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Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 8904

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Prof. Dr. Koki Homma

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Graduate School of Agricultural Science, Tohoku University 468-1 Aramaki Aza-Aoba, Aoba, Sendai 980-8572, Japan
Interests: farmers’ field productivity; crop production constraints; crop production system; simulation model; remote sensing
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Dr. Masayasu Maki

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Faculty of Food and Agricultural Sciences, Fukushima University 1 Kanayagawa, Fukushima 960-1296, Japan
Interests: remote sensing for precision agriculture; integration of drone and satellite data for agriculture; assimilation of remote sensing data and crop growth simulation model

Dr. Mongkol Raksapatcharawong

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Faculty of Engineering, Kasetsart University, 50 Ngamwongwan Rd, Jatujak Bangkok 10900, Thailand
Interests: crop/drought model development; remote sensing applications; big data analytic for smart agriculture

Special Issue Information

Dear Colleagues,

In recent years, the frequency and intensification of extreme weather have been increasing, which may be associated with global warming and climate change. Under such circumstances, the monitoring of meteorological disasters in agriculture and their impact assessment are very important issues for food security. Quantifying the disasters based on satellite observations is recommended for this purpose. It is necessary to monitor and assess disasters at the farmer level. As UAV technology is becoming more popular and easily accessible, monitoring can start at any level. Carrying out assessments immediately after a disaster can provide information that will help shape the countermeasures in such cases. Monitoring and assessment are also being tested in the field of agricultural insurance. Insurance assessment based on remote-sensing may increase fairness and decrease cost.

This Special Issue calls for papers on the monitoring of meteorological disasters in agriculture. It covers not only floods and droughts, but also production fluctuations due to high temperature, low solar radiation, and so on, from the country scale to the farmers’ field scale. The trials of impact assessment using simulation models are especially welcome to utilize remote-sensing monitoring. Further development in this topic is expected by introducing the latest findings in this Special Issue.

Prof. Koki Homma
Dr. Masayasu Maki
Dr. Mongkol Raksapatcharawong
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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12 pages, 9865 KiB

Open AccessCommunication

Evaluation of Geographical and Annual Changes in Rice Planting Patterns Using Satellite Images in the Flood-Prone Area of the Pampanga River Basin, the Philippines

by Kohei Hosonuma, Kentaro Aida, Vicente Ballaran, Jr., Naoko Nagumo, Patricia Ann J. Sanchez, Tsuyoshi Sumita and Koki Homma

Remote Sens. 2024, 16(3), 499; https://doi.org/10.3390/rs16030499 - 28 Jan 2024

Viewed by 916

Abstract

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns vary according to the flood risk. However, quantitative analyses of the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzed MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consisted of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba was divided into four areas, which were found to be partly consistent with the observed flood risk. Subsequently, annual LAI dynamics for each area were divided into two or three clusters. Obvious differences among clusters were caused by flooding in the late rainy season, which delayed the start of planting in the dry season. Clusters also indicated that cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provided an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences. Full article

(This article belongs to the Special Issue Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters )

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21 pages, 4475 KiB

Open AccessArticle

Prediction of Areal Soybean Lodging Using a Main Stem Elongation Model and a Soil-Adjusted Vegetation Index That Accounts for the Ratio of Vegetation Cover

by Tomohiro Konno and Koki Homma

Remote Sens. 2023, 15(13), 3446; https://doi.org/10.3390/rs15133446 - 07 Jul 2023

Cited by 1 | Viewed by 1051

Abstract

In soybean, lodging is sometimes caused by strong winds and rains, resulting in a decrease in yield and quality. Technical measures against lodging include “pinching”, in which the main stem is pruned when excessive growth is expected. However, there can be a decrease in yield when pinching is undertaken when the risk of lodging is relatively low. Therefore, it is important that pinching is performed after the future risk of lodging has been determined. The lodging angle at the full maturity stage (R8) can be explained using a multiple regression model with main stem elongation from the sixth leaf stage (V6) to the blooming stage (R1) and main stem length at the full seed stage (R6) as the explanatory variables. The objective of this study was to develop an areal lodging prediction method by combining a main stem elongation model with areal main stem length estimation using UAV remote sensing. The main stem elongation model from emergence to R1 was a logistic regression formula with the temperature and daylight hours functions f (T_i, D_i) as the explanatory variables. The main stem elongation model from R1 to the peak main stem length was a linear regression formula with the main stem length of R1 as the explanatory variable. The model that synthesized these two regression formulas were used as the main stem elongation model from emergence to R8. The accuracy of the main stem elongation model was tested on the test data, and the average RMSE was 5.3. For the areal main stem length estimation by UAV remote sensing, we proposed a soil-adjusted vegetation index (SAVI_vc) that takes vegetation cover into account. SAVI_vc was more accurate in estimating the main stem length than the previously reported vegetation index (R² = 0.78, p < 0.001). The main stem length estimated by the main stem elongation model combined with SAVI_vc was substituted into a multiple regression model of lodging angle to test the accuracy of the areal lodging prediction method. The method was able to predict lodging angles with an accuracy of RMSE = 8.8. These results suggest that the risk of lodging can be estimated in an areal manner prior to pinching, even though the actual occurrence is affected by wind. Full article

(This article belongs to the Special Issue Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters )

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21 pages, 10082 KiB

Open AccessArticle

Construction and Assessment of a Drought-Monitoring Index Based on Multi-Source Data Using a Bias-Corrected Random Forest (BCRF) Model

by Yihao Wang, Linghua Meng, Huanjun Liu, Chong Luo, Yilin Bao, Beisong Qi and Xinle Zhang

Remote Sens. 2023, 15(9), 2477; https://doi.org/10.3390/rs15092477 - 08 May 2023

Cited by 1 | Viewed by 1614

Abstract

Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50–500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought. Full article

(This article belongs to the Special Issue Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters )

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20 pages, 21626 KiB

Open AccessEditor’s ChoiceArticle

Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

by Mongkol Raksapatcharawong, Watcharee Veerakachen, Koki Homma, Masayasu Maki and Kazuo Oki

Remote Sens. 2020, 12(13), 2099; https://doi.org/10.3390/rs12132099 - 30 Jun 2020

Cited by 12 | Viewed by 4422

Abstract

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change. Full article

(This article belongs to the Special Issue Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters )

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