The utility of surface temperature measurements for the remote sensing of surface soil water status

1975 ◽  
Vol 80 (21) ◽  
pp. 3044-3049 ◽  
Author(s):  
S. B. Idso ◽  
T. J. Schmugge ◽  
R. D. Jackson ◽  
R. J. Reginato
2021 ◽  
Author(s):  
Sandra María Martínez-Pedreño ◽  
Pablo Berríos ◽  
Abdelmalek Temnani ◽  
Susana Zapata ◽  
Manuel Forcén ◽  
...  

<p>In water scarcity areas, it is necessary not only reducing the water applied as much as possible, but also optimizing nutrients application to avoid soil salinization and aquifers pollution because of leaching bellow the root zone. Increasing the sustainability of fertirrigation needs technology to adjust the irrigation time, knowing more precisely the soil water retention capacity and facilitate water absorption by the crop. The aim of this trial was to establish protocols for sustainable fertirrigation in melon crop under semi-arid conditions, both at an environmental and economic level, based on the use of soil water status indicators measured by sensors that allow us to increase the irrigation water use efficiency. Two irrigation treatments were established: i) Control (CTL), irrigated to satisfy the water requirements of the crop, according to the farmer's criterion throughout the crop cycle and ii) DI, deficit irrigation, irrigated to allow a maximum soil water depletion of 20%, with respect to field capacity throughout the crop cycle, from sensors located below the 20 cm depth horizon, in order to limit water leaching into the soil. An experimental design was established with 4 repetitions per treatment distributed at random, with 5 plants per repetition. Macro and micronutrients concentration of soil solution, leaves and fruits were analysed. The crop water status was determined fortnightly by measurements taken at solar midday of stem water potential, net photosynthesis, evapotranspiration rate and leaf conductance. Whereas photosynthetically active radiation absorption, basal stem and fruit equatorial diameters were determined to estimate plant and fruit growth. The physical (longitudinal and equatorial fruit diameters, fruit weight, pulp width and firmness) and chemical (titratable acidity, pH and total soluble solid of the juice, total phenolic content, total antioxidant capacity and total ascorbic acid) characteristics of harvested fruits were determined. Total water applied in CTL treatment was 3,254 m<sup>3</sup> ha<sup>-1</sup> throughout the crop cycle whereas DI received 2,284 m<sup>3</sup> ha<sup>-1</sup>, a 29.8% lower. In both cases, the volume of water applied was lower than recommended by FAO. The regulation of the irrigation time in the DI treatment respect to the CTL promoted a reduction of the soil water content from 30 cm depth, mitigating the water loss below the root system, along with a lower contribution of nutrients, around of 43, 41.8 and 22% of N, P and K, respectively, and less salinization of the soil profile. No significant difference between treatments was detected in the concentration of these nutrients at leaf level. No difference was observed at harvest, with 0.53 and 0.59 g fruit g<sup>-1</sup> total dry mass of harvest index in CTL and DI, respectively. Fruit quality was not negatively affected in DI but improved since ascorbic acid was higher. This means that DI treatment not only did not negatively affect the crop water status and the amount and quality of the yield, but also improved its biochemical quality while reducing water and nutrients use and leaching.</p>


2020 ◽  
Vol 12 (8) ◽  
pp. 1242 ◽  
Author(s):  
Sumanta Chatterjee ◽  
Jingyi Huang ◽  
Alfred E. Hartemink

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.


2020 ◽  
Vol 290 ◽  
pp. 107988
Author(s):  
Fei Li ◽  
Guibiao Yang ◽  
Yunfeng Peng ◽  
Guanqin Wang ◽  
Shuqi Qin ◽  
...  

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