynamics of SSM: it increased in summer and decreased in spring and autumn. (3) For all four regions studied, the temporal dynamics of SSM were similar to those of the whole of NEC, but with different rates of SSM change. Moreover, SSM in regions B and D had a lower spatial variance than the other two regions because of the stable spatial pattern of cropland. (4) The change rates for SSM were consistent with that observed for the warming rates, which indicated that SSM levels derived from remote sensing data will correlate with climate change. In summary, a wetter summer and a drier spring and autumn were observed in NEC over the past 30 years.

scholarly journals Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979-2010)

2016 ◽  
Vol 121 (10) ◽  
pp. 5177-5192 ◽  
Author(s):  
Xiuzhi Chen ◽  
Yongxian Su ◽  
Jishan Liao ◽  
Jiali Shang ◽  
Taifeng Dong ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Eastern China ◽  
Surface Soil Moisture ◽  
The Past

scholarly journals Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

2020 ◽  
Vol 12 (9) ◽  
pp. 1455
Author(s):  
Yaasiin Oozeer ◽  
Christopher G. Fletcher ◽  
Catherine Champagne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Surface Soil ◽  
European Space Agency ◽  
Agricultural Drought ◽  
Surface Soil Moisture ◽  
Large Variability ◽  
Time Space ◽  
Agricultural Regions

Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3.

scholarly journals Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 236
Author(s):  
Laura Almendra-Martín ◽  
José Martínez-Fernández ◽  
Ángel González-Zamora ◽  
Pilar Benito-Verdugo ◽  
Carlos Miguel Herrero-Jiménez
Keyword(s):  
Soil Moisture ◽  
Iberian Peninsula ◽  
Clay Content ◽  
Growing Season ◽  
Soil Characteristics ◽  
Agricultural Drought ◽  
Positive Trend ◽  
The Past ◽  
Agriculture And Food ◽  
Clear Predominance

Drought has a great impact on agriculture and food security, and climate change is increasing its frequency and exacerbating its intensity. Given the enormous interest in studying the recent drought evolution, in this work, agricultural drought trends over the past four decades on the Iberian Peninsula (IP) were analyzed. A trend analysis was performed with soil moisture based on the study of the evolution of anomalies and the Soil Water Deficit Index (SWDI). Two soil moisture databases (Lisflood and ERA5-Land) were used and the analysis was performed at daily and weekly temporal scales. Climate characteristics and soil properties were also considered to detect whether a trend spatial pattern could be identified. The results have shown a clear predominance of negative trends. A marked temporal pattern with negative trends was obtained over a 10-month period that coincided with the growing season of most of the crops on the IP, while a positive trend was observed over 2 months. No differences were found based on the climatic zone or soil characteristics. However, negative trends were observed to decrease as the clay content increased. These results can provide useful information for better water management and agriculture of the IP and other Mediterranean areas.

scholarly journals Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

2020 ◽  
Vol 12 (6) ◽  
pp. 1038
Author(s):  
Lei Wang ◽  
Shibo Fang ◽  
Zhifang Pei ◽  
Yongchao Zhu ◽  
Dao Nguyen Khoi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Regional Scale ◽  
Back Propagation ◽  
The United States ◽  
Vegetation Coverage ◽  
Surface Soil Moisture ◽  
Modis Ndvi ◽  
Lower Accuracy

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.

scholarly journals Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1956 ◽  
Author(s):  
Minfeng Xing ◽  
Binbin He ◽  
Xiliang Ni ◽  
Jinfei Wang ◽  
Gangqiang An ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Cloud Model ◽  
Growing Season ◽  
Retrieval Algorithm ◽  
Surface Soil Moisture ◽  
Covered Area ◽  
Water Cloud ◽  
Sar Data ◽  
Underlying Soil

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

scholarly journals Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

2019 ◽  
Vol 11 (4) ◽  
pp. 372 ◽  
Author(s):  
Catherine Champagne ◽  
Jenelle White ◽  
Aaron Berg ◽  
Stephane Belair ◽  
Marco Carrera
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Crop Yields ◽  
Agricultural Drought ◽  
Data Sets ◽  
Surface Soil Moisture ◽  
Short Baseline ◽  
Data Set ◽  
Soil Moisture Data ◽  
The Relationship

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

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