Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image

Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.publishedVersionPeer reviewe

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θv) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θv. The results showed that the modeled θv was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θv estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θv image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θv) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θv. The results showed that the modeled θv was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θv estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θv image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics

TVDI obtido de diferentes sensores aplicado ao monitoramento agrícola de risco da cultura da soja

O principal desafio na atualidade do setor agrícola é produzir maior quantidade de alimentos para atender a demanda crescente da população do planeta, mas de forma mais sustentável ambientalmente. Parte deste desafio pode ser atendido a partir de incrementos na produtividade doscultivos. Neste contexto, o índice TVDI (Temperature-Vegetation Dryness Index), que usa dados de sensores remotos orbitais, índice de vegetação e Ts (Temperatura de superfície), tem sido utilizado por diversos especialistas.Este índice, portanto, quando aplicado em escala regional pode-se tornar uma importante ferramenta para o monitoramentodo déficit hídrico,principal fator de risco das áreas agrícolas no sul do Brasil. Este estudo buscou compreender a metodologia de determinação do índice de umidade da superfície, o TVDI, e analisar sua robustez em caracterizar a umidade da superfície, as vantagens e as limitações associadas à sua obtenção em diferentes períodos de tempo, assim como, em diferentes escalas espaciais, visando gerar informações que possibilitem seu uso em sistemas de monitoramento agrícola integrando de forma complementar dados de diferentes sensores. O TVDI foi obtido a partir de sensores espectrais de superfície,comparado a dados oriundos do balanço hídrico meteorológico diário, em experimento ‘On Farm’, também foi avaliada a sua coerência frente aos dados obtidos usando imagens orbitais. Foram utilizadas 4 imagens do sensor OLI/TIRS e 12 imagens MODIS com resoluções espacial, espectral e temporal distintas, para analisar a consistência na sua distribuição espacial na região. Foi, então, analisado como os padrões espaciais e temporais do TVDI, obtido com sensores terrestres e orbitais, podemser usadosde forma eficiente em programas de monitoramento agrícola. Os resultados mostraram que o ajuste do triangulo evaporativo para os distintos sensores apresentou coerência, sendo as principais diferenças entre eles associadas as características de resolução dos mesmos. Os dados do TVDI obtidos em escala local e a partir dos sensores orbitais apresentaram coerência e demonstraram a complementariedade de informações espaciais e temporais. O TDVI apresentou correlação significativa com diversas variáveis associadas a condição hídrica do sistema solo –água –planta (armazenamento, déficit, ETr, ETr/ET0 e umidade). O uso conjugado dos diferentes sensores possibilitou a construção de uma proposta para um sistema de monitoramento agrícola auxiliando na identificação dos períodos de deficiências hídricas em relação as fases da cultura e o detalhamento da distribuição espacial a nível de parcela agrícola.The main challenge in the agricultural sector today is to produce more food to meet the growing demand of the planet's population, but in a more environmentally sustainable way. Part of this challenge can be met from increases in crop productivity. In this context, the TVDI index (Temperature-Vegetation Dryness Index), which uses data from remote orbital sensors, vegetation index and Ts (surface temperature), has been used by several experts. This index, therefore, when applied on a regional scale can become an important tool for monitoring the main risk factor of agricultural areas in southern Brazil. This study soughtto understand the methodology for determining the surface moisture index, the TVDI, and to analyze its robustness in characterizing the surface moisture, the advantages and limitations associated with obtaining it in different periods of time, as well as,in different scales spatial, aiming to generate information that allows its use in agricultural monitoring systems by integrating data from different sensors in a complementary way. The TVDI was obtained from spectral surface sensors, compared to data from the daily meteorological water balance, in an ‘On Farm’ experiment, its coherence was also assessed against the data obtained using orbital images. Four images from the OLI / TIRS sensor and 12 MODIS images with different spatial, spectral and temporal resolutions were used to analyze the consistency in their spatial distribution in the region. It was then analyzed how the spatial and temporal patterns of TVDI, obtained with terrestrial and orbital sensors, can be used efficiently in agricultural monitoring programs. The results showed that the adjustment of the evaporative triangle for the different sensors showed coherence, the main differences between them being associated with their resolution characteristics. TVDI data obtained on a local scale and from orbital sensors showed coherence and demonstrated the complementarity of spatial and temporal information. TDVI showed a significant correlation with several variables associated with the water condition of the soil -water -plant system (storage, deficit, ETr, ETr / ET0 and humidity). The combined use of the different sensors enabled the construction of a proposal for an agricultural monitoring system, helping to identify periods of water deficiencies in relation to the crop phases and detailing the spatial distribution at the level of the agricultural plot