Recent Progesses on Optical Remote Sensing Modelling Over Complex Land Surface

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

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GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery

10.5194/essd-2021-86 ◽

2021 ◽

Author(s):

Miao Zhang ◽

Bingfang Wu ◽

Hongwei Zeng ◽

Guojin He ◽

Chong Liu ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Agricultural Land ◽

Cropping Systems ◽

Agricultural System ◽

Joint Control ◽

Optical Remote Sensing ◽

Multiple Cropping ◽

Efficient Manner ◽

Cropping Intensity

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30-m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.04) and median (1.13) CI values of the existing six estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two other satellite based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28 million km2) of the world’s cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global coverage, fine-resolution CI product, GCI30 can facilitate ongoing efforts to achieve sustainable development goals (SDGs) by improving food production while minimizing environmental impacts. The data are available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al, 2020).

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Recent developments in estimating land surface biogeophysical variables from optical remote sensing

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133307084626 ◽

2007 ◽

Vol 31 (5) ◽

pp. 501-516 ◽

Cited By ~ 104

Author(s):

Shunlin Liang

Keyword(s):

Remote Sensing ◽

Land Surface ◽

State Of The Art ◽

Optical Remote Sensing ◽

Nonlinear Inversion ◽

Spatial And Temporal Scales ◽

Recent Developments ◽

Ill Posed ◽

Earth System Models ◽

New Algorithms

Earth system models and many other applications require biogeophysical variables, and remote sensing is the only means by which to estimate them at the appropriate spatial and temporal scales. Developing advanced inversion methods to solve ill-posed multidimensional nonlinear inversion problems is critical and very challenging. This article reviews state-of-the-art algorithms for estimating land surface biogeophysical variables in optical remote sensing (from the visible to the thermal infrared spectrum) to stimulate the development of new algorithms and to utilize existing ones.

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Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Frontiers in Remote Sensing ◽

10.3389/frsen.2021.716430 ◽

2022 ◽

Vol 2 ◽

Author(s):

J. Joiner ◽

Z. Fasnacht ◽

W. Qin ◽

Y. Yoshida ◽

A. P. Vasilkov ◽

...

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Land Surface ◽

Rayleigh Scattering ◽

Near Infrared ◽

Computational Cost ◽

Training Data ◽

Optical Remote Sensing ◽

Spectral Features ◽

Hyper Spectral

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

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Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia

Journal of Glaciology ◽

10.1017/jog.2021.47 ◽

2021 ◽

pp. 1-12

Author(s):

Iulian-Horia Holobâcă ◽

Levan G. Tielidze ◽

Kinga Ivan ◽

Mariam Elizbarashvili ◽

Mircea Alexe ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Optical Remote Sensing ◽

Glacier Surface ◽

The Caucasus ◽

Greater Caucasus ◽

Sensing Systems ◽

Automatic Mapping ◽

Remote Sensing Systems ◽

Normalized Difference Snow Index

Abstract Global warming is causing glaciers in the Caucasus Mountains and around the world to lose mass at an accelerated pace. As a result of this rapid retreat, significant parts of the glacierized surface area can be covered with debris deposits, often making them indistinguishable from the surrounding land surface by optical remote-sensing systems. Here, we present the DebCovG-carto toolbox to delineate debris-covered and debris-free glacier surfaces from non-glacierized regions. The algorithm uses synthetic aperture radar-derived coherence images and the normalized difference snow index applied to optical satellite data. Validating the remotely-sensed boundaries of Ushba and Chalaati glaciers using field GPS data demonstrates that the use of pairs of Sentinel-1 images (2019) from identical ascending and descending orbits can substantially improve debris-covered glacier surface detection. The DebCovG-carto toolbox leverages multiple orbits to automate the mapping of debris-covered glacier surfaces. This new automatic method offers the possibility of quickly correcting glacier mapping errors caused by the presence of debris and makes automatic mapping of glacierized surfaces considerably faster than the use of other subjective methods.

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GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

Earth System Science Data ◽

10.5194/essd-13-4799-2021 ◽

2021 ◽

Vol 13 (10) ◽

pp. 4799-4817

Author(s):

Miao Zhang ◽

Bingfang Wu ◽

Hongwei Zeng ◽

Guojin He ◽

Chong Liu ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Surface ◽

Agricultural Land ◽

Agricultural Land Use ◽

Agricultural System ◽

Joint Control ◽

Optical Remote Sensing ◽

Multiple Cropping ◽

Cropping Intensity

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

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Optical remote sensing of forest leaf area index and biomass

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133312471367 ◽

2012 ◽

Vol 37 (1) ◽

pp. 98-113 ◽

Cited By ~ 43

Author(s):

Conghe Song

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Structural Parameters ◽

Terrestrial Ecosystem ◽

Great Effort ◽

Optical Remote Sensing ◽

Area Index ◽

Physical Relationship

Forests are the most complex terrestrial ecosystem on Earth’s land surface, providing vital goods and services upon which the welfare of humanity depends. The quantification of leaves and biomass in forests is critical for understanding the ecological role of forests in the terrestrial ecosystem. Great effort has been dedicated to the mapping of leaf area and biomass using remotely sensed data. This review focuses on the use of optical remote sensing in mapping leaf area index (LAI) and aboveground biomass for forests. Significant progress has been made in mapping LAI in the past few decades. Mapping of LAI started with location-specific empirical approaches and evolved to semi-empirical and biophysical approaches, which can be applied globally. Although there are some biases in the current LAI products, it can be expected that better-quality LAI products will be delivered in the future. At present, mapping biomass remains predominantly empirical because there is no direct physical relationship between reflected energy in visible, near or mid infrared wavelengths and biomass. Mapping biomass relies on the explicit or implicit mapping of forest structural parameters that are related to biomass allometrically. Although optical images have been successfully used in mapping biomass in low biomass areas, it remains a challenge to map biomass in forested areas with high biomass density due to signal saturation.

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