Approaches for evaluation of relief morphometric characteristics influence on spatial distribution of moisture in the soils of steppe part of Crimea

Data on soil moisture reserves are the basis for decision-making in the agricultural boghara system, because it determines the development of agricultural crops potential, terms of top-dressing and additional fertilizing, and makes it possible to predict yield of agricultural crops. In this article the influence of relief morphometric characteristics on the distribution of precipitation over the territory was studied. The research area is the land of the eastern part of Klepininsky rural settlement of Krasnogvardeysky district, the central part of Crimean Peninsula. The article considers approaches, divided into 2 main categories (according to the type of data used), based on the use of GIS capabilities and remote sensing data, to analyze the soil water content (SWC) using the example of research area and relationship of this parameter to the terrain relief. It was established that the morphometric characteristics of relief affect the amount of soil moisture.

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Imaging Spectroscopy for Conservation Applications

Remote Sensing ◽

10.3390/rs13020292 ◽

2021 ◽

Vol 13 (2) ◽

pp. 292

Author(s):

Megan Seeley ◽

Gregory P. Asner

Keyword(s):

Remote Sensing ◽

Decision Making ◽

Case Studies ◽

Spatial Scales ◽

Imaging Spectroscopy ◽

Remote Sensing Data ◽

Decision Makers ◽

High Spectral Resolution ◽

Conservation Areas ◽

Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

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Soil moisture retrieval during the Southern Great Plains Hydrology Experiment 1999: A comparison between experimental remote sensing data and operational products

Water Resources Research ◽

10.1029/2003wr002441 ◽

2004 ◽

Vol 40 (2) ◽

Cited By ~ 32

Author(s):

Matthias Drusch ◽

Eric F. Wood ◽

H. Gao ◽

Ariane Thiele

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Great Plains ◽

Remote Sensing Data ◽

Southern Great Plains ◽

Sensing Data

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Efficient land use based on remote sensing data

Problems of AgriMarket ◽

10.46666/2021-3.2708-9991.20 ◽

2021 ◽

Vol 3 ◽

pp. 180-185

Author(s):

Y. M. Kenzhegaliyev ◽

◽

Keyword(s):

Remote Sensing ◽

Land Use ◽

Remote Sensing Data ◽

Crop Productivity ◽

The State ◽

Agricultural Crops ◽

Earth Remote Sensing ◽

Spectral Reflectivity ◽

Sensing Data ◽

Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

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Estimation of Soil Moisture from Remote Sensing Data and Cokriging

10.3997/2214-4609-pdb.157.sageep096 ◽

2009 ◽

Author(s):

Y. Liu ◽

M. Momayez ◽

D. Tong

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Remote Sensing Data ◽

Sensing Data

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Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽

10.3390/w12113223 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3223

Author(s):

Hamed Adab ◽

Renato Morbidelli ◽

Carla Saltalippi ◽

Mahmoud Moradian ◽

Gholam Abbas Fallah Ghalhari

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Surface Soil Moisture ◽

Machine Learning Methods ◽

Land Use Types ◽

Semi Arid Region ◽

Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽

10.3390/fire2040055 ◽

2019 ◽

Vol 2 (4) ◽

pp. 55 ◽

Cited By ~ 1

Author(s):

Alexander J. Schaefer ◽

Brian I. Magi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Cover ◽

Remote Sensing Data ◽

Sensing Data ◽

Fire Activity ◽

Steep Decline ◽

Human Use ◽

Moisture Conditions ◽

In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

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