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Remote Sensing for Crop Stress Monitoring and Yield Prediction

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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 July 2022) | Viewed by 30163

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Special Issue Editors

Dr. Jianxiu Qiu

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Guest Editor

School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: drought detection; microwave-based soil moisture retrieval; land data assimilation

Dr. Xiaohu Zhang

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Guest Editor

National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop monitoring; machine learning; remote sensing; detection and mapping; spatial analysis

Dr. Zhenzhong Zeng

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Guest Editor

School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: water and agriculture resources; biosphere-atmosphere interactions; global change and the Earth system

Dr. Gabriel de Oliveira

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Guest Editor

Department of Earth Sciences, University of South Alabama, 5871 USA Drive North, LSCB Room 342, Mobile, AL 36688, USA
Interests: vegetation dynamics; biosphere–atmosphere interactions; water and carbon cycling; remote sensing/GIS; land use and land cover changes (LULCC); Amazonia
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Special Issue Information

Dear Colleagues,

Understanding and predicting crop stress and yield with changing climate is critical for designing effective adaptation and mitigation strategies. Recently, numerous studies have been conducted for multi-scale crop stress monitoring and climate impact assessment, using various data sources and novel algorithms. This Special Issue is designed to synthesize recent advances in utilizing remote sensing for cropland and irrigation mapping, crop growth assessment, crop water and heat stress monitoring, as well as yield prediction. Studies combing remote sensing and process-based/statistical models for better yield prediction under extreme weather (e.g., droughts, floods, heatwaves, heavy winds) and quantifying the associated uncertainties through inter-method and inter-model comparisons are especially welcomed.

Dr. Jianxiu Qiu
Dr. Xiaohu Zhang
Dr. Zhenzhong Zeng
Dr. Gabriel de Oliveira
Guest Editors

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Keywords

crop monitoring
crop yield prediction
multi-scale modeling
multi-source data fusion
climate impact assessment

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Published Papers (7 papers)

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Research

16 pages, 3693 KiB

Open AccessArticle

Remote Sensing Imaging as a Tool to Support Mulberry Cultivation for Silk Production

by Domenico Giora, Alberto Assirelli, Silvia Cappellozza, Luigi Sartori, Alessio Saviane, Francesco Marinello and José A. Martínez-Casasnovas

Remote Sens. 2022, 14(21), 5450; https://doi.org/10.3390/rs14215450 - 29 Oct 2022

Cited by 1 | Viewed by 2304

Abstract

In recent decades there has been an increasing use of remotely sensed data for precision agricultural purposes. Sericulture, the activity of rearing silkworm (Bombyx mori L.) larvae to produce silk in the form of cocoons, is an agricultural practice that has rarely used remote sensing techniques but that could benefit from them. The aim of this work was to investigate the possibility of using satellite imaging in order to monitor leaf harvesting in mulberry (Morus alba L.) plants cultivated for feeding silkworms; additionally, quantitative parameters on silk cocoon production were related to the analyses on vegetation indices. Adopting PlanetScope satellite images, four M. alba fields were monitored from the beginning of the silkworm rearing season until its end in 2020 and 2021. The results of our work showed that a decrease in the multispectral vegetation indices in the mulberry plots due to leaf harvesting was correlated with the different parameters of silk cocoons spun by silkworm larvae; in particular, a decrease in the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) had high correlations with quantitative silk cocoon production parameters (R² values up to 0.56, p < 0.05). These results led us to the conclusion that precision agriculture can improve sericultural practice, offering interesting solutions for estimating the quantity of produced silk cocoons through the remote analysis of mulberry fields. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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19 pages, 3886 KiB

Open AccessArticle

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

by Xia Jing, Bingyu Li, Qixing Ye, Qin Zou, Jumei Yan and Kaiqi Du

Remote Sens. 2022, 14(14), 3427; https://doi.org/10.3390/rs14143427 - 16 Jul 2022

Cited by 5 | Viewed by 2110

Abstract

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIF_canopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIR_VP, which can characterize the canopy structure, and SIF_tot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIF_canopy, NIR_VP, and SIF_tot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIR_VP was more sensitive to variation in SIF_canopy than SIF_tot, and the correlation between SIF_tot and SL was 6.6% higher than that of SIF_canopy. Using the decomposed SIF_tot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIF_canopy was affected by both NIR_VP and SIF_tot, and the detection accuracy of SIF_canopy for wheat stripe rust was better than that of the NIR_VP and SIF_tot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIF_tot was more sensitive to the variation in SIF_canopy, and NIR_VP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIF_canopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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19 pages, 3571 KiB

Open AccessArticle

Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

by Nguyen Thi Thanh Thao, Dao Nguyen Khoi, Antoine Denis, Luong Van Viet, Joost Wellens and Bernard Tychon

Remote Sens. 2022, 14(13), 2975; https://doi.org/10.3390/rs14132975 - 22 Jun 2022

Cited by 12 | Viewed by 4456

Abstract

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R² = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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23 pages, 5134 KiB

Open AccessArticle

Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data

by Péter Bognár, Anikó Kern, Szilárd Pásztor, Péter Steinbach and János Lichtenberger

Remote Sens. 2022, 14(12), 2860; https://doi.org/10.3390/rs14122860 - 15 Jun 2022

Cited by 9 | Viewed by 3243

Abstract

Remote sensing-based crop yield estimation methods rely on vegetation indices, which depend on the availability of the number of observations during the year, influencing the value of the derived crop yield. In the present study, a robust yield estimation method was improved for estimating the yield of corn, winter wheat, sunflower, and rapeseed in Hungary for the period 2000–2020 using 16 vegetation indices. Then, meteorological data were used to reduce the differences between the estimated and census yield data. In the case of corn, the best result was obtained using the Green Atmospherically Resistant Vegetation Index, where the correlation between estimated and census data was R² = 0.888 before and R² = 0.968 after the meteorological correction. In the case of winter wheat, the Difference Vegetation Index produced the best result with R² = 0.815 and 0.894 before and after the meteorological correction. For sunflower, these correlation values were 0.730 and 0.880, and for rapeseed, 0.765 and 0.922, respectively. Using the meteorological correction, the average percentage differences between estimated and census data decreased from 7.7% to 3.9%, from 6.7% to 3.9%, from 7.2% to 4.2%, and from 7.8% to 5.1% in the case of corn, winter wheat, sunflower, and rapeseed, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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9 pages, 1837 KiB

Open AccessCommunication

Formulation of a Structural Equation Relating Remotely Sensed Electron Transport Rate Index to Photosynthesis Activity

by Oded Liran

Remote Sens. 2022, 14(10), 2439; https://doi.org/10.3390/rs14102439 - 19 May 2022

Cited by 1 | Viewed by 7077

Abstract

Chlorophyll fluorescence can be remotely sensed in open fields via the Fraunhofer atmospheric absorption lines of oxygen and is termed Solar-Induced Fluorescence (SIF). SIF has been extensively related to carbon assimilation at global ecology scale and was interpreted as electron transport rate. However, SIF was shown to be unrelated directly to carbon assimilation at finer-scale resolution and may be related to other photosynthetic processes, such as non-photochemical quenching. This raises the question how exactly the SIF relates to actual photosynthetic activity. Based on a recently introduced spectral index that relates the photochemical fraction of SIF to the actual electron transport rate, this study presents the formulation of a structural equation, relating the remotely sensed electron transport rate index to fluorescence yield which considers the various fates of energetic quanta and electron excitation. The proposed structural equations are used to examine and interpret the relation between the novel spectral index and seasonal growth of corn (Z. mays Sh2, ‘super sweet’) on a platform of fertilization concentration gradient. Potential uses, practical and theoretical, for the proposed structural equations are discussed. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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21 pages, 6865 KiB

Open AccessArticle

Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China

by Rongrong Zhang, Junyu Qi, Song Leng and Qianfeng Wang

Remote Sens. 2022, 14(6), 1396; https://doi.org/10.3390/rs14061396 - 14 Mar 2022

Cited by 57 | Viewed by 4959

Abstract

Due to the complex coupling between phenology and climatic factors, the influence mechanism of climate, especially preseason temperature and preseason precipitation, on vegetation phenology is still unclear. In the present study, we explored the long-term trends of phenological parameters of different vegetation types in China north of 30°N from 1982 to 2014 and their comprehensive responses to preseason temperature and precipitation. Simultaneously, annual double-season phenological stages were considered. Results show that the satellite-based phenological data were corresponding with the ground-based phenological data. Our analyses confirmed that the preseason temperature has a strong controlling effect on vegetation phenology. The start date of the growing season (SOS) had a significant advanced trend for 13.5% of the study area, and the end date of the growing season (EOS) showed a significant delayed trend for 23.1% of the study area. The impact of preseason precipitation on EOS was overall stronger than that on SOS, and different vegetation types had different responses. Compared with other vegetation types, SOS and EOS of crops were greatly affected by human activities while the preseason precipitation had less impact. This study will help us to make a scientific decision to tackle global climate change and regulate ecological engineering. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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23 pages, 7431 KiB

Open AccessArticle

A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data

by Wenjie Li, Jingfeng Huang, Lingbo Yang, Yan Chen, Yahua Fang, Hongwei Jin, Han Sun and Ran Huang

Remote Sens. 2021, 13(16), 3231; https://doi.org/10.3390/rs13163231 - 14 Aug 2021

Cited by 22 | Viewed by 3370

Abstract

Late frost damage is one of the main meteorological disasters that affect the growth of wine grapes in spring, causing a decline in wine grapes quality and a reduction in yield in Northwest China. At present, remote sensing technology has been widely used in the field of crop meteorological disasters monitoring and loss assessments, but little research has been carried out on late frost damage in wine grapes. To monitor the impact of late frost in wine grapes accurately and quickly, in this research, we selected the Ningxia planting area as the study area. A practical framework of late frost damage on wine grapes by integrating visible, near-infrared, and thermal infrared satellite data is proposed. This framework includes: (1) Wine grape planting area extraction using Gaofen-1 (GF-1), Landsat-8, and Sentinel-2 based on optimal feature selection and Random Forest (RF) algorithm; (2) retrieval of the land surface temperature (LST) using Landsat-8 thermal infrared data; (3) data fusion using Landsat-8 LST and MODIS LST for a high spatiotemporal resolution of LST with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM); (4) the estimation of daily minimum air temperature (T_min) using downscaled LST and meteorological station data; (5) monitoring and evaluation of the degree of late frost damage in wine grapes in April 2020 by combining satellite-derived data and late frost indicators. The results show that the total area of wine grapes extracted in Ningxia was about 39,837 ha. The overall accuracy was 90.47%, the producer’s accuracy was 91.09%, and the user’s accuracy was 90.22%. The root mean square (RMSE) and the coefficient of determination (R²) of the T_min estimation model were 1.67 ℃ and 0.91, respectively. About 41.12% of the vineyards suffered severe late frost damage, and the total affected area was about 16,381 ha during April 20–25, 2020. This suggests the satellite data can accurately monitor late frost damage in wine grapes by mapping the wine grape area and estimating T_min. The results can help farmers to take remedial measures to reduce late frost damage in wine grapes, and provide an objective evaluation of late frost damage insurance claims for wine grapes. With the increasing weather extremes, this study has an important reference value for standardized global wine grape management and food security planning. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Stress Monitoring and Yield Prediction)

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