Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Petropoulos, George P.; Srivastava, Prashant K.; Piles, María; Pearson, Simon

doi:10.3390/su10010181

Cited by 53 publications

(58 citation statements)

References 105 publications

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“…Currently, several satellite soil moisture operational products are available from microwave, optical, and thermal sensors (e.g., [20]. One of the most widely used ones includes that from the Soil Moisture Active and Passive (SMAP) satellite, which was launched in January 2015 starting from April 2015 with ∼36 km 2 /2-day spatial/temporal resolution [21].…”

Section: Introductionmentioning

confidence: 99%

“…It includes two aspects: one is that the result of the validation can be used as a feedback to algorithm developers for further improvements in the retrieval of SSM; the other is to facilitate the potential users to understand the status of the product, such as the accuracy, magnitude, and the uncertainties of the remote sensed products. Both are very important as they help in better understanding their potential use for practical applications (e.g., [20,31]). Additionally, long term soil moisture datasets are been used in hydrological and land surface modeling assimilation [26,32].…”

Section: Introductionmentioning

confidence: 99%

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Operational Soil Moisture from ASCAT in Support of Water Resources Management

et al. 2019

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This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational Soil Moisture from ASCAT in Support of Water Resources Management

et al. 2019

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“…Popular EO techniques include satellite and radar-based technologies [13] towards estimating basic biophysical parameters in the fields, such as Leaf Area Index (LAI), crop height and water requirements [14,15]. According to [16] radar-based technologies are used to enable the estimation of soil moisture spatial variability and can efficiently estimate LAI. However, EO can achieve sturdy higher spatial resolutions only with calibration against accurate ground truth instruments that measure at within field scale resolutions.…”

Section: The Sensor Layermentioning

confidence: 99%

Precision Agriculture: A Remote Sensing Monitoring System Architecture

2019

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Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

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“…Remote sensing is a viable alternative for the spatial estimation of evapotranspiration, assisting in decisionmaking at levels of agricultural plots (Bastiaanssen et al, 1998a;Allen et al, 2007;Teixeira, 2010). For the spatialized evapotranspiration information to become a differential, it is necessary to know how accurate the estimation of this variable is (Djaman et al, 2016;Petropoulos et al, 2016Petropoulos et al, , 2018, besides being relevant the knowledge of the influence of intermediate variables on the evapotranspiration phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Evapotranspiration Estimated by Orbital Images Under Influence of Surface Temperature

et al. 2019

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Surface temperature (Ts) is a determining factor to obtain energy balance parameters, being relevant to understand the influence of this variable on the estimation of evapotranspiration. Thus, the objective of this study was to simulate errors in Ts estimation to verify the consequences of actual evapotranspiration (ETa) estimated by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model. For this, an image of the Landsat-8 satellite was used to induce errors from 0.2K to 10K in the variable Ts, allowing verifying the consequences in the ETa data. After the estimations of Ts and ETa, the quantitative consequences and dynamics of Ts impact on the ETa data were verified along the different land uses in the study area. The results showed that the precise estimation of Ts is essential to obtain ETa accurately. The image of ETa errors presented the highest relative errors on the surface with exposed soils and with high Ts values. However, the highest residuals of ETa images occurred on the surfaces with milder Ts and higher evapotranspiration rates (irrigated surfaces).

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Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Cited by 53 publications

References 105 publications

Operational Soil Moisture from ASCAT in Support of Water Resources Management

Operational Soil Moisture from ASCAT in Support of Water Resources Management

Precision Agriculture: A Remote Sensing Monitoring System Architecture

Sensitivity of Evapotranspiration Estimated by Orbital Images Under Influence of Surface Temperature

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