scholarly journals Soil Salinity Assessment in Irrigated Paddy Fields of the Niger Valley Using a Four-Year Time Series of Sentinel-2 Satellite Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3399
Author(s):  
Issaka Moussa ◽  
Christian Walter ◽  
Didier Michot ◽  
Issifou Adam Boukary ◽  
Hervé Nicolas ◽  
...  
Keyword(s):  
Grain Yield ◽  
Temporal Resolution ◽  
Soil Salinity ◽  
Bare Soil ◽  
Paddy Fields ◽  
Soil Conditions ◽  
High Temporal Resolution ◽  
Total Biomass ◽  
Growing Seasons ◽  
Sentinel 2

Salinization is a major soil degradation threat in irrigated systems worldwide. Irrigated systems in the Niger River basin are also affected by salinity, but its spatial distribution and intensity are not currently known. The aim of this study was to develop a method to detect salt-affected soils in irrigated systems. Two complementary approaches were tested: salinity assessment of bare soils using a salinity index (SI) and monitoring of indirect effects of salinity on rice growth using temporal series of a vegetation index (NDVI). The study area was located south of Niamey (Niger) in two irrigated systems of rice paddy fields that cover 6.5 km2. We used remote-sensing and ground-truth data to relate vegetation behavior and reflectance to soil characteristics. We explored all existing Sentinel-2 images from January 2016 to December 2019 and selected cloud-free images on 157 dates that covered eight successive rice-growing seasons. In the dry season of 2019, we also sampled 44 rice fields, collecting 147 biomass samples and 180 topsoil samples from January to June. For each field and growing season, time-integrated NDVI (TI-NDVI) was estimated, and the SI was calculated for dates on which bare soil conditions (NDVI < 0.21) prevailed. Results showed that since there were few periods of bare soil, SI could not differentiate salinity classes. In contrast, the high temporal resolution of Sentinel-2 images enabled us to describe rice-growing conditions over time. In 2019, TI-NDVI and crop yields were strongly correlated (r = 0.77 with total biomass yield and 0.82 with grain yield), while soil electrical conductivity was negatively correlated with both TI-NDVI (r = −0.38) and crop yield (r = −0.23 with total biomass and r = −0.29 with grain yield). Considering the TI-NDVI data from 2016–2019, principal component analysis followed by ascending hierarchical classification identified a typology of five clusters with different patterns of TI-NDVI during the eight growing seasons. When applied to the entire study area, this classification clearly identified the extreme classes (i.e., areas with high or no salinity). Other classes with low TI-NDVI (i.e., during dry seasons) may be related to areas with moderate or seasonal soil salinity. Finally, the high temporal resolution of Sentinel-2 images enabled us to detect stresses on vegetation that occurred repeatedly over the growing seasons, which may be good indicators of soil constraints due to salinity in the context of the irrigated paddy systems of Niger. Further research will validate the ability of the method developed to detect moderate soil salinity constraints over large areas.

scholarly journals Performances of Sheet-Pipe Typed Subsurface Drainage on Land and Water Productivity of Paddy Fields in Indonesia

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Chusnul Arif ◽  
Budi Indra Setiawan ◽  
Satyanto Krido Saptomo ◽  
Hiroshi Matsuda ◽  
Koremasa Tamura ◽  
...  
Keyword(s):  
Grain Yield ◽  
Water Productivity ◽  
Type System ◽  
Soil Surface ◽  
Subsurface Drainage ◽  
Crop Productivity ◽  
Paddy Fields ◽  
Soil Conditions ◽  
Rice Varieties ◽  
Soil Aeration

Subsurface drainage technology may offer a useful option in improving crop productivity by preventing water-logging in poor drainage paddy fields. The present study compared two paddy fields with and without sheet-pipe type subsurface drainage on land and water productivities in Indonesia. Sheet-pipe typed is perforated plastic sheets with a hole diameter of 2 mm and made from high-density polyethylene. It is commonly installed 30–50 cm below the soil surface and placed horizontally by a machine called a mole drainer, and then the sheets will automatically be a capillary pipe. Two fields were prepared, i.e., the sheet-pipe typed field (SP field) and the non-sheet-pipe typed field (NSP field) with three rice varieties (Situ Bagendit, Inpari 6 Jete, and Inpari 43 Agritan). In both fields, weather parameters and water depth were measured by the automatic weather stations, soil moisture sensors and water level sensors. During one season, the SP field drained approximately 45% more water compared to the NSP field. Thus, it caused increasing in soil aeration and producing a more significant grain yield, particularly for Inpari 43 Agritan. The SP field produced a 5.77 ton/ha grain yield, while the NSP field was 5.09 ton/ha. By producing more grain yield, the SP field was more effective in water use as represented by higher water productivity by 20%. The results indicated that the sheet-pipe type system developed better soil aeration that provides better soil conditions for rice.

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

Ground-Based Dual-Frequency Radiometry of Bare Soil at High Temporal Resolution

2004 ◽  
Vol 42 (3) ◽  
pp. 588-595 ◽  
Author(s):  
K. Schneeberger ◽  
C. Stamm ◽  
C. Matzler ◽  
H. Fluhler
Keyword(s):  
Temporal Resolution ◽  
Bare Soil ◽  
High Temporal Resolution ◽  
Dual Frequency

scholarly journals Phenotypic and Physiological Evaluation of Wheat Genotypes under Non-Saline and Saline Soil Conditions

2021 ◽  
pp. 29-43
Author(s):  
Hamada Amer ◽  
Mohamed Z. Dakroury ◽  
Ibrahim S. El Basyoni ◽  
Hanaa M. Abouzied
Keyword(s):  
Grain Yield ◽  
Saline Soil ◽  
Block Design ◽  
Soil Conditions ◽  
Salt Tolerant ◽  
Wheat Genotypes ◽  
Growing Seasons ◽  
Randomized Complete Block Design ◽  
Salinity Levels ◽  
Physiological Evaluation

This study was conducted to assess the effect of soil salinity on leaf area (LA), the number of days to flowering (DF), plant height (PH), and grain yield. Overall, 60 wheat genotypes were used, including 49 CIMMYT elite lines and 11 commercially grown Egyptian wheat cultivars. During two growing seasons (2017 and 2018), the genotypes were grown in non-saline (S0) and saline (S1) soils. A randomized complete block design with three replicates was used in a split-plot arrangement. Salinity levels were randomly assigned to the main plots, while genotypes were randomly assigned to the subplots. The obtained results showed that the saline soil adversely affected the evaluated genotypes. Furthermore, a highly significant effect of genotypes × salinity was observed on grain yield and its attributed traits. Based on salinity indices results, some of the imported wheat genotypes outperformed the Egyptian cultivars in grain yield under salinity stress conditions. The results further indicated that Sakha-93, C-31, and C-40 were the most salt-tolerant genotypes. The best performing line among the CIMMYT lines was C-31, which recorded the highest grain yield under none-saline and saline soil in the two seasons of study.

Overexpression of the Arabidopsis vacuolar H+-pyrophosphatase AVP1 gene in rice plants improves grain yield under paddy field conditions

2013 ◽  
Vol 152 (6) ◽  
pp. 941-953 ◽  
Author(s):  
Y. S. KIM ◽  
I. S. KIM ◽  
Y. H. CHOE ◽  
M. J. BAE ◽  
S. Y. SHIN ◽  
...  
Keyword(s):  
Grain Yield ◽  
Transgenic Rice ◽  
Inorganic Pyrophosphatase ◽  
Transgenic Crop ◽  
Total Biomass ◽  
Rice Grain ◽  
Rice Plants ◽  
Growing Seasons ◽  
Transgenic Rice Plants ◽  
Photochemical Yield

SUMMARYThe Arabidopsis gene AVP1 encodes a vacuolar H+-translocating inorganic pyrophosphatase (enzyme classification (EC) 3.6.1.1) that functions as an electronic proton pump in the vacuolar membrane and affects growth development and the stress response in plants. The current study was conducted to evaluate the molecular properties of the Arabidopsis thaliana vacuolar H+-pyrophosphatase (AVP1) gene in rice (Oryza sativa L.). Incorporation and expression of the transgene was confirmed by semi-quantitative reverse-transcription polymerase chain reaction (RT-PCR) and quantitative real-time PCR. Expression of the AVP1 gene in transgenic rice plants (TRP1 and TRP2) resulted in a significantly enhanced tolerance to 5·8 g/l NaCl under greenhouse conditions when compared with the control wild-type (WT) rice plants. Augmented AVP1 expression in the transgenic rice plants (TRP) also affected total biomass and improved ion homoeostasis through increased accumulation of Na+ ions in whole tissues when compared with control WT rice plants under high salinity conditions. The photochemical yield (Fv/Fm) values of TRP were higher than those of the WT rice plants, even though the values decreased over time in both the WT and transgenic (TRP1 to TRP8) rice plants. Furthermore, rice grain yield and biomass of the TRP were at least 15% higher based on culm and root weights, and panicle and spikelet numbers when compared with those of the WT rice plants during the 2010 and 2010 growing seasons in South Korea. Thus, these results suggest that ectopic AVP1 expression conferred tolerance and stress resistance to genetically modified transgenic crop plants by improving cellular ion homoeostasis in response to saline conditions, which enhanced rice yield and biomass under natural conditions in paddy fields.

scholarly journals Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

2018 ◽  
Author(s):  
Andrew G. Williamson ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Greenland Ice Sheet ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Dual Sensor ◽  
Sentinel 2

Abstract. Although remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet, most satellite records must trade-off high spatial resolution for high temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (~ 3 days). We achieve this by mosaicking imagery from the Landsat 8 OLI with imagery from the recently launched Sentinel-2 MSI for a ~ 12 000 km2 area of West Greenland in summer 2016. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2 = 0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤ 4 day) lake-drainage identification. With the dual Sentinel-2-Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes 

scholarly journals Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

2019 ◽  
Vol 11 (22) ◽  
pp. 2599 ◽  
Author(s):  
Markus Immitzer ◽  
Martin Neuwirth ◽  
Sebastian Böck ◽  
Harald Brenner ◽  
Francesco Vuolo ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Tree Species ◽  
European Space Agency ◽  
Feature Reduction ◽  
High Temporal Resolution ◽  
Detailed Knowledge ◽  
Classification Models ◽  
Central European ◽  
Multi Temporal ◽  
Sentinel 2

Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

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