Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry

The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (r = 0.62) compared to GRI (r = 0.56) and NDVI (r = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (p = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (p = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment.

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Seasonal dynamics of lingonberry and blueberry spectra

Silva Fennica ◽

10.14214/sf.10150 ◽

2019 ◽

Vol 53 (2) ◽

Author(s):

Petri Forsström ◽

Jouni Peltoniemi ◽

Miina Rautiainen

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Reference Data ◽

Leaf Growth ◽

Vegetation Indices ◽

Growing Season ◽

Moisture Stress ◽

Stress Index ◽

Infrared Spectral

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350â2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Instituteâs FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

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Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

Remote Sensing ◽

10.3390/rs13050907 ◽

2021 ◽

Vol 13 (5) ◽

pp. 907

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Lukáš Vlček ◽

Robert Minařík

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Data ◽

Groundwater Level ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sampling Strategy ◽

Peat Bog ◽

Strong Impact ◽

Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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Characterizing waste disposal sites by using multi-spectral satellite imagery

10.5194/egusphere-egu21-12495 ◽

2021 ◽

Author(s):

Gaetana Ganci ◽

Annalisa Cappello ◽

Giuseppe Bilotta ◽

Giuseppe Pollicino ◽

Luigi Lodato

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Waste Disposal ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

The State ◽

Dump Site ◽

Temperature State ◽

Surrounding Areas

The application of remote sensing for monitoring, detecting and analysing the spatial and extents and temporal changes of waste dumping sites and landfills could become a cost-effective and powerful solution. Multi-spectral satellite images, especially in the thermal infrared, can be exploited to characterize the state of activity of a landfill.&#160; Indeed, waste disposal sites, during the period of activity, can show differences in surface temperature (LST, Land Surface Temperature), state of vegetation (estimated through NDVI, Normalized Difference Vegetation Index) or soil moisture (estimated through NDWI, Normalized Difference Water Index) compared to neighboring areas. Landfills with organic waste typically show higher temperatures than surrounding areas due to exothermic decomposition activities. In fact, the biogas, in the absence or in case of inefficiency of the conveying plants, rises through the layers of organic matter and earth (landfill body) until it reaches the surface at a temperature of over 40 &#176; C. Moreover, in some cases, leachate contamination of the aquifers can be identified by analyzing the soil moisture, through the estimate of the NDWI, and the state of suffering of the vegetation surrounding the site, through the estimate of the NDVI. This latter can also be an indicator of soil contamination due to the presence of toxic and potentially dangerous waste when buried or present nearby. To take into account these facts, we combine the LST, NDVI and NDWI indices of the dump site and surrounding areas in order to characterize waste disposal sites. Preliminary results show how this approach can bring out the area and level of activity of known landfill sites. This could prove particularly useful for the definition of intervention priorities in landfill remediation works.

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Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽

10.1614/ws-05-54.2.346 ◽

2006 ◽

Vol 54 (02) ◽

pp. 346-353 ◽

Cited By ~ 41

Author(s):

Francisca López-Granados ◽

Montse Jurado-Expósito ◽

Jose M. Peña-Barragán ◽

Luis García-Torres

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Research ◽

Aerial Images ◽

Wild Oat ◽

Late Season ◽

High Resolution Satellite Imagery ◽

Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

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A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

10.31219/osf.io/65xkt ◽

2020 ◽

Author(s):

Toby N. Carlson ◽

George Petropoulos

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Heat Fluxes ◽

Surface Model ◽

Landsat 8 ◽

Simple Method ◽

Practical Applications ◽

Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

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Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Remote Sensing ◽

10.3390/rs10081293 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1293 ◽

Cited By ~ 26

Author(s):

Yunpeng Luo ◽

Tarek S. El-Madany ◽

Gianluca Filippa ◽

Xuanlong Ma ◽

Bernhard Ahrens ◽

...

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Plant Phenology ◽

Mediterranean Ecosystem ◽

Near Infrared Reflectance ◽

Seasonally Dry ◽

Growing Season Length ◽

Dry Down

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

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Environmental benefits of traditional irrigation ditches in the Sierra Nevada (Spain) ecosystem by analysing the spatial-temporal evolution of NDVI on different time scales

10.5194/egusphere-egu21-15870 ◽

2021 ◽

Author(s):

Javier Aparicio ◽

Rafael Pimentel ◽

María José Polo

Keyword(s):

Soil Moisture ◽

Sierra Nevada ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Global Analysis ◽

Environmental Benefits ◽

Mountain Range ◽

Mountain Area ◽

Traditional Irrigation ◽

Landsat Satellite

In Mediterranean mountain regions, traditional irrigation systems still persist in areas where the&#160; modernization approaches do not succeed in being operational. It is common that these systems alter the soil uses, vegetation distribution and hydrological natural regime.&#160;This is the case of the extensive network of irrigation ditches in the Sierra Nevada Mountain Range in southeastern Spain (an UNESCO&#160; Reserve of the Biosphere, with areas as Natural and National Park), which originated in Muslim times, and is still operational in some areas. These ditches have contributed to maintaining local agricultural systems and populations in basins dominated by snow conditions, and they constitute a traditional regulation of water resources in the area. The network is made up of two types of irrigation ditches: &#8220;careo&#8221; and irrigation ditches. The first, the "careo", collects the meltwater and infiltrates it along its course, maintaining a high level of soil moisture and favouring deep percolation volumes that can be later consumed by the population through springs and natural fountains. The second, the irrigation ones, are used to transport water from the natural sources to the agricultural plots downstream the mountain area. In 2014, several irrigation ditches were restored in the Natural Park. This is a chance to further explore and quantify the role of this network in the hydrological budget on a local basis.&#160;&#160;The aim of this work is to evaluate to what extent the existence of these intermittent water networks affects the evolution of the surrounding vegetation. For this, one of the restored systems,&#160; the Barjas Ditch in the village of Ca&#241;ar, with a successful water circulation along its way, was selected from the increase of the soil water content in the ditch influence area and, indirectly a differential development of vegetation. Two analyses are performed using remote sensing information. The Normalized Difference Vegetation Index, NDVI, which is a spectral index used to estimate the quantity, quality and development of vegetation that can therefore be used indirectly as an indicator of the state of soil moisture, was used as the indicator of evolution. For this purpose, a historical set of LandSat satellite images&#160; (TM, ETM+ and OLI) has been used. On the one hand, a global analysis on the whole mountainous range was carried out, comparing NDVI patterns in areas affected and non-affected by the ditches. On the other hand, the restored&#160; Barjas ditch is used to assess vegetation changes before and after the restoration.

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Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

SN Applied Sciences ◽

10.1007/s42452-019-1488-y ◽

2019 ◽

Vol 1 (11) ◽

Cited By ~ 1

Author(s):

Ichirow Kaihotsu ◽

Jun Asanuma ◽

Kentaro Aida ◽

Dambaravjaa Oyunbaatar

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Surface Soil ◽

Surface Soil Moisture ◽

Mongolian Plateau ◽

Moisture Measurement ◽

Rainfall Condition ◽

Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

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Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Theoretical and Applied Genetics ◽

10.1007/s00122-020-03637-6 ◽

2020 ◽

Vol 133 (10) ◽

pp. 2853-2868

Author(s):

Mahlet T. Anche ◽

Nicholas S. Kaczmar ◽

Nicolas Morales ◽

James W. Clohessy ◽

Daniel C. Ilut ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Crop Improvement ◽

Genetic Correlations ◽

Random Regression ◽

Multiple Time ◽

Heritable Variation ◽

Using Data

Abstract Key message Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Abstract Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

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Two Decades of Smart Irrigation Controllers in U.S. Landscape Irrigation

Transactions of the ASABE ◽

10.13031/trans.13930 ◽

2020 ◽

Vol 63 (5) ◽

pp. 1593-1601

Author(s):

Michael D. Dukes

Keyword(s):

Soil Moisture ◽

Water Conservation ◽

Human Interaction ◽

List Type ◽

Single Family ◽

Moisture Sensor ◽

Soil Moisture Sensor ◽

Water Sensor ◽

The Difference ◽

The U.S

HighlightsSavings numbers in new studies across multiple soil types and climates are similar to those summarized in 2011 and are summarized here as 51% in research plot studies and 30% in single-family homes.Studies of the human factors have begun showing how important the users are to success of the technology.Education in implementation remains important to achieve potential water conservation.Abstract. Smart irrigation controllers, such as evapotranspiration (ET) and soil moisture sensor (SMS) controllers, have become commonly available from virtually all irrigation controller manufacturers. This review summarizes the literature since the Fifth Decennial National Irrigation Symposium (NIS) concerning these controllers in research studies and pilot implementations. Studies have expanded to multiple climates throughout the U.S. on a variety of soils and plant types. When these devices are implemented properly on sites that have potential irrigation savings (i.e., excess irrigation), they are able to reduce irrigation while maintaining plant quality. The level of reduction depends on many factors, including the amount of excess irrigation, climate, plant type, and human interaction with the technology. When studies report positive savings, the levels documented here range from 40% to 61% (51% avg.) in plot studies and from 28% to 32% (30% avg.) in residential studies. Of 17 identified studies in the past decade, five reported negative savings, and in most cases those results were due to ET controllers installed on sites with little excess irrigation or controller programming that was not optimized for savings. New trends in the industry include Wi-Fi signal-based ET controllers with smartphone app capability, an upcoming standard for SMS controllers, as well as smart controllers becoming mandatory in areas of the U.S. As identified in the Fifth Decennial NIS, it remains important to implement controllers on sites with the potential for irrigation reduction as well as proper implementation with the best current information. Finally, there is a need to understand human interaction with these devices because improper programming can make the difference between a water-saving device and ineffective technology with a dissatisfied customer. Keywords: ET controller, Landscape irrigation, Smart controller, SMS, Soil moisture sensor, Soil water sensor.

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