Long-term projections of temperature, precipitation and soil moisture using non-stationary oscillation processes over the UAE region

2015 ◽  
Vol 35 (15) ◽  
pp. 4606-4618 ◽  
Author(s):  
Ghouse Basha ◽  
T. B.M.J. Ouarda ◽  
P. R. Marpu
Keyword(s):  
Soil Moisture ◽  
Stationary Oscillation

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

2021 ◽  
Vol 13 (4) ◽  
pp. 680
Author(s):  
Lei Wang ◽  
Wen Zhuo ◽  
Zhifang Pei ◽  
Xingyuan Tong ◽  
Wei Han ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arabian Peninsula ◽  
Vegetation Coverage ◽  
Observation Data ◽  
Desert Locust ◽  
Meteorological Observations ◽  
Earth Observation Data ◽  
Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.

scholarly journals Towards a long-term dataset of ELBARA-II measurements assisting SMOS level-3 land product and algorithm validation at the Valencia Anchor Station

2015 ◽  
pp. 55
Author(s):  
R. Fernandez Moran ◽  
J. P. Wigneron ◽  
E. Lopez-Baeza ◽  
M. Miernecki ◽  
P. Salgado-Hernanz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Anchor Station ◽  
Ocean Salinity ◽  
Algorithm Validation ◽  
Level 3

La misión de SMOS (Soil Moisture and Ocean Salinity) se lanzó el 2 de Noviembre de 2009 con el objetivo de proporcionar datos de humedad del suelo y salinidad del mar. La principal actividad de la conocida como Valencia Anchor Station (VAS) es asistir en la validación a largo plazo de productos de suelo de SMOS. El presente estudio se centra en una validación de datos de nivel 3 de SMOS en la VAS con medidas in situ tomadas en el periodo 2010-2012. El radiómetro Elbara-II está situado dentro de los confines de la VAS, observando un campo de viñedos que se considera representativo de una gran proporción de un área de 50×50 km, suficiente para cubrir un footprint de SMOS. Las temperaturas de brillo (TB) adquiridas por ELBARA-II se compararon con las observadas por SMOS en las mismas fechas y horas. También se utilizó la inversión del modelo L-MEB con el fin de obtener humedades de suelo (SM) que, posteriormente, se compararon con datos de nivel 3 de SMOS. Se ha encontrado una buena correlación entre ambas series de TB, con mejoras año tras año, achacable fundamentalmente a la disminución de precipitaciones en el periodo objeto de estudio y a la mitigación de las interferencias por radiofrecuencia en banda L. La mayor homogeneidad del footprint del radiómetro ELBARA-II frente al de SMOS explica la mayor variabilidad de sus TB. Los periodos de precipitación más intensa (primavera y otoño) también son de mayor SM, lo que corrobora la consistencia de los resultados de SM simulados a través de las observaciones del radiómetro. Sin embargo, se debe resaltar una subestimación por parte de SMOS de los valores de SM respecto a los obtenidos por ELBARA-II, presumiblemente debido a la influencia que la pequeña fracción de suelo no destinado al cultivo de la vid tiene sobre SMOS. Las estimaciones por parte de SMOS en órbita descendente (6 p.m.) resultaron de mayor calidad (mayor correlación y menores RMSE y bias) que en órbita ascendente (6 a.m., momento de mayor humedad de suelo).

scholarly journals Influence of Tillage on the Mollisols Physicochemical Properties, Seed Emergence and Yield of Maize in Northeast China

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 939
Author(s):  
Qiang Chen ◽  
Xingyi Zhang ◽  
Li Sun ◽  
Jianhua Ren ◽  
Yaru Yuan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Physicochemical Properties ◽  
Northeast China ◽  
Soil Structure ◽  
Aggregate Stability ◽  
Maize Yield ◽  
Conventional Tillage ◽  
Soil Conditions ◽  
Emergence Period

Tillage practices are critical for sustaining soil quality necessary for successful crop growth and productivity, but there are only few studies for strip tillage (ST) in the Mollisols region of Northeast China at present. A long-term (≥10-year) study was carried out to investigate the influence of within the tilled row (IR) and between rows (BR) in ST (10-year), conventional tillage (CT, 14-year) and no tillage (NT, 14-year) treatments on soil physicochemical properties. Soil samples were taken in May of 2019 at 0–5, 5–10, 10–20 and 20–30 cm depths and used to analyze bulk density (BD), soil aggregate distribution and stability, and soil organic carbon (SOC). Meanwhile, our study also explored the differences in seed emergence, soil moisture, and temperature during the seed emergence period, and yield of maize (Zea mays L.) among the different treatments. Similar soil properties were observed between ST-BR and NT, which showed they had a significantly greater BD, >0.25 mm water stable aggregate content (WR0.25) (especially in the amount of >2 mm and 1–2 mm size proportion), aggregate stability, and SOC than ST-IR and CT-IR at a depth of 0–20 cm. By improving soil conditions of seedbed, ST-IR and CT-IR increased soil temperature above NT by 1.64 °C and 1.80 °C, respectively, and ST-IR had a slight greater soil moisture than CT-IR in the top 10 cm layer during the seed emergence period. Late maize seed emergence was observed NT in than ST-IR and CT-IR and the average annual yields in ST were slightly greater than NT and CT, but the differences were not significant. Our results also showed that CT-BR had a poor soil structure and lower SOC than other treatments at 0–30 cm depth. We conclude from these long-term experimental results that ST could improve soil water-heat conditions to promote seed germination, maintain soil structure, and increase the maize yield and it should be applied in the Mollisols region of Northeast China.

scholarly journals The Influence of Some Phytotechnical Elements on Attaque of Diabrotica Virgifera Virgifera Le Conte (The Western Root Worm) in Maize from Crisurilor Plain

1970 ◽  
Vol 66 (1) ◽  
Author(s):  
Cornelia CIOBANU ◽  
Cornel DOMUŢA ◽  
Gheorghe CIOBANU ◽  
Maria ŞANDOR ◽  
Alina Dora SAMUEL ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Diabrotica Virgifera Virgifera ◽  
Crop Rotations ◽  
Wheat Crop ◽  
Diabrotica Virgifera ◽  
Term Trial ◽  
Lower Plant

The paper based on the researches carried out during 2005 – 2008 in the long term trial placed in 1990 on the preluvosoil from Oradea. The monoculture of maize, maize – wheat and maize – soybean – wheat crop rotations were studied in unirrigated and irrigated conditions. The crop rotatin of six years (oat + clover – clover – maize – wheat – maize - sunflower) was studied in unirrigated conditions. The researches emphasized that the monoculture is the most responsible for the pest multiplication. In maize monoculture the larvae number on the roots was ranged between 4.91-8.23 and root attack degree in IOWA scale (with marks from 1 to 6 in which maximum attack is 6), had values between 3.84 and 5.62 and the frequent of attacked plants with the symptom “goose neck” ranged between 16,4% and 31.2% and larval aggressiveness being higher in the case of favorable soil moisture in irrigated condition. The maize rotation with other plants interrupts the biologic pest cycle. The results obtained emphasize that later maize sowing alongside by the utilizing of lower plant thickness level contribute to prevention of pest multiplication, while earlier sowing at high thickness favor the larvae developing.

scholarly journals Soil moisture under no-tillage and tillage systems in maize long-term experiment

2018 ◽  
Vol 51 (1) ◽  
pp. 103
Author(s):  
Aleksandra Król ◽  
Tomasz Żyłowski ◽  
Jerzy Kozyra ◽  
Jerzy Księżak
Keyword(s):  
Soil Moisture ◽  
No Tillage ◽  
Tillage Systems ◽  
Term Experiment ◽  
Long Term Experiment

scholarly journals Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Bibiana Rodrigues Colossi ◽  
Carlos Eduardo Morelli Tucci
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Computing Time ◽  
Ensemble Member ◽  
Lead Times ◽  
Hydrologic Simulation ◽  
The Mean ◽  
Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

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