The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

Download Full-text

Applicability of crop models in the context of parametric insurance – a Caribbean case study

10.5194/egusphere-egu21-10120 ◽

2021 ◽

Author(s):

Beatrice Monteleone ◽

Luigi Cesarini ◽

Rui Figueiredo ◽

Mario Martina

Keyword(s):

Real Time ◽

Crop Yield ◽

Vegetation Index ◽

Crop Yields ◽

Growing Season ◽

Gridded Data ◽

Crop Models ◽

Weather Events ◽

Weather Stations ◽

Weather Parameters

Evaluating the impacts of weather events on the agricultural sector is of high importance. Weather has a huge influence on crop performance and agricultural system management, particularly in those countries where agriculture is mainly rainfed.&#160;Climate change is expected to further affect farmers&#8217; incomes since the risk of extreme weather events with a relevant impact on crop yields is predicted to increase.Appropriate strategies to deal with the economic impacts of agriculture need to be developed, to enable farmers to quickly recover after a disaster. In this context, weather-based index insurance (also known as parametric insurance) plays a key role since it allows farmers to receive financial aid soon after a disaster occurs.This study evaluates the applicability of crop models run with gridded data in the framework of index-based insurance to assess their added value in providing estimations of crop yield in case of drought events.At first, the cropland area is identified using satellite data on Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) retrieved from various sources, such as Sentinel and Landsat. Crop Type maps are then produced to identify the location of the different crops grown in a region. Then, weather data coming from stations are exploited to run the AquaCrop crop model and estimate the crop yield for the areas near the weather stations.Since in many countries weather stations are often missing or do not record continuously, the AquaCrop model is also run with gridded data coming from reanalysis, specifically ERA, which is a product released by the European Centre for Medium Range Weather Forecast and has the advantage to provide daily estimation of &#160;multiple weather parameters on a 0.25&#176; grid. In addition, ERA5 has a short latency time (in the order of days) and thus allows a near-real time monitoring of the crop growing season. The AquaCrop outputs obtained when the model is run with the station data are then compared to the ones obtained when the model is run with gridded data. The performance of the two model configurations (weather parameters coming from stations or from ERA5) in estimating yield reductions during drought events, previously identified using the Probabilistic Precipitation Vegetation Index (PPVI), are evaluated.The framework was applied in the context of the Dominican Republic, a Caribbean country in which 52% of the national territory is devoted to agriculture. The Dominican agricultural industry has as main products cocoa, tobacco, sugarcane, coffee, cotton, rice, beans, potatoes, corn and bananas. Results shows that gridded data can be a valuable tool to provide near-real time estimates of the crop growing season and thus help in forecasting final crop yields in near-real time.

Download Full-text

Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

Remote Sensing ◽

10.3390/rs13122249 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2249

Author(s):

Sadia Alam Shammi ◽

Qingmin Meng

Keyword(s):

Climate Change ◽

Crop Yield ◽

Crop Yields ◽

Negative Impact ◽

Positive Impact ◽

Information Criterion ◽

Growing Season ◽

Maximum Temperature ◽

Linear Regression Models ◽

The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

Download Full-text

Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

10.5194/gmd-2020-186 ◽

2020 ◽

Author(s):

Yaqiong Lu ◽

Xianyu Yang

Keyword(s):

Crop Yield ◽

Temporal Resolution ◽

Land Surface ◽

Crop Yields ◽

Surface Model ◽

High Temporal Resolution ◽

Climate Scenarios ◽

Climate Data ◽

Land Surface Models ◽

Surface Models

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

Download Full-text

Analysis of the Effects of High Precipitation in Texas on Rainfed Sorghum Yields

Water ◽

10.3390/w11091920 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1920 ◽

Cited By ~ 2

Author(s):

Sharma ◽

Kannan ◽

Cook ◽

Pokhrel ◽

McKenzie

Keyword(s):

Crop Yield ◽

Missing Values ◽

Crop Yields ◽

Principal Component ◽

Growing Season ◽

Grain Sorghum ◽

Extreme Weather Events ◽

Total Precipitation ◽

Independent Variables ◽

Two Parameters

Most of the recent studies on the consequences of extreme weather events on crop yields are focused on droughts and warming climate. The knowledge of the consequences of excess precipitation on the crop yield is lacking. We attempted to fill this gap by estimating reductions in rainfed grain sorghum yields for excess precipitation. The historical grain sorghum yield and corresponding historical precipitation data are collected by county. These data are sorted based on length of the record and missing values and arranged for the period 1973–2003. Grain sorghum growing periods in the different parts of Texas is estimated based on the east-west precipitation gradient, north-south temperature gradient, and typical planting and harvesting dates in Texas. We estimated the growing season total precipitation and maximum 4-day total precipitation for each county growing rainfed grain sorghum. These two parameters were used as independent variables, and crop yields of sorghum was used as the dependent variable. We tried to find the relationships between excess precipitation and decreases in crop yields using both graphical and mathematical relationships. The result were analyzed in four different levels; 1. Storm by storm consequences on the crop yield; 2. Growing season total precipitation and crop yield; 3. Maximum 4-day precipitation and crop yield; and 4. Multiple linear regression of independent variables with and without a principal component analysis (to remove the correlations between independent variables) and the dependent variable. The graphical and mathematical results show decreases in rainfed sorghum yields in Texas for excess precipitation could be between 18% and 38%.

Download Full-text

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

Remote Sensing ◽

10.3390/rs12040680 ◽

2020 ◽

Vol 12 (4) ◽

pp. 680 ◽

Cited By ~ 1

Author(s):

Meng Guo ◽

Jing Li ◽

Shubo Huang ◽

Lixiang Wen

Keyword(s):

Chlorophyll Fluorescence ◽

Boreal Forest ◽

Spatial Resolution ◽

Vegetation Index ◽

Mean Squared Error ◽

Gross Primary Production ◽

Coniferous Forest ◽

Terrestrial Ecosystems ◽

Linear Regression Models ◽

Modis Evi

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

Download Full-text

Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-27-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 27-30 ◽

Cited By ~ 2

Author(s):

H. H. Jaafar ◽

F. A. Ahmad

Keyword(s):

Primary Production ◽

Crop Yield ◽

Vegetation Index ◽

Crop Yields ◽

Gross Primary Production ◽

Remotely Sensed ◽

Early Assessment ◽

Food Shortages ◽

Semi Arid ◽

Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

Download Full-text

Estimating non-additive within-season temperature effects on maize yields using Bayesian approaches

Scientific Reports ◽

10.1038/s41598-019-55037-6 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Jisang Yu ◽

Gyuhyeong Goh

Keyword(s):

Temperature Effects ◽

Crop Yields ◽

Empirical Studies ◽

Growing Season ◽

Weather Variables ◽

Temperature And Precipitation ◽

Maize Yields ◽

Yield Responses ◽

The Impact ◽

The U.S

AbstractDetrimental impacts of extreme heats on the U.S. crop yields have been well-documented by a number of empirical studies. However, less have focused on within-growing season weather variation and the interaction between temperature and precipitation. The objective of this study is to emphasize the importance of disaggregating temperature exposures within growing season. To achieve our objective, we estimate the impact of within-season monthly temperature and precipitation variations on maize yields in the U.S. corn belt region. We provide a discussion on variable selection methods in the context of estimating crop yield responses to climate variables. We find that the models that utilize within-growing season monthly variations performs better compared to the models with growing season aggregated weather variables and show the strength of Bayesian estimations. We also find that the warming impacts predicted by the models that utilize within-growing season variations are smaller than the predicted impacts of the models with aggregated weather variables. The findings indicate that the temperature effects are not additive across months within growing season.

Download Full-text

Vegetation Change and Its Response to Climate Change between 2000 and 2016 in Marshes of the Songnen Plain, Northeast China

Sustainability ◽

10.3390/su12093569 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3569 ◽

Cited By ~ 3

Author(s):

Yanji Wang ◽

Xiangjin Shen ◽

Ming Jiang ◽

Xianguo Lu

Keyword(s):

Climate Change ◽

Vegetation Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Maximum Temperature ◽

Climate Data ◽

Songnen Plain ◽

Asymmetric Effects ◽

Climate Change Effects

Songnen Plain is a representative semi-arid marshland in China. The Songnen Plain marshes have undergone obvious loss during the past decades. In order to protect and restore wetland vegetation, it is urgent to investigate the vegetation change and its response to climate change in the Songnen Plain marshes. Based on the normalized difference vegetation index (NDVI) and climate data, we investigated the spatiotemporal change of vegetation and its relationship with temperature and precipitation in the Songnen Plain marshes. During 2000–2016, the growing season mean NDVI of the Songnen Plain marshes significantly (p < 0.01) increased at a rate of 0.06/decade. For the climate change effects on vegetation, the growing season precipitation had a significant positive effect on the growing season NDVI of marshes. In addition, this study first found asymmetric effects of daytime maximum temperature (Tmax) and nighttime minimum temperature (Tmin) on NDVI of the Songnen Plain marshes: The growing season NDVI correlated negatively with Tmax but positively with Tmin. Considering the global asymmetric warming of Tmax and Tmin, more attention should be paid to these asymmetric effects of Tmax and Tmin on the vegetation of marshes.

Download Full-text

Relationship of soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages

10.5194/egusphere-egu2020-2071 ◽

2020 ◽

Author(s):

Qiu Shen ◽

Jianjun Wu ◽

Leizhen Liu ◽

Wenhui Zhao

Keyword(s):

Soil Moisture ◽

Chlorophyll Fluorescence ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Maturity Stage ◽

Phenological Stages ◽

Active Radiation ◽

Relationship Of ◽

The Relationship

As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIFyield, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIFPAR_norm is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and &#160;rainfed&#160;croplands&#160; in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.

Download Full-text

Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0258.1 ◽

2017 ◽

Vol 56 (4) ◽

pp. 897-913 ◽

Cited By ~ 9

Author(s):

Ting Meng ◽

Richard Carew ◽

Wojciech J. Florkowski ◽

Anna M. Klepacka

Keyword(s):

Crop Yield ◽

Spring Wheat ◽

Climate Model ◽

Crop Yields ◽

Wheat Yield ◽

Growing Season ◽

Weather Data ◽

Growth Stages ◽

Yield Distribution ◽

The Impact

AbstractThe IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.

Download Full-text